diff --git a/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/analytics/coremldata.bin b/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/analytics/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..d08a0f9d03eb6e1c2aa269ad8a112077a636267c
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:257f0247edf788b1ee34cc766f13441dc37bbb76e263207d622eceebfde004c1
+size 243
diff --git a/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/coremldata.bin b/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..259d662f4c44673c0e354a0eb585e0eac12442c2
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b42fcc1728ae57657aef0d04be604304285d11b3bbf56c38c105fc498af05b2
+size 463
diff --git a/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/metadata.json b/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..8a7d42477d1c6023af957cd7205a339725a80455
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/metadata.json
@@ -0,0 +1,69 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "shortDescription" : "Parakeet CTC decoder head (encoder -> log_probs)",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "log_probs",
+        "type" : "MultiArray"
+      }
+    ],
+    "storagePrecision" : "Float16",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.transpose" : 1,
+      "Ios18.cast" : 2,
+      "Ios18.conv" : 1,
+      "Ios18.softmax" : 1,
+      "Ios18.log" : 1
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 1024 × 1...188",
+        "shapeRange" : "[[1, 1], [1024, 1024], [1, 188]]",
+        "formattedType" : "MultiArray (Float32 1 × 1024 × 1)",
+        "type" : "MultiArray",
+        "shape" : "[1, 1024, 1]",
+        "name" : "encoder",
+        "shortDescription" : ""
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-02-27",
+      "com.github.apple.coremltools.source" : "torch==2.10.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "parakeet_ctc_decoder",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/model.mil b/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..18ea80545bbbb60e17019e10a95e450ca62d7a17
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/model.mil
@@ -0,0 +1,24 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 1024, ?]> encoder) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"encoder", [1, 1024, 1]}}), ("RangeDims", {{"encoder", [[1, 1], [1024, 1024], [1, 188]]}})))] {
+            int32 var_4 = const()[name = string("op_4"), val = int32(-1)];
+            string var_18_pad_type_0 = const()[name = string("op_18_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_18_strides_0 = const()[name = string("op_18_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_18_pad_0 = const()[name = string("op_18_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_18_dilations_0 = const()[name = string("op_18_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_18_groups_0 = const()[name = string("op_18_groups_0"), val = int32(1)];
+            string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1025, 1024, 1]> module_decoder_layers_0_weight_to_fp16 = const()[name = string("module_decoder_layers_0_weight_to_fp16"), val = tensor<fp16, [1025, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [1025]> module_decoder_layers_0_bias_to_fp16 = const()[name = string("module_decoder_layers_0_bias_to_fp16"), val = tensor<fp16, [1025]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2099328)))];
+            tensor<fp16, [1, 1024, ?]> encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_1")];
+            tensor<fp16, [1, 1025, ?]> var_18_cast_fp16 = conv(bias = module_decoder_layers_0_bias_to_fp16, dilations = var_18_dilations_0, groups = var_18_groups_0, pad = var_18_pad_0, pad_type = var_18_pad_type_0, strides = var_18_strides_0, weight = module_decoder_layers_0_weight_to_fp16, x = encoder_to_fp16)[name = string("op_18_cast_fp16")];
+            tensor<int32, [3]> input_perm_0 = const()[name = string("input_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, ?, 1025]> input_cast_fp16 = transpose(perm = input_perm_0, x = var_18_cast_fp16)[name = string("transpose_0")];
+            tensor<fp16, [1, ?, 1025]> out_objects_softmax_cast_fp16 = softmax(axis = var_4, x = input_cast_fp16)[name = string("out_objects_softmax_cast_fp16")];
+            fp32 out_objects_epsilon_0 = const()[name = string("out_objects_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [1, ?, 1025]> out_objects_cast_fp16 = log(epsilon = out_objects_epsilon_0, x = out_objects_softmax_cast_fp16)[name = string("out_objects_cast_fp16")];
+            string out_objects_cast_fp16_to_fp32_dtype_0 = const()[name = string("out_objects_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [1, ?, 1025]> log_probs = cast(dtype = out_objects_cast_fp16_to_fp32_dtype_0, x = out_objects_cast_fp16)[name = string("cast_0")];
+        } -> (log_probs);
+}
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/weights/weight.bin b/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..5a2dc1ef1b33369d33e395cb0fe298baf5a42b7c
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml/parakeet_ctc_decoder.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4676cb50ed9593fadebd8e5917d728dc437e2858a92695f8225237461dc4e3ea
+size 2101442
diff --git a/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin b/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..c0de4cb82504de130d241255539fa6aa3520f1f8
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b19f1f034a958dc31d6c6e2049ede86e5d345958dc2a4de9c094f4a592279f9d
+size 243
diff --git a/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin b/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..dba12707e691921af402691e44de8b88aba6b593
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9fb7247da4f4d4a8168ea123505c35c2098dd2e3830e4a618c42260b6bcbece4
+size 496
diff --git a/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/metadata.json b/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..225500c0a8c9776a002c0cbb98743581fa014640
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/metadata.json
@@ -0,0 +1,117 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "shortDescription" : "Parakeet CTC Mel+Encoder (15.0s window)",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 1024 × 188)",
+        "shortDescription" : "",
+        "shape" : "[1, 1024, 188]",
+        "name" : "encoder",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "encoder_length",
+        "type" : "MultiArray"
+      }
+    ],
+    "storagePrecision" : "Mixed (Float16, Int32)",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Stack" : 1,
+      "Ios18.conv" : 79,
+      "Ios18.mul" : 107,
+      "Ios18.cast" : 12,
+      "Identity" : 1,
+      "Ios18.layerNorm" : 120,
+      "Ios18.log" : 1,
+      "Ios18.equal" : 1,
+      "Ios18.floorDiv" : 4,
+      "Ios16.reduceSum" : 4,
+      "Ios18.logicalNot" : 3,
+      "Ios18.reshape" : 147,
+      "Pad" : 49,
+      "Ios18.concat" : 1,
+      "Ios18.add" : 180,
+      "Ios18.realDiv" : 3,
+      "Ios18.greaterEqual" : 1,
+      "Ios18.notEqual" : 1,
+      "Ios18.relu" : 3,
+      "Ios18.sub" : 7,
+      "Ios18.matmul" : 73,
+      "Ios18.silu" : 72,
+      "Ios18.expandDims" : 25,
+      "Ios18.linear" : 193,
+      "Ios18.sigmoid" : 24,
+      "Ios18.sliceByIndex" : 51,
+      "Ios18.transpose" : 172,
+      "Split" : 24,
+      "Ios18.sqrt" : 1,
+      "Ios18.softmax" : 24,
+      "Ios18.pow" : 2,
+      "Select" : 78,
+      "Ios18.logicalAnd" : 2,
+      "Ios18.less" : 6,
+      "Tile" : 9
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 240000)",
+        "shortDescription" : "",
+        "shape" : "[1, 240000]",
+        "name" : "audio_signal",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "audio_length",
+        "type" : "MultiArray"
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-02-27",
+      "com.github.apple.coremltools.source" : "torch==2.10.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "parakeet_ctc_mel_encoder",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/model.mil b/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..4a6d0ac4e6751bbe09c03b626d454072ab1107c0
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/model.mil
@@ -0,0 +1,3839 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios18>(tensor<int32, [1]> audio_length, tensor<fp32, [1, 240000]> audio_signal) {
+            int32 var_20 = const()[name = string("op_20"), val = int32(0)];
+            int32 var_21 = const()[name = string("op_21"), val = int32(160)];
+            int32 var_22 = const()[name = string("op_22"), val = int32(1)];
+            int32 var_32 = const()[name = string("op_32"), val = int32(512)];
+            tensor<int32, [1]> var_33 = add(x = audio_length, y = var_32)[name = string("op_33")];
+            int32 var_34 = const()[name = string("op_34"), val = int32(512)];
+            tensor<int32, [1]> var_35 = sub(x = var_33, y = var_34)[name = string("op_35")];
+            tensor<int32, [1]> floor_div_0 = floor_div(x = var_35, y = var_21)[name = string("floor_div_0")];
+            tensor<bool, [1]> var_38 = equal(x = audio_length, y = var_20)[name = string("op_38")];
+            tensor<int32, [1]> var_39 = const()[name = string("op_39"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1]> seq_len = select(a = var_39, b = floor_div_0, cond = var_38)[name = string("seq_len")];
+            tensor<int32, [1, 240000]> var_43 = const()[name = string("op_43"), val = tensor<int32, [1, 240000]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<int32, [1]> var_44_axes_0 = const()[name = string("op_44_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1]> var_44 = expand_dims(axes = var_44_axes_0, x = audio_length)[name = string("op_44")];
+            tensor<bool, [1, 240000]> timemask = less(x = var_43, y = var_44)[name = string("timemask")];
+            tensor<int32, [2]> var_47_begin_0 = const()[name = string("op_47_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_47_end_0 = const()[name = string("op_47_end_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<bool, [2]> var_47_end_mask_0 = const()[name = string("op_47_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<bool, [2]> var_47_squeeze_mask_0 = const()[name = string("op_47_squeeze_mask_0"), val = tensor<bool, [2]>([false, true])];
+            string audio_signal_to_fp16_dtype_0 = const()[name = string("audio_signal_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 240000]> audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = string("cast_242")];
+            tensor<fp16, [1]> var_47_cast_fp16 = slice_by_index(begin = var_47_begin_0, end = var_47_end_0, end_mask = var_47_end_mask_0, squeeze_mask = var_47_squeeze_mask_0, x = audio_signal_to_fp16)[name = string("op_47_cast_fp16")];
+            tensor<int32, [1]> var_48_axes_0 = const()[name = string("op_48_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_48_cast_fp16 = expand_dims(axes = var_48_axes_0, x = var_47_cast_fp16)[name = string("op_48_cast_fp16")];
+            tensor<int32, [2]> var_50_begin_0 = const()[name = string("op_50_begin_0"), val = tensor<int32, [2]>([0, 1])];
+            tensor<int32, [2]> var_50_end_0 = const()[name = string("op_50_end_0"), val = tensor<int32, [2]>([1, 240000])];
+            tensor<bool, [2]> var_50_end_mask_0 = const()[name = string("op_50_end_mask_0"), val = tensor<bool, [2]>([true, true])];
+            tensor<fp16, [1, 239999]> var_50_cast_fp16 = slice_by_index(begin = var_50_begin_0, end = var_50_end_0, end_mask = var_50_end_mask_0, x = audio_signal_to_fp16)[name = string("op_50_cast_fp16")];
+            tensor<int32, [2]> var_52_begin_0 = const()[name = string("op_52_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_52_end_0 = const()[name = string("op_52_end_0"), val = tensor<int32, [2]>([1, 239999])];
+            tensor<bool, [2]> var_52_end_mask_0 = const()[name = string("op_52_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<fp16, [1, 239999]> var_52_cast_fp16 = slice_by_index(begin = var_52_begin_0, end = var_52_end_0, end_mask = var_52_end_mask_0, x = audio_signal_to_fp16)[name = string("op_52_cast_fp16")];
+            fp16 var_53_to_fp16 = const()[name = string("op_53_to_fp16"), val = fp16(0x1.f0cp-1)];
+            tensor<fp16, [1, 239999]> var_54_cast_fp16 = mul(x = var_52_cast_fp16, y = var_53_to_fp16)[name = string("op_54_cast_fp16")];
+            tensor<fp16, [1, 239999]> var_55_cast_fp16 = sub(x = var_50_cast_fp16, y = var_54_cast_fp16)[name = string("op_55_cast_fp16")];
+            bool x_3_interleave_0 = const()[name = string("x_3_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 240000]> x_3_cast_fp16 = concat(axis = var_22, interleave = x_3_interleave_0, values = (var_48_cast_fp16, var_55_cast_fp16))[name = string("x_3_cast_fp16")];
+            tensor<bool, [1, 240000]> var_58 = logical_not(x = timemask)[name = string("op_58")];
+            fp16 var_16_to_fp16 = const()[name = string("op_16_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 240000]> input_1_cast_fp16 = select(a = var_16_to_fp16, b = x_3_cast_fp16, cond = var_58)[name = string("input_1_cast_fp16")];
+            tensor<int32, [3]> var_63 = const()[name = string("op_63"), val = tensor<int32, [3]>([1, 1, 240000])];
+            tensor<fp16, [1, 1, 240000]> input_3_cast_fp16 = reshape(shape = var_63, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")];
+            tensor<int32, [6]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 256])];
+            string input_5_mode_0 = const()[name = string("input_5_mode_0"), val = string("constant")];
+            fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1, 240512]> input_5_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")];
+            tensor<int32, [2]> var_69 = const()[name = string("op_69"), val = tensor<int32, [2]>([1, 240512])];
+            tensor<fp16, [1, 240512]> input_7_cast_fp16 = reshape(shape = var_69, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")];
+            tensor<int32, [1]> expand_dims_10 = const()[name = string("expand_dims_10"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [1]> expand_dims_11_axes_0 = const()[name = string("expand_dims_11_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 240512]> expand_dims_11_cast_fp16 = expand_dims(axes = expand_dims_11_axes_0, x = input_7_cast_fp16)[name = string("expand_dims_11_cast_fp16")];
+            string conv_0_pad_type_0 = const()[name = string("conv_0_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv_0_pad_0 = const()[name = string("conv_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_0_dilations_0 = const()[name = string("conv_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 conv_0_groups_0 = const()[name = string("conv_0_groups_0"), val = int32(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_8_to_fp16 = const()[name = string("expand_dims_8_to_fp16"), val = tensor<fp16, [257, 1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(960128)))];
+            tensor<fp16, [1, 257, 1501]> 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_10, weight = expand_dims_8_to_fp16, x = expand_dims_11_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<int32, [2]> conv_1_pad_0 = const()[name = string("conv_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_1_dilations_0 = const()[name = string("conv_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 conv_1_groups_0 = const()[name = string("conv_1_groups_0"), val = int32(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_9_to_fp16 = const()[name = string("expand_dims_9_to_fp16"), val = tensor<fp16, [257, 1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1223360)))];
+            tensor<fp16, [1, 257, 1501]> 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_10, weight = expand_dims_9_to_fp16, x = expand_dims_11_cast_fp16)[name = string("conv_1_cast_fp16")];
+            int32 stack_0_axis_0 = const()[name = string("stack_0_axis_0"), val = int32(-1)];
+            tensor<fp16, [1, 257, 1501, 2]> 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_13_promoted_to_fp16 = const()[name = string("op_13_promoted_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1, 257, 1501, 2]> var_73_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_13_promoted_to_fp16)[name = string("op_73_cast_fp16")];
+            tensor<int32, [1]> var_75_axes_0 = const()[name = string("op_75_axes_0"), val = tensor<int32, [1]>([-1])];
+            bool var_75_keep_dims_0 = const()[name = string("op_75_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 257, 1501]> var_75_cast_fp16 = reduce_sum(axes = var_75_axes_0, keep_dims = var_75_keep_dims_0, x = var_73_cast_fp16)[name = string("op_75_cast_fp16")];
+            tensor<fp16, [1, 257, 1501]> x_11_cast_fp16 = identity(x = var_75_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<fp16, [1, 80, 257]> const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = tensor<fp16, [1, 80, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1486592)))];
+            tensor<fp16, [1, 80, 1501]> x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = const_9_to_fp16, y = x_11_cast_fp16)[name = string("x_13_cast_fp16")];
+            fp16 var_82_to_fp16 = const()[name = string("op_82_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [1, 80, 1501]> var_83_cast_fp16 = add(x = x_13_cast_fp16, y = var_82_to_fp16)[name = string("op_83_cast_fp16")];
+            fp32 x_15_epsilon_0 = const()[name = string("x_15_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [1, 80, 1501]> x_15_cast_fp16 = log(epsilon = x_15_epsilon_0, x = var_83_cast_fp16)[name = string("x_15_cast_fp16")];
+            tensor<int32, [1, 1501]> var_88 = const()[name = string("op_88"), val = tensor<int32, [1, 1501]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1527808)))];
+            tensor<int32, [1]> var_91_axes_0 = const()[name = string("op_91_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1]> var_91 = expand_dims(axes = var_91_axes_0, x = seq_len)[name = string("op_91")];
+            tensor<bool, [1, 1501]> valid_mask = less(x = var_88, y = var_91)[name = string("valid_mask")];
+            tensor<int32, [1]> var_93_axes_0 = const()[name = string("op_93_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_93 = expand_dims(axes = var_93_axes_0, x = valid_mask)[name = string("op_93")];
+            tensor<int32, [3]> var_93_after_broadcast_reps_0 = const()[name = string("op_93_after_broadcast_reps_0"), val = tensor<int32, [3]>([1, 80, 1])];
+            tensor<bool, [1, 80, 1501]> var_93_after_broadcast = tile(reps = var_93_after_broadcast_reps_0, x = var_93)[name = string("op_93_after_broadcast")];
+            tensor<fp16, [1, 80, 1501]> var_16_after_broadcast_to_fp16 = const()[name = string("op_16_after_broadcast_to_fp16"), val = tensor<fp16, [1, 80, 1501]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533888)))];
+            tensor<fp16, [1, 80, 1501]> var_94_cast_fp16 = select(a = x_15_cast_fp16, b = var_16_after_broadcast_to_fp16, cond = var_93_after_broadcast)[name = string("op_94_cast_fp16")];
+            tensor<int32, [1]> x_mean_numerator_axes_0 = const()[name = string("x_mean_numerator_axes_0"), val = tensor<int32, [1]>([2])];
+            bool x_mean_numerator_keep_dims_0 = const()[name = string("x_mean_numerator_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 80]> x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_94_cast_fp16)[name = string("x_mean_numerator_cast_fp16")];
+            tensor<int32, [1]> x_mean_denominator_axes_0 = const()[name = string("x_mean_denominator_axes_0"), val = tensor<int32, [1]>([1])];
+            bool x_mean_denominator_keep_dims_0 = const()[name = string("x_mean_denominator_keep_dims_0"), val = bool(false)];
+            string cast_2_to_fp16_dtype_0 = const()[name = string("cast_2_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 1501]> valid_mask_to_fp16 = cast(dtype = cast_2_to_fp16_dtype_0, x = valid_mask)[name = string("cast_241")];
+            tensor<fp16, [1]> x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = string("x_mean_denominator_cast_fp16")];
+            tensor<int32, [1]> var_99_axes_0 = const()[name = string("op_99_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_99_cast_fp16 = expand_dims(axes = var_99_axes_0, x = x_mean_denominator_cast_fp16)[name = string("op_99_cast_fp16")];
+            tensor<fp16, [1, 80]> x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_99_cast_fp16)[name = string("x_mean_cast_fp16")];
+            tensor<int32, [1]> var_102_axes_0 = const()[name = string("op_102_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 80, 1]> var_102_cast_fp16 = expand_dims(axes = var_102_axes_0, x = x_mean_cast_fp16)[name = string("op_102_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> var_103_cast_fp16 = sub(x = x_15_cast_fp16, y = var_102_cast_fp16)[name = string("op_103_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> var_104_cast_fp16 = select(a = var_103_cast_fp16, b = var_16_after_broadcast_to_fp16, cond = var_93_after_broadcast)[name = string("op_104_cast_fp16")];
+            fp16 var_13_promoted_1_to_fp16 = const()[name = string("op_13_promoted_1_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1, 80, 1501]> var_105_cast_fp16 = pow(x = var_104_cast_fp16, y = var_13_promoted_1_to_fp16)[name = string("op_105_cast_fp16")];
+            tensor<int32, [1]> var_107_axes_0 = const()[name = string("op_107_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_107_keep_dims_0 = const()[name = string("op_107_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 80]> var_107_cast_fp16 = reduce_sum(axes = var_107_axes_0, keep_dims = var_107_keep_dims_0, x = var_105_cast_fp16)[name = string("op_107_cast_fp16")];
+            fp16 var_109_to_fp16 = const()[name = string("op_109_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1]> var_110_cast_fp16 = sub(x = var_99_cast_fp16, y = var_109_to_fp16)[name = string("op_110_cast_fp16")];
+            tensor<fp16, [1, 80]> var_111_cast_fp16 = real_div(x = var_107_cast_fp16, y = var_110_cast_fp16)[name = string("op_111_cast_fp16")];
+            tensor<fp16, [1, 80]> x_std_1_cast_fp16 = sqrt(x = var_111_cast_fp16)[name = string("x_std_1_cast_fp16")];
+            tensor<bool, [1, 80]> var_113_cast_fp16 = not_equal(x = x_std_1_cast_fp16, y = x_std_1_cast_fp16)[name = string("op_113_cast_fp16")];
+            tensor<fp16, [1, 80]> x_std_3_cast_fp16 = select(a = var_16_to_fp16, b = x_std_1_cast_fp16, cond = var_113_cast_fp16)[name = string("x_std_3_cast_fp16")];
+            fp16 var_7_to_fp16 = const()[name = string("op_7_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 80]> x_std_cast_fp16 = add(x = x_std_3_cast_fp16, y = var_7_to_fp16)[name = string("x_std_cast_fp16")];
+            tensor<int32, [1]> var_118_axes_0 = const()[name = string("op_118_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 80, 1]> var_118_cast_fp16 = expand_dims(axes = var_118_axes_0, x = x_std_cast_fp16)[name = string("op_118_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> x_17_cast_fp16 = real_div(x = var_103_cast_fp16, y = var_118_cast_fp16)[name = string("x_17_cast_fp16")];
+            tensor<bool, [1, 1501]> mask_3 = greater_equal(x = var_88, y = var_91)[name = string("mask_3")];
+            tensor<int32, [1]> var_127_axes_0 = const()[name = string("op_127_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_127 = expand_dims(axes = var_127_axes_0, x = mask_3)[name = string("op_127")];
+            tensor<fp16, [1, 80, 1501]> processed_signal_cast_fp16 = select(a = var_16_to_fp16, b = x_17_cast_fp16, cond = var_127)[name = string("processed_signal_cast_fp16")];
+            int32 var_152 = const()[name = string("op_152"), val = int32(-1)];
+            tensor<int32, [3]> x_19_perm_0 = const()[name = string("x_19_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> tensor_1_axes_0 = const()[name = string("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1501, 80]> x_19_cast_fp16 = transpose(perm = x_19_perm_0, x = processed_signal_cast_fp16)[name = string("transpose_291")];
+            tensor<fp16, [1, 1, 1501, 80]> tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_19_cast_fp16)[name = string("tensor_1_cast_fp16")];
+            tensor<int32, [1]> var_242_axes_0 = const()[name = string("op_242_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 1501, 1]> var_242 = expand_dims(axes = var_242_axes_0, x = valid_mask)[name = string("op_242")];
+            tensor<int32, [3]> var_244_reps_0 = const()[name = string("op_244_reps_0"), val = tensor<int32, [3]>([1, 1, 80])];
+            tensor<bool, [1, 1501, 80]> var_244 = tile(reps = var_244_reps_0, x = var_242)[name = string("op_244")];
+            tensor<int32, [1]> var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_5_to_fp16_dtype_0 = const()[name = string("mask_5_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 1501, 80]> var_244_to_fp16 = cast(dtype = mask_5_to_fp16_dtype_0, x = var_244)[name = string("cast_240")];
+            tensor<fp16, [1, 1, 1501, 80]> var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = var_244_to_fp16)[name = string("op_250_cast_fp16")];
+            tensor<fp16, [1, 1, 1501, 80]> input_9_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_250_cast_fp16)[name = string("input_9_cast_fp16")];
+            string tensor_3_pad_type_0 = const()[name = string("tensor_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_3_pad_0 = const()[name = string("tensor_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_3_strides_0 = const()[name = string("tensor_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> tensor_3_dilations_0 = const()[name = string("tensor_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_3_groups_0 = const()[name = string("tensor_3_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_0_weight_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1774144)))];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_0_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1778816)))];
+            tensor<fp16, [1, 256, 751, 40]> tensor_3_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_0_weight_to_fp16, x = input_9_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_261_promoted_to_fp16 = const()[name = string("op_261_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> seq_len_to_fp16 = cast(dtype = current_lengths_1_to_fp16_dtype_0, x = seq_len)[name = string("cast_239")];
+            tensor<fp16, [1]> var_262_cast_fp16 = add(x = seq_len_to_fp16, y = var_261_promoted_to_fp16)[name = string("op_262_cast_fp16")];
+            fp16 var_263_promoted_to_fp16 = const()[name = string("op_263_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_264_cast_fp16 = add(x = var_262_cast_fp16, y = var_263_promoted_to_fp16)[name = string("op_264_cast_fp16")];
+            fp16 var_265_promoted_to_fp16 = const()[name = string("op_265_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_266_cast_fp16 = sub(x = var_264_cast_fp16, y = var_265_promoted_to_fp16)[name = string("op_266_cast_fp16")];
+            fp16 var_154_promoted_to_fp16 = const()[name = string("op_154_promoted_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_1_cast_fp16 = floor_div(x = var_266_cast_fp16, y = var_154_promoted_to_fp16)[name = string("floor_div_1_cast_fp16")];
+            fp16 var_268_promoted_to_fp16 = const()[name = string("op_268_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_3_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_268_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<int32, [1, 751]> expand_dims_4 = const()[name = string("expand_dims_4"), val = tensor<int32, [1, 751]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1779392)))];
+            tensor<int32, [1]> var_277_axes_0 = const()[name = string("op_277_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_3_cast_fp16_to_int32 = cast(dtype = lengths_19_dtype_0, x = current_lengths_3_cast_fp16)[name = string("cast_238")];
+            tensor<int32, [1, 1]> var_277 = expand_dims(axes = var_277_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = string("op_277")];
+            tensor<bool, [1, 751]> time_mask_3 = less(x = expand_dims_4, y = var_277)[name = string("time_mask_3")];
+            tensor<int32, [1]> var_279_axes_0 = const()[name = string("op_279_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 751, 1]> var_279 = expand_dims(axes = var_279_axes_0, x = time_mask_3)[name = string("op_279")];
+            tensor<int32, [3]> var_281_reps_0 = const()[name = string("op_281_reps_0"), val = tensor<int32, [3]>([1, 1, 40])];
+            tensor<bool, [1, 751, 40]> var_281 = tile(reps = var_281_reps_0, x = var_279)[name = string("op_281")];
+            tensor<int32, [1]> var_287_axes_0 = const()[name = string("op_287_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_7_to_fp16_dtype_0 = const()[name = string("mask_7_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 751, 40]> var_281_to_fp16 = cast(dtype = mask_7_to_fp16_dtype_0, x = var_281)[name = string("cast_237")];
+            tensor<fp16, [1, 1, 751, 40]> var_287_cast_fp16 = expand_dims(axes = var_287_axes_0, x = var_281_to_fp16)[name = string("op_287_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_3_reps_0 = const()[name = string("expanded_mask_3_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 751, 40]> expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_287_cast_fp16)[name = string("expanded_mask_3_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> input_11_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_11_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> tensor_5_cast_fp16 = relu(x = input_11_cast_fp16)[name = string("tensor_5_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> input_13_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_13_cast_fp16")];
+            string tensor_7_pad_type_0 = const()[name = string("tensor_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_7_pad_0 = const()[name = string("tensor_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_7_strides_0 = const()[name = string("tensor_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 tensor_7_groups_0 = const()[name = string("tensor_7_groups_0"), val = int32(256)];
+            tensor<int32, [2]> tensor_7_dilations_0 = const()[name = string("tensor_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_2_weight_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1782464)))];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_2_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1787136)))];
+            tensor<fp16, [1, 256, 376, 20]> tensor_7_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_2_weight_to_fp16, x = input_13_cast_fp16)[name = string("tensor_7_cast_fp16")];
+            fp16 var_307_promoted_to_fp16 = const()[name = string("op_307_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_308_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_307_promoted_to_fp16)[name = string("op_308_cast_fp16")];
+            fp16 var_309_promoted_to_fp16 = const()[name = string("op_309_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_310_cast_fp16 = add(x = var_308_cast_fp16, y = var_309_promoted_to_fp16)[name = string("op_310_cast_fp16")];
+            fp16 var_311_promoted_to_fp16 = const()[name = string("op_311_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_312_cast_fp16 = sub(x = var_310_cast_fp16, y = var_311_promoted_to_fp16)[name = string("op_312_cast_fp16")];
+            fp16 var_154_promoted_1_to_fp16 = const()[name = string("op_154_promoted_1_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_2_cast_fp16 = floor_div(x = var_312_cast_fp16, y = var_154_promoted_1_to_fp16)[name = string("floor_div_2_cast_fp16")];
+            fp16 var_314_promoted_to_fp16 = const()[name = string("op_314_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_5_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_314_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<int32, [1, 376]> expand_dims_5 = const()[name = string("expand_dims_5"), val = tensor<int32, [1, 376]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1787712)))];
+            tensor<int32, [1]> var_323_axes_0 = const()[name = string("op_323_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_5_cast_fp16_to_int32 = cast(dtype = lengths_21_dtype_0, x = current_lengths_5_cast_fp16)[name = string("cast_236")];
+            tensor<int32, [1, 1]> var_323 = expand_dims(axes = var_323_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = string("op_323")];
+            tensor<bool, [1, 376]> time_mask_5 = less(x = expand_dims_5, y = var_323)[name = string("time_mask_5")];
+            tensor<int32, [1]> var_325_axes_0 = const()[name = string("op_325_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 376, 1]> var_325 = expand_dims(axes = var_325_axes_0, x = time_mask_5)[name = string("op_325")];
+            tensor<int32, [3]> var_327_reps_0 = const()[name = string("op_327_reps_0"), val = tensor<int32, [3]>([1, 1, 20])];
+            tensor<bool, [1, 376, 20]> var_327 = tile(reps = var_327_reps_0, x = var_325)[name = string("op_327")];
+            tensor<int32, [1]> var_333_axes_0 = const()[name = string("op_333_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_9_to_fp16_dtype_0 = const()[name = string("mask_9_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 376, 20]> var_327_to_fp16 = cast(dtype = mask_9_to_fp16_dtype_0, x = var_327)[name = string("cast_235")];
+            tensor<fp16, [1, 1, 376, 20]> var_333_cast_fp16 = expand_dims(axes = var_333_axes_0, x = var_327_to_fp16)[name = string("op_333_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_7_reps_0 = const()[name = string("expanded_mask_7_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 376, 20]> expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_333_cast_fp16)[name = string("expanded_mask_7_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_15_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_15_cast_fp16")];
+            string tensor_9_pad_type_0 = const()[name = string("tensor_9_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> tensor_9_strides_0 = const()[name = string("tensor_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> tensor_9_pad_0 = const()[name = string("tensor_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> tensor_9_dilations_0 = const()[name = string("tensor_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_9_groups_0 = const()[name = string("tensor_9_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 1, 1]> encoder_module_pre_encode_conv_3_weight_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_3_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1789312)))];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_3_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_3_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1920448)))];
+            tensor<fp16, [1, 256, 376, 20]> tensor_9_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_3_weight_to_fp16, x = input_15_cast_fp16)[name = string("tensor_9_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_17_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_17_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> tensor_11_cast_fp16 = relu(x = input_17_cast_fp16)[name = string("tensor_11_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_19_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_19_cast_fp16")];
+            string tensor_13_pad_type_0 = const()[name = string("tensor_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_13_pad_0 = const()[name = string("tensor_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_13_strides_0 = const()[name = string("tensor_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 tensor_13_groups_0 = const()[name = string("tensor_13_groups_0"), val = int32(256)];
+            tensor<int32, [2]> tensor_13_dilations_0 = const()[name = string("tensor_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_5_weight_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_5_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1921024)))];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_5_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_5_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1925696)))];
+            tensor<fp16, [1, 256, 188, 10]> tensor_13_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_5_weight_to_fp16, x = input_19_cast_fp16)[name = string("tensor_13_cast_fp16")];
+            fp16 var_368_promoted_to_fp16 = const()[name = string("op_368_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_369_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_368_promoted_to_fp16)[name = string("op_369_cast_fp16")];
+            fp16 var_370_promoted_to_fp16 = const()[name = string("op_370_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_371_cast_fp16 = add(x = var_369_cast_fp16, y = var_370_promoted_to_fp16)[name = string("op_371_cast_fp16")];
+            fp16 var_372_promoted_to_fp16 = const()[name = string("op_372_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_373_cast_fp16 = sub(x = var_371_cast_fp16, y = var_372_promoted_to_fp16)[name = string("op_373_cast_fp16")];
+            fp16 var_154_promoted_2_to_fp16 = const()[name = string("op_154_promoted_2_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_3_cast_fp16 = floor_div(x = var_373_cast_fp16, y = var_154_promoted_2_to_fp16)[name = string("floor_div_3_cast_fp16")];
+            fp16 var_375_promoted_to_fp16 = const()[name = string("op_375_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_cast_fp16 = add(x = floor_div_3_cast_fp16, y = var_375_promoted_to_fp16)[name = string("current_lengths_cast_fp16")];
+            string lengths_dtype_0 = const()[name = string("lengths_dtype_0"), val = string("int32")];
+            tensor<int32, [1, 188]> expand_dims_6 = const()[name = string("expand_dims_6"), val = tensor<int32, [1, 188]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1926272)))];
+            tensor<int32, [1]> var_384_axes_0 = const()[name = string("op_384_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_cast_fp16_to_int32 = cast(dtype = lengths_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_234")];
+            tensor<int32, [1, 1]> var_384 = expand_dims(axes = var_384_axes_0, x = current_lengths_cast_fp16_to_int32)[name = string("op_384")];
+            tensor<bool, [1, 188]> time_mask = less(x = expand_dims_6, y = var_384)[name = string("time_mask")];
+            tensor<int32, [1]> var_386_axes_0 = const()[name = string("op_386_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 188, 1]> var_386 = expand_dims(axes = var_386_axes_0, x = time_mask)[name = string("op_386")];
+            tensor<int32, [3]> var_388_reps_0 = const()[name = string("op_388_reps_0"), val = tensor<int32, [3]>([1, 1, 10])];
+            tensor<bool, [1, 188, 10]> var_388 = tile(reps = var_388_reps_0, x = var_386)[name = string("op_388")];
+            tensor<int32, [1]> var_394_axes_0 = const()[name = string("op_394_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_11_to_fp16_dtype_0 = const()[name = string("mask_11_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 188, 10]> var_388_to_fp16 = cast(dtype = mask_11_to_fp16_dtype_0, x = var_388)[name = string("cast_233")];
+            tensor<fp16, [1, 1, 188, 10]> var_394_cast_fp16 = expand_dims(axes = var_394_axes_0, x = var_388_to_fp16)[name = string("op_394_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_13_reps_0 = const()[name = string("expanded_mask_13_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 188, 10]> expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_394_cast_fp16)[name = string("expanded_mask_13_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> input_21_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_21_cast_fp16")];
+            string tensor_15_pad_type_0 = const()[name = string("tensor_15_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> tensor_15_strides_0 = const()[name = string("tensor_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> tensor_15_pad_0 = const()[name = string("tensor_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> tensor_15_dilations_0 = const()[name = string("tensor_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_15_groups_0 = const()[name = string("tensor_15_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 1, 1]> encoder_module_pre_encode_conv_6_weight_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_6_weight_to_fp16"), val = tensor<fp16, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1927104)))];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_6_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_6_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2058240)))];
+            tensor<fp16, [1, 256, 188, 10]> tensor_15_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_6_weight_to_fp16, x = input_21_cast_fp16)[name = string("tensor_15_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> input_23_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_23_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> tensor_cast_fp16 = relu(x = input_23_cast_fp16)[name = string("tensor_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> x_21_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("x_21_cast_fp16")];
+            tensor<int32, [4]> var_428_perm_0 = const()[name = string("op_428_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_429 = const()[name = string("op_429"), val = tensor<int32, [3]>([1, 188, -1])];
+            tensor<fp16, [1, 188, 256, 10]> var_428_cast_fp16 = transpose(perm = var_428_perm_0, x = x_21_cast_fp16)[name = string("transpose_290")];
+            tensor<fp16, [1, 188, 2560]> input_25_cast_fp16 = reshape(shape = var_429, x = var_428_cast_fp16)[name = string("input_25_cast_fp16")];
+            tensor<fp16, [1024, 2560]> encoder_module_pre_encode_out_weight_to_fp16 = const()[name = string("encoder_module_pre_encode_out_weight_to_fp16"), val = tensor<fp16, [1024, 2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2058816)))];
+            tensor<fp16, [1024]> encoder_module_pre_encode_out_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7301760)))];
+            tensor<fp16, [1, 188, 1024]> linear_0_cast_fp16 = linear(bias = encoder_module_pre_encode_out_bias_to_fp16, weight = encoder_module_pre_encode_out_weight_to_fp16, x = input_25_cast_fp16)[name = string("linear_0_cast_fp16")];
+            string padding_length_dtype_0 = const()[name = string("padding_length_dtype_0"), val = string("int32")];
+            fp16 var_440_to_fp16 = const()[name = string("op_440_to_fp16"), val = fp16(0x1p+5)];
+            tensor<fp16, [1, 188, 1024]> x_23_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_440_to_fp16)[name = string("x_23_cast_fp16")];
+            tensor<int32, [1]> var_469_axes_0 = const()[name = string("op_469_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<int32, [1]> encoder_length = cast(dtype = padding_length_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_232")];
+            tensor<int32, [1, 1]> var_469 = expand_dims(axes = var_469_axes_0, x = encoder_length)[name = string("op_469")];
+            tensor<bool, [1, 188]> pad_mask_1 = less(x = expand_dims_6, y = var_469)[name = string("pad_mask_1")];
+            tensor<int32, [1]> var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188]> var_471 = expand_dims(axes = var_471_axes_0, x = pad_mask_1)[name = string("op_471")];
+            tensor<int32, [3]> var_472 = const()[name = string("op_472"), val = tensor<int32, [3]>([1, 188, 1])];
+            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask_1 = tile(reps = var_472, x = var_471)[name = string("pad_mask_for_att_mask_1")];
+            tensor<int32, [3]> var_474_perm_0 = const()[name = string("op_474_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<bool, [1, 188, 188]> var_474 = transpose(perm = var_474_perm_0, x = pad_mask_for_att_mask_1)[name = string("transpose_289")];
+            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_474)[name = string("pad_mask_for_att_mask")];
+            tensor<bool, [1, 188, 188]> const_81 = const()[name = string("const_81"), val = tensor<bool, [1, 188, 188]>([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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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, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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<bool, [1, 188, 188]> att_mask = logical_and(x = pad_mask_for_att_mask, y = const_81)[name = string("att_mask")];
+            tensor<bool, [1, 188, 188]> mask_13 = logical_not(x = att_mask)[name = string("mask_13")];
+            tensor<bool, [1, 188]> pad_mask = logical_not(x = pad_mask_1)[name = string("pad_mask")];
+            tensor<int32, [1]> input_29_axes_0 = const()[name = string("input_29_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7303872)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7305984)))];
+            fp16 var_166_to_fp16 = const()[name = string("op_166_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 188, 1024]> input_29_cast_fp16 = layer_norm(axes = input_29_axes_0, beta = encoder_module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_feed_forward1_weight_to_fp16, x = x_23_cast_fp16)[name = string("input_29_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7308096)))];
+            tensor<fp16, [4096]> encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15696768)))];
+            tensor<fp16, [1, 188, 4096]> linear_1_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16, x = input_29_cast_fp16)[name = string("linear_1_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_33_cast_fp16 = silu(x = linear_1_cast_fp16)[name = string("input_33_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15705024)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24093696)))];
+            tensor<fp16, [1, 188, 1024]> linear_2_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16, x = input_33_cast_fp16)[name = string("linear_2_cast_fp16")];
+            fp16 var_507_to_fp16 = const()[name = string("op_507_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_508_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_507_to_fp16)[name = string("op_508_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_39_cast_fp16 = add(x = x_23_cast_fp16, y = var_508_cast_fp16)[name = string("input_39_cast_fp16")];
+            tensor<int32, [1]> query_1_axes_0 = const()[name = string("query_1_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24095808)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24097920)))];
+            tensor<fp16, [1, 188, 1024]> query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = encoder_module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_self_att_weight_to_fp16, x = input_39_cast_fp16)[name = string("query_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24100032)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26197248)))];
+            tensor<fp16, [1, 188, 1024]> linear_3_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_q_weight_to_fp16, x = query_1_cast_fp16)[name = string("linear_3_cast_fp16")];
+            tensor<int32, [4]> var_525 = const()[name = string("op_525"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_1_cast_fp16 = reshape(shape = var_525, x = linear_3_cast_fp16)[name = string("q_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26199360)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28296576)))];
+            tensor<fp16, [1, 188, 1024]> linear_4_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_k_weight_to_fp16, x = query_1_cast_fp16)[name = string("linear_4_cast_fp16")];
+            tensor<int32, [4]> var_530 = const()[name = string("op_530"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_1_cast_fp16 = reshape(shape = var_530, x = linear_4_cast_fp16)[name = string("k_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28298688)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30395904)))];
+            tensor<fp16, [1, 188, 1024]> linear_5_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_v_weight_to_fp16, x = query_1_cast_fp16)[name = string("linear_5_cast_fp16")];
+            tensor<int32, [4]> var_535 = const()[name = string("op_535"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_1_cast_fp16 = reshape(shape = var_535, x = linear_5_cast_fp16)[name = string("v_1_cast_fp16")];
+            tensor<int32, [4]> value_5_perm_0 = const()[name = string("value_5_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30398016)))];
+            tensor<fp16, [1, 188, 8, 128]> var_547_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_module_layers_0_self_attn_pos_bias_u_to_fp16)[name = string("op_547_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30400128)))];
+            tensor<fp16, [1, 188, 8, 128]> var_549_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_module_layers_0_self_attn_pos_bias_v_to_fp16)[name = string("op_549_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_1_perm_0 = const()[name = string("q_with_bias_v_1_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_27_transpose_x_0 = const()[name = string("x_27_transpose_x_0"), val = bool(false)];
+            bool x_27_transpose_y_0 = const()[name = string("x_27_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_551_to_fp16 = const()[name = string("op_551_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30402240)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_549_cast_fp16)[name = string("transpose_287")];
+            tensor<fp16, [1, 8, 188, 375]> x_27_cast_fp16 = matmul(transpose_x = x_27_transpose_x_0, transpose_y = x_27_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = var_551_to_fp16)[name = string("x_27_cast_fp16")];
+            tensor<int32, [8]> x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_29_mode_0 = const()[name = string("x_29_mode_0"), val = string("constant")];
+            fp16 const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_29_cast_fp16 = pad(constant_val = const_88_to_fp16, mode = x_29_mode_0, pad = x_29_pad_0, x = x_27_cast_fp16)[name = string("x_29_cast_fp16")];
+            tensor<int32, [4]> var_559 = const()[name = string("op_559"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_31_cast_fp16 = reshape(shape = var_559, x = x_29_cast_fp16)[name = string("x_31_cast_fp16")];
+            tensor<int32, [4]> var_563_begin_0 = const()[name = string("op_563_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_563_end_0 = const()[name = string("op_563_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_563_end_mask_0 = const()[name = string("op_563_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_563_cast_fp16 = slice_by_index(begin = var_563_begin_0, end = var_563_end_0, end_mask = var_563_end_mask_0, x = x_31_cast_fp16)[name = string("op_563_cast_fp16")];
+            tensor<int32, [4]> var_564 = const()[name = string("op_564"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_1_cast_fp16 = reshape(shape = var_564, x = var_563_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<int32, [4]> transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_73 = transpose(perm = transpose_73_perm_0, x = k_1_cast_fp16)[name = string("transpose_285")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_72 = transpose(perm = transpose_72_perm_0, x = var_547_cast_fp16)[name = string("transpose_286")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_1_cast_fp16 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_72, y = transpose_73)[name = string("matrix_ac_1_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_3_end_0 = const()[name = string("matrix_bd_3_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_573_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = string("op_573_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<fp16, [1, 8, 188, 188]> _inversed_scores_1_cast_fp16 = mul(x = var_573_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")];
+            tensor<int32, [1]> mask_15_axes_0 = const()[name = string("mask_15_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188, 188]> mask_15 = expand_dims(axes = mask_15_axes_0, x = mask_13)[name = string("mask_15")];
+            fp16 var_163_to_fp16 = const()[name = string("op_163_to_fp16"), val = fp16(-0x1.388p+13)];
+            tensor<fp16, [1, 8, 188, 188]> scores_3_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_15)[name = string("scores_3_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_579_cast_fp16 = softmax(axis = var_152, x = scores_3_cast_fp16)[name = string("op_579_cast_fp16")];
+            fp16 var_164_to_fp16 = const()[name = string("op_164_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 188]> input_41_cast_fp16 = select(a = var_164_to_fp16, b = var_579_cast_fp16, cond = mask_15)[name = string("input_41_cast_fp16")];
+            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<fp16, [1, 8, 188, 128]> value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_1_cast_fp16)[name = string("transpose_288")];
+            tensor<fp16, [1, 8, 188, 128]> x_33_cast_fp16 = matmul(transpose_x = x_33_transpose_x_0, transpose_y = x_33_transpose_y_0, x = input_41_cast_fp16, y = value_5_cast_fp16)[name = string("x_33_cast_fp16")];
+            tensor<int32, [4]> var_583_perm_0 = const()[name = string("op_583_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_584 = const()[name = string("op_584"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_583_cast_fp16 = transpose(perm = var_583_perm_0, x = x_33_cast_fp16)[name = string("transpose_284")];
+            tensor<fp16, [1, 188, 1024]> input_43_cast_fp16 = reshape(shape = var_584, x = var_583_cast_fp16)[name = string("input_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31170304)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33267520)))];
+            tensor<fp16, [1, 188, 1024]> linear_7_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_out_weight_to_fp16, x = input_43_cast_fp16)[name = string("linear_7_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_47_cast_fp16 = add(x = input_39_cast_fp16, y = linear_7_cast_fp16)[name = string("input_47_cast_fp16")];
+            tensor<int32, [1]> x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33269632)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33271744)))];
+            tensor<fp16, [1, 188, 1024]> x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, beta = encoder_module_layers_0_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_conv_weight_to_fp16, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")];
+            tensor<int32, [3]> input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_0_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_0_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33273856)))];
+            tensor<fp16, [2048]> encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37468224)))];
+            tensor<fp16, [1, 1024, 188]> input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_37_cast_fp16)[name = string("transpose_283")];
+            tensor<fp16, [1, 2048, 188]> input_51_cast_fp16 = conv(bias = encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_0_conv_pointwise_conv1_weight_to_fp16, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")];
+            int32 x_39_split_num_splits_0 = const()[name = string("x_39_split_num_splits_0"), val = int32(2)];
+            int32 x_39_split_axis_0 = const()[name = string("x_39_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_39_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_39_split_cast_fp16_1 = split(axis = x_39_split_axis_0, num_splits = x_39_split_num_splits_0, x = input_51_cast_fp16)[name = string("x_39_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_39_split_1_sigmoid_cast_fp16 = sigmoid(x = x_39_split_cast_fp16_1)[name = string("x_39_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_39_cast_fp16 = mul(x = x_39_split_cast_fp16_0, y = x_39_split_1_sigmoid_cast_fp16)[name = string("x_39_cast_fp16")];
+            tensor<int32, [1]> var_608_axes_0 = const()[name = string("op_608_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188]> var_608 = expand_dims(axes = var_608_axes_0, x = pad_mask)[name = string("op_608")];
+            tensor<fp16, [1, 1024, 188]> input_53_cast_fp16 = select(a = var_164_to_fp16, b = x_39_cast_fp16, cond = var_608)[name = string("input_53_cast_fp16")];
+            tensor<int32, [6]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("constant")];
+            fp16 const_91_to_fp16 = const()[name = string("const_91_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_55_cast_fp16 = pad(constant_val = const_91_to_fp16, mode = input_55_mode_0, pad = input_55_pad_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")];
+            string input_57_pad_type_0 = const()[name = string("input_57_pad_type_0"), val = string("valid")];
+            int32 input_57_groups_0 = const()[name = string("input_57_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_57_strides_0 = const()[name = string("input_57_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_57_dilations_0 = const()[name = string("input_57_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_322_to_fp16 = const()[name = string("const_322_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37472384)))];
+            tensor<fp16, [1024]> const_323_to_fp16 = const()[name = string("const_323_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37490880)))];
+            tensor<fp16, [1, 1024, 188]> input_59_cast_fp16 = conv(bias = const_323_to_fp16, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_322_to_fp16, x = input_55_cast_fp16)[name = string("input_59_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_61_cast_fp16 = silu(x = input_59_cast_fp16)[name = string("input_61_cast_fp16")];
+            string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37492992)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39590208)))];
+            tensor<fp16, [1, 1024, 188]> x_41_cast_fp16 = conv(bias = encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16, x = input_61_cast_fp16)[name = string("x_41_cast_fp16")];
+            tensor<int32, [3]> input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_41_cast_fp16)[name = string("transpose_282")];
+            tensor<fp16, [1, 188, 1024]> input_65_cast_fp16 = add(x = input_47_cast_fp16, y = input_63_cast_fp16)[name = string("input_65_cast_fp16")];
+            tensor<int32, [1]> input_67_axes_0 = const()[name = string("input_67_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39592320)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39594432)))];
+            tensor<fp16, [1, 188, 1024]> input_67_cast_fp16 = layer_norm(axes = input_67_axes_0, beta = encoder_module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39596544)))];
+            tensor<fp16, [4096]> encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47985216)))];
+            tensor<fp16, [1, 188, 4096]> linear_8_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16, x = input_67_cast_fp16)[name = string("linear_8_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_71_cast_fp16 = silu(x = linear_8_cast_fp16)[name = string("input_71_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47993472)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56382144)))];
+            tensor<fp16, [1, 188, 1024]> linear_9_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16, x = input_71_cast_fp16)[name = string("linear_9_cast_fp16")];
+            fp16 var_650_to_fp16 = const()[name = string("op_650_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_651_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_650_to_fp16)[name = string("op_651_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_77_cast_fp16 = add(x = input_65_cast_fp16, y = var_651_cast_fp16)[name = string("input_77_cast_fp16")];
+            tensor<int32, [1]> input_79_axes_0 = const()[name = string("input_79_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56384256)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56386368)))];
+            tensor<fp16, [1, 188, 1024]> input_79_cast_fp16 = layer_norm(axes = input_79_axes_0, beta = encoder_module_layers_0_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_out_weight_to_fp16, x = input_77_cast_fp16)[name = string("input_79_cast_fp16")];
+            tensor<int32, [1]> input_81_axes_0 = const()[name = string("input_81_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56388480)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56390592)))];
+            tensor<fp16, [1, 188, 1024]> input_81_cast_fp16 = layer_norm(axes = input_81_axes_0, beta = encoder_module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_79_cast_fp16)[name = string("input_81_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56392704)))];
+            tensor<fp16, [4096]> encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64781376)))];
+            tensor<fp16, [1, 188, 4096]> linear_10_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16, x = input_81_cast_fp16)[name = string("linear_10_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_85_cast_fp16 = silu(x = linear_10_cast_fp16)[name = string("input_85_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64789632)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73178304)))];
+            tensor<fp16, [1, 188, 1024]> linear_11_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16, x = input_85_cast_fp16)[name = string("linear_11_cast_fp16")];
+            fp16 var_681_to_fp16 = const()[name = string("op_681_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_682_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_681_to_fp16)[name = string("op_682_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_91_cast_fp16 = add(x = input_79_cast_fp16, y = var_682_cast_fp16)[name = string("input_91_cast_fp16")];
+            tensor<int32, [1]> query_3_axes_0 = const()[name = string("query_3_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73180416)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73182528)))];
+            tensor<fp16, [1, 188, 1024]> query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = encoder_module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_self_att_weight_to_fp16, x = input_91_cast_fp16)[name = string("query_3_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73184640)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75281856)))];
+            tensor<fp16, [1, 188, 1024]> linear_12_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_q_weight_to_fp16, x = query_3_cast_fp16)[name = string("linear_12_cast_fp16")];
+            tensor<int32, [4]> var_699 = const()[name = string("op_699"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_7_cast_fp16 = reshape(shape = var_699, x = linear_12_cast_fp16)[name = string("q_7_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75283968)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77381184)))];
+            tensor<fp16, [1, 188, 1024]> linear_13_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_k_weight_to_fp16, x = query_3_cast_fp16)[name = string("linear_13_cast_fp16")];
+            tensor<int32, [4]> var_704 = const()[name = string("op_704"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_5_cast_fp16 = reshape(shape = var_704, x = linear_13_cast_fp16)[name = string("k_5_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77383296)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79480512)))];
+            tensor<fp16, [1, 188, 1024]> linear_14_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_v_weight_to_fp16, x = query_3_cast_fp16)[name = string("linear_14_cast_fp16")];
+            tensor<int32, [4]> var_709 = const()[name = string("op_709"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_3_cast_fp16 = reshape(shape = var_709, x = linear_14_cast_fp16)[name = string("v_3_cast_fp16")];
+            tensor<int32, [4]> value_7_perm_0 = const()[name = string("value_7_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79482624)))];
+            tensor<fp16, [1, 188, 8, 128]> var_721_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_module_layers_1_self_attn_pos_bias_u_to_fp16)[name = string("op_721_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79484736)))];
+            tensor<fp16, [1, 188, 8, 128]> var_723_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_module_layers_1_self_attn_pos_bias_v_to_fp16)[name = string("op_723_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_3_perm_0 = const()[name = string("q_with_bias_v_3_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_49_transpose_x_0 = const()[name = string("x_49_transpose_x_0"), val = bool(false)];
+            bool x_49_transpose_y_0 = const()[name = string("x_49_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_725_to_fp16 = const()[name = string("op_725_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79486848)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_723_cast_fp16)[name = string("transpose_280")];
+            tensor<fp16, [1, 8, 188, 375]> x_49_cast_fp16 = matmul(transpose_x = x_49_transpose_x_0, transpose_y = x_49_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = var_725_to_fp16)[name = string("x_49_cast_fp16")];
+            tensor<int32, [8]> x_51_pad_0 = const()[name = string("x_51_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_51_mode_0 = const()[name = string("x_51_mode_0"), val = string("constant")];
+            fp16 const_98_to_fp16 = const()[name = string("const_98_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_51_cast_fp16 = pad(constant_val = const_98_to_fp16, mode = x_51_mode_0, pad = x_51_pad_0, x = x_49_cast_fp16)[name = string("x_51_cast_fp16")];
+            tensor<int32, [4]> var_733 = const()[name = string("op_733"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_53_cast_fp16 = reshape(shape = var_733, x = x_51_cast_fp16)[name = string("x_53_cast_fp16")];
+            tensor<int32, [4]> var_737_begin_0 = const()[name = string("op_737_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_737_end_0 = const()[name = string("op_737_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_737_end_mask_0 = const()[name = string("op_737_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_737_cast_fp16 = slice_by_index(begin = var_737_begin_0, end = var_737_end_0, end_mask = var_737_end_mask_0, x = x_53_cast_fp16)[name = string("op_737_cast_fp16")];
+            tensor<int32, [4]> var_738 = const()[name = string("op_738"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_5_cast_fp16 = reshape(shape = var_738, x = var_737_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<int32, [4]> transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_75 = transpose(perm = transpose_75_perm_0, x = k_5_cast_fp16)[name = string("transpose_278")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_74 = transpose(perm = transpose_74_perm_0, x = var_721_cast_fp16)[name = string("transpose_279")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_3_cast_fp16 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_74, y = transpose_75)[name = string("matrix_ac_3_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_7_end_0 = const()[name = string("matrix_bd_7_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_747_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = string("op_747_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<fp16, [1, 8, 188, 188]> _inversed_scores_5_cast_fp16 = mul(x = var_747_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_7_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_15)[name = string("scores_7_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_753_cast_fp16 = softmax(axis = var_152, x = scores_7_cast_fp16)[name = string("op_753_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_93_cast_fp16 = select(a = var_164_to_fp16, b = var_753_cast_fp16, cond = mask_15)[name = string("input_93_cast_fp16")];
+            bool x_55_transpose_x_0 = const()[name = string("x_55_transpose_x_0"), val = bool(false)];
+            bool x_55_transpose_y_0 = const()[name = string("x_55_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_3_cast_fp16)[name = string("transpose_281")];
+            tensor<fp16, [1, 8, 188, 128]> x_55_cast_fp16 = matmul(transpose_x = x_55_transpose_x_0, transpose_y = x_55_transpose_y_0, x = input_93_cast_fp16, y = value_7_cast_fp16)[name = string("x_55_cast_fp16")];
+            tensor<int32, [4]> var_757_perm_0 = const()[name = string("op_757_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_758 = const()[name = string("op_758"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_757_cast_fp16 = transpose(perm = var_757_perm_0, x = x_55_cast_fp16)[name = string("transpose_277")];
+            tensor<fp16, [1, 188, 1024]> input_95_cast_fp16 = reshape(shape = var_758, x = var_757_cast_fp16)[name = string("input_95_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80254912)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82352128)))];
+            tensor<fp16, [1, 188, 1024]> linear_16_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_out_weight_to_fp16, x = input_95_cast_fp16)[name = string("linear_16_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_99_cast_fp16 = add(x = input_91_cast_fp16, y = linear_16_cast_fp16)[name = string("input_99_cast_fp16")];
+            tensor<int32, [1]> x_59_axes_0 = const()[name = string("x_59_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82354240)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82356352)))];
+            tensor<fp16, [1, 188, 1024]> x_59_cast_fp16 = layer_norm(axes = x_59_axes_0, beta = encoder_module_layers_1_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_conv_weight_to_fp16, x = input_99_cast_fp16)[name = string("x_59_cast_fp16")];
+            tensor<int32, [3]> input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_1_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_1_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82358464)))];
+            tensor<fp16, [2048]> encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86552832)))];
+            tensor<fp16, [1, 1024, 188]> input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_59_cast_fp16)[name = string("transpose_276")];
+            tensor<fp16, [1, 2048, 188]> input_103_cast_fp16 = conv(bias = encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_1_conv_pointwise_conv1_weight_to_fp16, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")];
+            int32 x_61_split_num_splits_0 = const()[name = string("x_61_split_num_splits_0"), val = int32(2)];
+            int32 x_61_split_axis_0 = const()[name = string("x_61_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_61_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_61_split_cast_fp16_1 = split(axis = x_61_split_axis_0, num_splits = x_61_split_num_splits_0, x = input_103_cast_fp16)[name = string("x_61_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_61_split_1_sigmoid_cast_fp16 = sigmoid(x = x_61_split_cast_fp16_1)[name = string("x_61_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_61_cast_fp16 = mul(x = x_61_split_cast_fp16_0, y = x_61_split_1_sigmoid_cast_fp16)[name = string("x_61_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_105_cast_fp16 = select(a = var_164_to_fp16, b = x_61_cast_fp16, cond = var_608)[name = string("input_105_cast_fp16")];
+            tensor<int32, [6]> input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("constant")];
+            fp16 const_101_to_fp16 = const()[name = string("const_101_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_107_cast_fp16 = pad(constant_val = const_101_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")];
+            string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("valid")];
+            int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_324_to_fp16 = const()[name = string("const_324_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86556992)))];
+            tensor<fp16, [1024]> const_325_to_fp16 = const()[name = string("const_325_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86575488)))];
+            tensor<fp16, [1, 1024, 188]> input_111_cast_fp16 = conv(bias = const_325_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_324_to_fp16, x = input_107_cast_fp16)[name = string("input_111_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_113_cast_fp16 = silu(x = input_111_cast_fp16)[name = string("input_113_cast_fp16")];
+            string x_63_pad_type_0 = const()[name = string("x_63_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_63_strides_0 = const()[name = string("x_63_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_63_pad_0 = const()[name = string("x_63_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_63_dilations_0 = const()[name = string("x_63_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_63_groups_0 = const()[name = string("x_63_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86577600)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88674816)))];
+            tensor<fp16, [1, 1024, 188]> x_63_cast_fp16 = conv(bias = encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16, dilations = x_63_dilations_0, groups = x_63_groups_0, pad = x_63_pad_0, pad_type = x_63_pad_type_0, strides = x_63_strides_0, weight = encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16, x = input_113_cast_fp16)[name = string("x_63_cast_fp16")];
+            tensor<int32, [3]> input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_63_cast_fp16)[name = string("transpose_275")];
+            tensor<fp16, [1, 188, 1024]> input_117_cast_fp16 = add(x = input_99_cast_fp16, y = input_115_cast_fp16)[name = string("input_117_cast_fp16")];
+            tensor<int32, [1]> input_119_axes_0 = const()[name = string("input_119_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88676928)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88679040)))];
+            tensor<fp16, [1, 188, 1024]> input_119_cast_fp16 = layer_norm(axes = input_119_axes_0, beta = encoder_module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88681152)))];
+            tensor<fp16, [4096]> encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97069824)))];
+            tensor<fp16, [1, 188, 4096]> linear_17_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16, x = input_119_cast_fp16)[name = string("linear_17_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_123_cast_fp16 = silu(x = linear_17_cast_fp16)[name = string("input_123_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97078080)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105466752)))];
+            tensor<fp16, [1, 188, 1024]> linear_18_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16, x = input_123_cast_fp16)[name = string("linear_18_cast_fp16")];
+            fp16 var_824_to_fp16 = const()[name = string("op_824_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_825_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_824_to_fp16)[name = string("op_825_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_129_cast_fp16 = add(x = input_117_cast_fp16, y = var_825_cast_fp16)[name = string("input_129_cast_fp16")];
+            tensor<int32, [1]> input_131_axes_0 = const()[name = string("input_131_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105468864)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105470976)))];
+            tensor<fp16, [1, 188, 1024]> input_131_cast_fp16 = layer_norm(axes = input_131_axes_0, beta = encoder_module_layers_1_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_out_weight_to_fp16, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")];
+            tensor<int32, [1]> input_133_axes_0 = const()[name = string("input_133_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105473088)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105475200)))];
+            tensor<fp16, [1, 188, 1024]> input_133_cast_fp16 = layer_norm(axes = input_133_axes_0, beta = encoder_module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_131_cast_fp16)[name = string("input_133_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105477312)))];
+            tensor<fp16, [4096]> encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113865984)))];
+            tensor<fp16, [1, 188, 4096]> linear_19_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16, x = input_133_cast_fp16)[name = string("linear_19_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_137_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("input_137_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113874240)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122262912)))];
+            tensor<fp16, [1, 188, 1024]> linear_20_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16, x = input_137_cast_fp16)[name = string("linear_20_cast_fp16")];
+            fp16 var_855_to_fp16 = const()[name = string("op_855_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_856_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_855_to_fp16)[name = string("op_856_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_143_cast_fp16 = add(x = input_131_cast_fp16, y = var_856_cast_fp16)[name = string("input_143_cast_fp16")];
+            tensor<int32, [1]> query_5_axes_0 = const()[name = string("query_5_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122265024)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122267136)))];
+            tensor<fp16, [1, 188, 1024]> query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = encoder_module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_self_att_weight_to_fp16, x = input_143_cast_fp16)[name = string("query_5_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122269248)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124366464)))];
+            tensor<fp16, [1, 188, 1024]> linear_21_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_q_weight_to_fp16, x = query_5_cast_fp16)[name = string("linear_21_cast_fp16")];
+            tensor<int32, [4]> var_873 = const()[name = string("op_873"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_13_cast_fp16 = reshape(shape = var_873, x = linear_21_cast_fp16)[name = string("q_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124368576)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126465792)))];
+            tensor<fp16, [1, 188, 1024]> linear_22_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_k_weight_to_fp16, x = query_5_cast_fp16)[name = string("linear_22_cast_fp16")];
+            tensor<int32, [4]> var_878 = const()[name = string("op_878"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_9_cast_fp16 = reshape(shape = var_878, x = linear_22_cast_fp16)[name = string("k_9_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126467904)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(128565120)))];
+            tensor<fp16, [1, 188, 1024]> linear_23_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_v_weight_to_fp16, x = query_5_cast_fp16)[name = string("linear_23_cast_fp16")];
+            tensor<int32, [4]> var_883 = const()[name = string("op_883"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_5_cast_fp16 = reshape(shape = var_883, x = linear_23_cast_fp16)[name = string("v_5_cast_fp16")];
+            tensor<int32, [4]> value_9_perm_0 = const()[name = string("value_9_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(128567232)))];
+            tensor<fp16, [1, 188, 8, 128]> var_895_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_module_layers_2_self_attn_pos_bias_u_to_fp16)[name = string("op_895_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(128569344)))];
+            tensor<fp16, [1, 188, 8, 128]> var_897_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_module_layers_2_self_attn_pos_bias_v_to_fp16)[name = string("op_897_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_5_perm_0 = const()[name = string("q_with_bias_v_5_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_71_transpose_x_0 = const()[name = string("x_71_transpose_x_0"), val = bool(false)];
+            bool x_71_transpose_y_0 = const()[name = string("x_71_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_899_to_fp16 = const()[name = string("op_899_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(128571456)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_897_cast_fp16)[name = string("transpose_273")];
+            tensor<fp16, [1, 8, 188, 375]> x_71_cast_fp16 = matmul(transpose_x = x_71_transpose_x_0, transpose_y = x_71_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = var_899_to_fp16)[name = string("x_71_cast_fp16")];
+            tensor<int32, [8]> x_73_pad_0 = const()[name = string("x_73_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("constant")];
+            fp16 const_108_to_fp16 = const()[name = string("const_108_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_73_cast_fp16 = pad(constant_val = const_108_to_fp16, mode = x_73_mode_0, pad = x_73_pad_0, x = x_71_cast_fp16)[name = string("x_73_cast_fp16")];
+            tensor<int32, [4]> var_907 = const()[name = string("op_907"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_75_cast_fp16 = reshape(shape = var_907, x = x_73_cast_fp16)[name = string("x_75_cast_fp16")];
+            tensor<int32, [4]> var_911_begin_0 = const()[name = string("op_911_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_911_end_0 = const()[name = string("op_911_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_911_end_mask_0 = const()[name = string("op_911_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_911_cast_fp16 = slice_by_index(begin = var_911_begin_0, end = var_911_end_0, end_mask = var_911_end_mask_0, x = x_75_cast_fp16)[name = string("op_911_cast_fp16")];
+            tensor<int32, [4]> var_912 = const()[name = string("op_912"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_9_cast_fp16 = reshape(shape = var_912, x = var_911_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<int32, [4]> transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_77 = transpose(perm = transpose_77_perm_0, x = k_9_cast_fp16)[name = string("transpose_271")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_76 = transpose(perm = transpose_76_perm_0, x = var_895_cast_fp16)[name = string("transpose_272")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_5_cast_fp16 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_76, y = transpose_77)[name = string("matrix_ac_5_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_11_end_0 = const()[name = string("matrix_bd_11_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_921_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = string("op_921_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<fp16, [1, 8, 188, 188]> _inversed_scores_9_cast_fp16 = mul(x = var_921_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_11_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_15)[name = string("scores_11_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_927_cast_fp16 = softmax(axis = var_152, x = scores_11_cast_fp16)[name = string("op_927_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_145_cast_fp16 = select(a = var_164_to_fp16, b = var_927_cast_fp16, cond = mask_15)[name = string("input_145_cast_fp16")];
+            bool x_77_transpose_x_0 = const()[name = string("x_77_transpose_x_0"), val = bool(false)];
+            bool x_77_transpose_y_0 = const()[name = string("x_77_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_5_cast_fp16)[name = string("transpose_274")];
+            tensor<fp16, [1, 8, 188, 128]> x_77_cast_fp16 = matmul(transpose_x = x_77_transpose_x_0, transpose_y = x_77_transpose_y_0, x = input_145_cast_fp16, y = value_9_cast_fp16)[name = string("x_77_cast_fp16")];
+            tensor<int32, [4]> var_931_perm_0 = const()[name = string("op_931_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_932 = const()[name = string("op_932"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_931_cast_fp16 = transpose(perm = var_931_perm_0, x = x_77_cast_fp16)[name = string("transpose_270")];
+            tensor<fp16, [1, 188, 1024]> input_147_cast_fp16 = reshape(shape = var_932, x = var_931_cast_fp16)[name = string("input_147_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129339520)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131436736)))];
+            tensor<fp16, [1, 188, 1024]> linear_25_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_out_weight_to_fp16, x = input_147_cast_fp16)[name = string("linear_25_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_151_cast_fp16 = add(x = input_143_cast_fp16, y = linear_25_cast_fp16)[name = string("input_151_cast_fp16")];
+            tensor<int32, [1]> x_81_axes_0 = const()[name = string("x_81_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131438848)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131440960)))];
+            tensor<fp16, [1, 188, 1024]> x_81_cast_fp16 = layer_norm(axes = x_81_axes_0, beta = encoder_module_layers_2_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_conv_weight_to_fp16, x = input_151_cast_fp16)[name = string("x_81_cast_fp16")];
+            tensor<int32, [3]> input_153_perm_0 = const()[name = string("input_153_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_2_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_2_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131443072)))];
+            tensor<fp16, [2048]> encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135637440)))];
+            tensor<fp16, [1, 1024, 188]> input_153_cast_fp16 = transpose(perm = input_153_perm_0, x = x_81_cast_fp16)[name = string("transpose_269")];
+            tensor<fp16, [1, 2048, 188]> input_155_cast_fp16 = conv(bias = encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_2_conv_pointwise_conv1_weight_to_fp16, x = input_153_cast_fp16)[name = string("input_155_cast_fp16")];
+            int32 x_83_split_num_splits_0 = const()[name = string("x_83_split_num_splits_0"), val = int32(2)];
+            int32 x_83_split_axis_0 = const()[name = string("x_83_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_83_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_83_split_cast_fp16_1 = split(axis = x_83_split_axis_0, num_splits = x_83_split_num_splits_0, x = input_155_cast_fp16)[name = string("x_83_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_83_split_1_sigmoid_cast_fp16 = sigmoid(x = x_83_split_cast_fp16_1)[name = string("x_83_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_83_cast_fp16 = mul(x = x_83_split_cast_fp16_0, y = x_83_split_1_sigmoid_cast_fp16)[name = string("x_83_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_157_cast_fp16 = select(a = var_164_to_fp16, b = x_83_cast_fp16, cond = var_608)[name = string("input_157_cast_fp16")];
+            tensor<int32, [6]> input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_159_mode_0 = const()[name = string("input_159_mode_0"), val = string("constant")];
+            fp16 const_111_to_fp16 = const()[name = string("const_111_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_159_cast_fp16 = pad(constant_val = const_111_to_fp16, mode = input_159_mode_0, pad = input_159_pad_0, x = input_157_cast_fp16)[name = string("input_159_cast_fp16")];
+            string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("valid")];
+            int32 input_161_groups_0 = const()[name = string("input_161_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_161_strides_0 = const()[name = string("input_161_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_161_dilations_0 = const()[name = string("input_161_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_326_to_fp16 = const()[name = string("const_326_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135641600)))];
+            tensor<fp16, [1024]> const_327_to_fp16 = const()[name = string("const_327_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135660096)))];
+            tensor<fp16, [1, 1024, 188]> input_163_cast_fp16 = conv(bias = const_327_to_fp16, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_326_to_fp16, x = input_159_cast_fp16)[name = string("input_163_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_165_cast_fp16 = silu(x = input_163_cast_fp16)[name = string("input_165_cast_fp16")];
+            string x_85_pad_type_0 = const()[name = string("x_85_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_85_strides_0 = const()[name = string("x_85_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_85_pad_0 = const()[name = string("x_85_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_85_dilations_0 = const()[name = string("x_85_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_85_groups_0 = const()[name = string("x_85_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135662208)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137759424)))];
+            tensor<fp16, [1, 1024, 188]> x_85_cast_fp16 = conv(bias = encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16, dilations = x_85_dilations_0, groups = x_85_groups_0, pad = x_85_pad_0, pad_type = x_85_pad_type_0, strides = x_85_strides_0, weight = encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16, x = input_165_cast_fp16)[name = string("x_85_cast_fp16")];
+            tensor<int32, [3]> input_167_perm_0 = const()[name = string("input_167_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_167_cast_fp16 = transpose(perm = input_167_perm_0, x = x_85_cast_fp16)[name = string("transpose_268")];
+            tensor<fp16, [1, 188, 1024]> input_169_cast_fp16 = add(x = input_151_cast_fp16, y = input_167_cast_fp16)[name = string("input_169_cast_fp16")];
+            tensor<int32, [1]> input_171_axes_0 = const()[name = string("input_171_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137761536)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137763648)))];
+            tensor<fp16, [1, 188, 1024]> input_171_cast_fp16 = layer_norm(axes = input_171_axes_0, beta = encoder_module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_169_cast_fp16)[name = string("input_171_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137765760)))];
+            tensor<fp16, [4096]> encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146154432)))];
+            tensor<fp16, [1, 188, 4096]> linear_26_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16, x = input_171_cast_fp16)[name = string("linear_26_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_175_cast_fp16 = silu(x = linear_26_cast_fp16)[name = string("input_175_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146162688)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154551360)))];
+            tensor<fp16, [1, 188, 1024]> linear_27_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16, x = input_175_cast_fp16)[name = string("linear_27_cast_fp16")];
+            fp16 var_998_to_fp16 = const()[name = string("op_998_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_999_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_998_to_fp16)[name = string("op_999_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_181_cast_fp16 = add(x = input_169_cast_fp16, y = var_999_cast_fp16)[name = string("input_181_cast_fp16")];
+            tensor<int32, [1]> input_183_axes_0 = const()[name = string("input_183_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154553472)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154555584)))];
+            tensor<fp16, [1, 188, 1024]> input_183_cast_fp16 = layer_norm(axes = input_183_axes_0, beta = encoder_module_layers_2_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_out_weight_to_fp16, x = input_181_cast_fp16)[name = string("input_183_cast_fp16")];
+            tensor<int32, [1]> input_185_axes_0 = const()[name = string("input_185_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154557696)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154559808)))];
+            tensor<fp16, [1, 188, 1024]> input_185_cast_fp16 = layer_norm(axes = input_185_axes_0, beta = encoder_module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_183_cast_fp16)[name = string("input_185_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154561920)))];
+            tensor<fp16, [4096]> encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162950592)))];
+            tensor<fp16, [1, 188, 4096]> linear_28_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16, x = input_185_cast_fp16)[name = string("linear_28_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_189_cast_fp16 = silu(x = linear_28_cast_fp16)[name = string("input_189_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162958848)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171347520)))];
+            tensor<fp16, [1, 188, 1024]> linear_29_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16, x = input_189_cast_fp16)[name = string("linear_29_cast_fp16")];
+            fp16 var_1029_to_fp16 = const()[name = string("op_1029_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1030_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_1029_to_fp16)[name = string("op_1030_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_195_cast_fp16 = add(x = input_183_cast_fp16, y = var_1030_cast_fp16)[name = string("input_195_cast_fp16")];
+            tensor<int32, [1]> query_7_axes_0 = const()[name = string("query_7_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171349632)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171351744)))];
+            tensor<fp16, [1, 188, 1024]> query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = encoder_module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_self_att_weight_to_fp16, x = input_195_cast_fp16)[name = string("query_7_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171353856)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173451072)))];
+            tensor<fp16, [1, 188, 1024]> linear_30_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_q_weight_to_fp16, x = query_7_cast_fp16)[name = string("linear_30_cast_fp16")];
+            tensor<int32, [4]> var_1047 = const()[name = string("op_1047"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_19_cast_fp16 = reshape(shape = var_1047, x = linear_30_cast_fp16)[name = string("q_19_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173453184)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175550400)))];
+            tensor<fp16, [1, 188, 1024]> linear_31_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_k_weight_to_fp16, x = query_7_cast_fp16)[name = string("linear_31_cast_fp16")];
+            tensor<int32, [4]> var_1052 = const()[name = string("op_1052"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_13_cast_fp16 = reshape(shape = var_1052, x = linear_31_cast_fp16)[name = string("k_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175552512)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177649728)))];
+            tensor<fp16, [1, 188, 1024]> linear_32_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_v_weight_to_fp16, x = query_7_cast_fp16)[name = string("linear_32_cast_fp16")];
+            tensor<int32, [4]> var_1057 = const()[name = string("op_1057"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_7_cast_fp16 = reshape(shape = var_1057, x = linear_32_cast_fp16)[name = string("v_7_cast_fp16")];
+            tensor<int32, [4]> value_11_perm_0 = const()[name = string("value_11_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177651840)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1069_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_module_layers_3_self_attn_pos_bias_u_to_fp16)[name = string("op_1069_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177653952)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1071_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_module_layers_3_self_attn_pos_bias_v_to_fp16)[name = string("op_1071_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_7_perm_0 = const()[name = string("q_with_bias_v_7_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_93_transpose_x_0 = const()[name = string("x_93_transpose_x_0"), val = bool(false)];
+            bool x_93_transpose_y_0 = const()[name = string("x_93_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_1073_to_fp16 = const()[name = string("op_1073_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177656064)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_1071_cast_fp16)[name = string("transpose_266")];
+            tensor<fp16, [1, 8, 188, 375]> x_93_cast_fp16 = matmul(transpose_x = x_93_transpose_x_0, transpose_y = x_93_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = var_1073_to_fp16)[name = string("x_93_cast_fp16")];
+            tensor<int32, [8]> x_95_pad_0 = const()[name = string("x_95_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("constant")];
+            fp16 const_118_to_fp16 = const()[name = string("const_118_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_95_cast_fp16 = pad(constant_val = const_118_to_fp16, mode = x_95_mode_0, pad = x_95_pad_0, x = x_93_cast_fp16)[name = string("x_95_cast_fp16")];
+            tensor<int32, [4]> var_1081 = const()[name = string("op_1081"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_97_cast_fp16 = reshape(shape = var_1081, x = x_95_cast_fp16)[name = string("x_97_cast_fp16")];
+            tensor<int32, [4]> var_1085_begin_0 = const()[name = string("op_1085_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1085_end_0 = const()[name = string("op_1085_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1085_end_mask_0 = const()[name = string("op_1085_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1085_cast_fp16 = slice_by_index(begin = var_1085_begin_0, end = var_1085_end_0, end_mask = var_1085_end_mask_0, x = x_97_cast_fp16)[name = string("op_1085_cast_fp16")];
+            tensor<int32, [4]> var_1086 = const()[name = string("op_1086"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_13_cast_fp16 = reshape(shape = var_1086, x = var_1085_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<int32, [4]> transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_79 = transpose(perm = transpose_79_perm_0, x = k_13_cast_fp16)[name = string("transpose_264")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_78 = transpose(perm = transpose_78_perm_0, x = var_1069_cast_fp16)[name = string("transpose_265")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_7_cast_fp16 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_78, y = transpose_79)[name = string("matrix_ac_7_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_15_end_0 = const()[name = string("matrix_bd_15_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1095_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = string("op_1095_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<fp16, [1, 8, 188, 188]> _inversed_scores_13_cast_fp16 = mul(x = var_1095_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = string("_inversed_scores_13_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_15_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_15)[name = string("scores_15_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1101_cast_fp16 = softmax(axis = var_152, x = scores_15_cast_fp16)[name = string("op_1101_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_197_cast_fp16 = select(a = var_164_to_fp16, b = var_1101_cast_fp16, cond = mask_15)[name = string("input_197_cast_fp16")];
+            bool x_99_transpose_x_0 = const()[name = string("x_99_transpose_x_0"), val = bool(false)];
+            bool x_99_transpose_y_0 = const()[name = string("x_99_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_7_cast_fp16)[name = string("transpose_267")];
+            tensor<fp16, [1, 8, 188, 128]> x_99_cast_fp16 = matmul(transpose_x = x_99_transpose_x_0, transpose_y = x_99_transpose_y_0, x = input_197_cast_fp16, y = value_11_cast_fp16)[name = string("x_99_cast_fp16")];
+            tensor<int32, [4]> var_1105_perm_0 = const()[name = string("op_1105_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1106 = const()[name = string("op_1106"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1105_cast_fp16 = transpose(perm = var_1105_perm_0, x = x_99_cast_fp16)[name = string("transpose_263")];
+            tensor<fp16, [1, 188, 1024]> input_199_cast_fp16 = reshape(shape = var_1106, x = var_1105_cast_fp16)[name = string("input_199_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178424128)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180521344)))];
+            tensor<fp16, [1, 188, 1024]> linear_34_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_out_weight_to_fp16, x = input_199_cast_fp16)[name = string("linear_34_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_203_cast_fp16 = add(x = input_195_cast_fp16, y = linear_34_cast_fp16)[name = string("input_203_cast_fp16")];
+            tensor<int32, [1]> x_103_axes_0 = const()[name = string("x_103_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180523456)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180525568)))];
+            tensor<fp16, [1, 188, 1024]> x_103_cast_fp16 = layer_norm(axes = x_103_axes_0, beta = encoder_module_layers_3_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_conv_weight_to_fp16, x = input_203_cast_fp16)[name = string("x_103_cast_fp16")];
+            tensor<int32, [3]> input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_207_pad_type_0 = const()[name = string("input_207_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_207_strides_0 = const()[name = string("input_207_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_207_pad_0 = const()[name = string("input_207_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_207_dilations_0 = const()[name = string("input_207_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_207_groups_0 = const()[name = string("input_207_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_3_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_3_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180527680)))];
+            tensor<fp16, [2048]> encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184722048)))];
+            tensor<fp16, [1, 1024, 188]> input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = x_103_cast_fp16)[name = string("transpose_262")];
+            tensor<fp16, [1, 2048, 188]> input_207_cast_fp16 = conv(bias = encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_3_conv_pointwise_conv1_weight_to_fp16, x = input_205_cast_fp16)[name = string("input_207_cast_fp16")];
+            int32 x_105_split_num_splits_0 = const()[name = string("x_105_split_num_splits_0"), val = int32(2)];
+            int32 x_105_split_axis_0 = const()[name = string("x_105_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_105_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_105_split_cast_fp16_1 = split(axis = x_105_split_axis_0, num_splits = x_105_split_num_splits_0, x = input_207_cast_fp16)[name = string("x_105_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_105_split_1_sigmoid_cast_fp16 = sigmoid(x = x_105_split_cast_fp16_1)[name = string("x_105_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_105_cast_fp16 = mul(x = x_105_split_cast_fp16_0, y = x_105_split_1_sigmoid_cast_fp16)[name = string("x_105_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_209_cast_fp16 = select(a = var_164_to_fp16, b = x_105_cast_fp16, cond = var_608)[name = string("input_209_cast_fp16")];
+            tensor<int32, [6]> input_211_pad_0 = const()[name = string("input_211_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_211_mode_0 = const()[name = string("input_211_mode_0"), val = string("constant")];
+            fp16 const_121_to_fp16 = const()[name = string("const_121_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_211_cast_fp16 = pad(constant_val = const_121_to_fp16, mode = input_211_mode_0, pad = input_211_pad_0, x = input_209_cast_fp16)[name = string("input_211_cast_fp16")];
+            string input_213_pad_type_0 = const()[name = string("input_213_pad_type_0"), val = string("valid")];
+            int32 input_213_groups_0 = const()[name = string("input_213_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_213_strides_0 = const()[name = string("input_213_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_213_dilations_0 = const()[name = string("input_213_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_328_to_fp16 = const()[name = string("const_328_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184726208)))];
+            tensor<fp16, [1024]> const_329_to_fp16 = const()[name = string("const_329_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184744704)))];
+            tensor<fp16, [1, 1024, 188]> input_215_cast_fp16 = conv(bias = const_329_to_fp16, dilations = input_213_dilations_0, groups = input_213_groups_0, pad = input_213_pad_0, pad_type = input_213_pad_type_0, strides = input_213_strides_0, weight = const_328_to_fp16, x = input_211_cast_fp16)[name = string("input_215_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_217_cast_fp16 = silu(x = input_215_cast_fp16)[name = string("input_217_cast_fp16")];
+            string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184746816)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186844032)))];
+            tensor<fp16, [1, 1024, 188]> x_107_cast_fp16 = conv(bias = encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16, x = input_217_cast_fp16)[name = string("x_107_cast_fp16")];
+            tensor<int32, [3]> input_219_perm_0 = const()[name = string("input_219_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_219_cast_fp16 = transpose(perm = input_219_perm_0, x = x_107_cast_fp16)[name = string("transpose_261")];
+            tensor<fp16, [1, 188, 1024]> input_221_cast_fp16 = add(x = input_203_cast_fp16, y = input_219_cast_fp16)[name = string("input_221_cast_fp16")];
+            tensor<int32, [1]> input_223_axes_0 = const()[name = string("input_223_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186846144)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186848256)))];
+            tensor<fp16, [1, 188, 1024]> input_223_cast_fp16 = layer_norm(axes = input_223_axes_0, beta = encoder_module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_221_cast_fp16)[name = string("input_223_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186850368)))];
+            tensor<fp16, [4096]> encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195239040)))];
+            tensor<fp16, [1, 188, 4096]> linear_35_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16, x = input_223_cast_fp16)[name = string("linear_35_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_227_cast_fp16 = silu(x = linear_35_cast_fp16)[name = string("input_227_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195247296)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203635968)))];
+            tensor<fp16, [1, 188, 1024]> linear_36_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16, x = input_227_cast_fp16)[name = string("linear_36_cast_fp16")];
+            fp16 var_1172_to_fp16 = const()[name = string("op_1172_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1173_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_1172_to_fp16)[name = string("op_1173_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_233_cast_fp16 = add(x = input_221_cast_fp16, y = var_1173_cast_fp16)[name = string("input_233_cast_fp16")];
+            tensor<int32, [1]> input_235_axes_0 = const()[name = string("input_235_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203638080)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203640192)))];
+            tensor<fp16, [1, 188, 1024]> input_235_cast_fp16 = layer_norm(axes = input_235_axes_0, beta = encoder_module_layers_3_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_out_weight_to_fp16, x = input_233_cast_fp16)[name = string("input_235_cast_fp16")];
+            tensor<int32, [1]> input_237_axes_0 = const()[name = string("input_237_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203642304)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203644416)))];
+            tensor<fp16, [1, 188, 1024]> input_237_cast_fp16 = layer_norm(axes = input_237_axes_0, beta = encoder_module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203646528)))];
+            tensor<fp16, [4096]> encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212035200)))];
+            tensor<fp16, [1, 188, 4096]> linear_37_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16, x = input_237_cast_fp16)[name = string("linear_37_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_241_cast_fp16 = silu(x = linear_37_cast_fp16)[name = string("input_241_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212043456)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220432128)))];
+            tensor<fp16, [1, 188, 1024]> linear_38_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16, x = input_241_cast_fp16)[name = string("linear_38_cast_fp16")];
+            fp16 var_1203_to_fp16 = const()[name = string("op_1203_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1204_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_1203_to_fp16)[name = string("op_1204_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_247_cast_fp16 = add(x = input_235_cast_fp16, y = var_1204_cast_fp16)[name = string("input_247_cast_fp16")];
+            tensor<int32, [1]> query_9_axes_0 = const()[name = string("query_9_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220434240)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220436352)))];
+            tensor<fp16, [1, 188, 1024]> query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = encoder_module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_self_att_weight_to_fp16, x = input_247_cast_fp16)[name = string("query_9_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220438464)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222535680)))];
+            tensor<fp16, [1, 188, 1024]> linear_39_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_q_weight_to_fp16, x = query_9_cast_fp16)[name = string("linear_39_cast_fp16")];
+            tensor<int32, [4]> var_1221 = const()[name = string("op_1221"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_25_cast_fp16 = reshape(shape = var_1221, x = linear_39_cast_fp16)[name = string("q_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222537792)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224635008)))];
+            tensor<fp16, [1, 188, 1024]> linear_40_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_k_weight_to_fp16, x = query_9_cast_fp16)[name = string("linear_40_cast_fp16")];
+            tensor<int32, [4]> var_1226 = const()[name = string("op_1226"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_17_cast_fp16 = reshape(shape = var_1226, x = linear_40_cast_fp16)[name = string("k_17_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224637120)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226734336)))];
+            tensor<fp16, [1, 188, 1024]> linear_41_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_v_weight_to_fp16, x = query_9_cast_fp16)[name = string("linear_41_cast_fp16")];
+            tensor<int32, [4]> var_1231 = const()[name = string("op_1231"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_9_cast_fp16 = reshape(shape = var_1231, x = linear_41_cast_fp16)[name = string("v_9_cast_fp16")];
+            tensor<int32, [4]> value_13_perm_0 = const()[name = string("value_13_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226736448)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1243_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_module_layers_4_self_attn_pos_bias_u_to_fp16)[name = string("op_1243_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226738560)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1245_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_module_layers_4_self_attn_pos_bias_v_to_fp16)[name = string("op_1245_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_9_perm_0 = const()[name = string("q_with_bias_v_9_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_115_transpose_x_0 = const()[name = string("x_115_transpose_x_0"), val = bool(false)];
+            bool x_115_transpose_y_0 = const()[name = string("x_115_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_1247_to_fp16 = const()[name = string("op_1247_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226740672)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_1245_cast_fp16)[name = string("transpose_259")];
+            tensor<fp16, [1, 8, 188, 375]> x_115_cast_fp16 = matmul(transpose_x = x_115_transpose_x_0, transpose_y = x_115_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = var_1247_to_fp16)[name = string("x_115_cast_fp16")];
+            tensor<int32, [8]> x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_117_mode_0 = const()[name = string("x_117_mode_0"), val = string("constant")];
+            fp16 const_128_to_fp16 = const()[name = string("const_128_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_117_cast_fp16 = pad(constant_val = const_128_to_fp16, mode = x_117_mode_0, pad = x_117_pad_0, x = x_115_cast_fp16)[name = string("x_117_cast_fp16")];
+            tensor<int32, [4]> var_1255 = const()[name = string("op_1255"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_119_cast_fp16 = reshape(shape = var_1255, x = x_117_cast_fp16)[name = string("x_119_cast_fp16")];
+            tensor<int32, [4]> var_1259_begin_0 = const()[name = string("op_1259_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1259_end_0 = const()[name = string("op_1259_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1259_end_mask_0 = const()[name = string("op_1259_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1259_cast_fp16 = slice_by_index(begin = var_1259_begin_0, end = var_1259_end_0, end_mask = var_1259_end_mask_0, x = x_119_cast_fp16)[name = string("op_1259_cast_fp16")];
+            tensor<int32, [4]> var_1260 = const()[name = string("op_1260"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_17_cast_fp16 = reshape(shape = var_1260, x = var_1259_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<int32, [4]> transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_81 = transpose(perm = transpose_81_perm_0, x = k_17_cast_fp16)[name = string("transpose_257")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_80 = transpose(perm = transpose_80_perm_0, x = var_1243_cast_fp16)[name = string("transpose_258")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_9_cast_fp16 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_80, y = transpose_81)[name = string("matrix_ac_9_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_19_end_0 = const()[name = string("matrix_bd_19_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1269_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = string("op_1269_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<fp16, [1, 8, 188, 188]> _inversed_scores_17_cast_fp16 = mul(x = var_1269_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = string("_inversed_scores_17_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_19_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_15)[name = string("scores_19_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1275_cast_fp16 = softmax(axis = var_152, x = scores_19_cast_fp16)[name = string("op_1275_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_249_cast_fp16 = select(a = var_164_to_fp16, b = var_1275_cast_fp16, cond = mask_15)[name = string("input_249_cast_fp16")];
+            bool x_121_transpose_x_0 = const()[name = string("x_121_transpose_x_0"), val = bool(false)];
+            bool x_121_transpose_y_0 = const()[name = string("x_121_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_9_cast_fp16)[name = string("transpose_260")];
+            tensor<fp16, [1, 8, 188, 128]> x_121_cast_fp16 = matmul(transpose_x = x_121_transpose_x_0, transpose_y = x_121_transpose_y_0, x = input_249_cast_fp16, y = value_13_cast_fp16)[name = string("x_121_cast_fp16")];
+            tensor<int32, [4]> var_1279_perm_0 = const()[name = string("op_1279_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1280 = const()[name = string("op_1280"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1279_cast_fp16 = transpose(perm = var_1279_perm_0, x = x_121_cast_fp16)[name = string("transpose_256")];
+            tensor<fp16, [1, 188, 1024]> input_251_cast_fp16 = reshape(shape = var_1280, x = var_1279_cast_fp16)[name = string("input_251_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227508736)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229605952)))];
+            tensor<fp16, [1, 188, 1024]> linear_43_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_out_weight_to_fp16, x = input_251_cast_fp16)[name = string("linear_43_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_255_cast_fp16 = add(x = input_247_cast_fp16, y = linear_43_cast_fp16)[name = string("input_255_cast_fp16")];
+            tensor<int32, [1]> x_125_axes_0 = const()[name = string("x_125_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229608064)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229610176)))];
+            tensor<fp16, [1, 188, 1024]> x_125_cast_fp16 = layer_norm(axes = x_125_axes_0, beta = encoder_module_layers_4_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_conv_weight_to_fp16, x = input_255_cast_fp16)[name = string("x_125_cast_fp16")];
+            tensor<int32, [3]> input_257_perm_0 = const()[name = string("input_257_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_259_pad_type_0 = const()[name = string("input_259_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_259_strides_0 = const()[name = string("input_259_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_259_pad_0 = const()[name = string("input_259_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_259_dilations_0 = const()[name = string("input_259_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_259_groups_0 = const()[name = string("input_259_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_4_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_4_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229612288)))];
+            tensor<fp16, [2048]> encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233806656)))];
+            tensor<fp16, [1, 1024, 188]> input_257_cast_fp16 = transpose(perm = input_257_perm_0, x = x_125_cast_fp16)[name = string("transpose_255")];
+            tensor<fp16, [1, 2048, 188]> input_259_cast_fp16 = conv(bias = encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_4_conv_pointwise_conv1_weight_to_fp16, x = input_257_cast_fp16)[name = string("input_259_cast_fp16")];
+            int32 x_127_split_num_splits_0 = const()[name = string("x_127_split_num_splits_0"), val = int32(2)];
+            int32 x_127_split_axis_0 = const()[name = string("x_127_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_127_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_127_split_cast_fp16_1 = split(axis = x_127_split_axis_0, num_splits = x_127_split_num_splits_0, x = input_259_cast_fp16)[name = string("x_127_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_127_split_1_sigmoid_cast_fp16 = sigmoid(x = x_127_split_cast_fp16_1)[name = string("x_127_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_127_cast_fp16 = mul(x = x_127_split_cast_fp16_0, y = x_127_split_1_sigmoid_cast_fp16)[name = string("x_127_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_261_cast_fp16 = select(a = var_164_to_fp16, b = x_127_cast_fp16, cond = var_608)[name = string("input_261_cast_fp16")];
+            tensor<int32, [6]> input_263_pad_0 = const()[name = string("input_263_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_263_mode_0 = const()[name = string("input_263_mode_0"), val = string("constant")];
+            fp16 const_131_to_fp16 = const()[name = string("const_131_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_263_cast_fp16 = pad(constant_val = const_131_to_fp16, mode = input_263_mode_0, pad = input_263_pad_0, x = input_261_cast_fp16)[name = string("input_263_cast_fp16")];
+            string input_265_pad_type_0 = const()[name = string("input_265_pad_type_0"), val = string("valid")];
+            int32 input_265_groups_0 = const()[name = string("input_265_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_265_strides_0 = const()[name = string("input_265_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_265_pad_0 = const()[name = string("input_265_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_265_dilations_0 = const()[name = string("input_265_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_330_to_fp16 = const()[name = string("const_330_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233810816)))];
+            tensor<fp16, [1024]> const_331_to_fp16 = const()[name = string("const_331_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233829312)))];
+            tensor<fp16, [1, 1024, 188]> input_267_cast_fp16 = conv(bias = const_331_to_fp16, dilations = input_265_dilations_0, groups = input_265_groups_0, pad = input_265_pad_0, pad_type = input_265_pad_type_0, strides = input_265_strides_0, weight = const_330_to_fp16, x = input_263_cast_fp16)[name = string("input_267_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_269_cast_fp16 = silu(x = input_267_cast_fp16)[name = string("input_269_cast_fp16")];
+            string x_129_pad_type_0 = const()[name = string("x_129_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_129_strides_0 = const()[name = string("x_129_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_129_pad_0 = const()[name = string("x_129_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_129_dilations_0 = const()[name = string("x_129_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_129_groups_0 = const()[name = string("x_129_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_4_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_4_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233831424)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235928640)))];
+            tensor<fp16, [1, 1024, 188]> x_129_cast_fp16 = conv(bias = encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_4_conv_pointwise_conv2_weight_to_fp16, x = input_269_cast_fp16)[name = string("x_129_cast_fp16")];
+            tensor<int32, [3]> input_271_perm_0 = const()[name = string("input_271_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_271_cast_fp16 = transpose(perm = input_271_perm_0, x = x_129_cast_fp16)[name = string("transpose_254")];
+            tensor<fp16, [1, 188, 1024]> input_273_cast_fp16 = add(x = input_255_cast_fp16, y = input_271_cast_fp16)[name = string("input_273_cast_fp16")];
+            tensor<int32, [1]> input_275_axes_0 = const()[name = string("input_275_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235930752)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235932864)))];
+            tensor<fp16, [1, 188, 1024]> input_275_cast_fp16 = layer_norm(axes = input_275_axes_0, beta = encoder_module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235934976)))];
+            tensor<fp16, [4096]> encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244323648)))];
+            tensor<fp16, [1, 188, 4096]> linear_44_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16, x = input_275_cast_fp16)[name = string("linear_44_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_279_cast_fp16 = silu(x = linear_44_cast_fp16)[name = string("input_279_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244331904)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252720576)))];
+            tensor<fp16, [1, 188, 1024]> linear_45_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16, x = input_279_cast_fp16)[name = string("linear_45_cast_fp16")];
+            fp16 var_1346_to_fp16 = const()[name = string("op_1346_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1347_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1346_to_fp16)[name = string("op_1347_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_285_cast_fp16 = add(x = input_273_cast_fp16, y = var_1347_cast_fp16)[name = string("input_285_cast_fp16")];
+            tensor<int32, [1]> input_287_axes_0 = const()[name = string("input_287_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252722688)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252724800)))];
+            tensor<fp16, [1, 188, 1024]> input_287_cast_fp16 = layer_norm(axes = input_287_axes_0, beta = encoder_module_layers_4_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_out_weight_to_fp16, x = input_285_cast_fp16)[name = string("input_287_cast_fp16")];
+            tensor<int32, [1]> input_289_axes_0 = const()[name = string("input_289_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252726912)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252729024)))];
+            tensor<fp16, [1, 188, 1024]> input_289_cast_fp16 = layer_norm(axes = input_289_axes_0, beta = encoder_module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_287_cast_fp16)[name = string("input_289_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252731136)))];
+            tensor<fp16, [4096]> encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261119808)))];
+            tensor<fp16, [1, 188, 4096]> linear_46_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16, x = input_289_cast_fp16)[name = string("linear_46_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_293_cast_fp16 = silu(x = linear_46_cast_fp16)[name = string("input_293_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261128064)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269516736)))];
+            tensor<fp16, [1, 188, 1024]> linear_47_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16, x = input_293_cast_fp16)[name = string("linear_47_cast_fp16")];
+            fp16 var_1377_to_fp16 = const()[name = string("op_1377_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1378_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1377_to_fp16)[name = string("op_1378_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_299_cast_fp16 = add(x = input_287_cast_fp16, y = var_1378_cast_fp16)[name = string("input_299_cast_fp16")];
+            tensor<int32, [1]> query_11_axes_0 = const()[name = string("query_11_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269518848)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269520960)))];
+            tensor<fp16, [1, 188, 1024]> query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = encoder_module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_self_att_weight_to_fp16, x = input_299_cast_fp16)[name = string("query_11_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269523072)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271620288)))];
+            tensor<fp16, [1, 188, 1024]> linear_48_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_q_weight_to_fp16, x = query_11_cast_fp16)[name = string("linear_48_cast_fp16")];
+            tensor<int32, [4]> var_1395 = const()[name = string("op_1395"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_31_cast_fp16 = reshape(shape = var_1395, x = linear_48_cast_fp16)[name = string("q_31_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271622400)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273719616)))];
+            tensor<fp16, [1, 188, 1024]> linear_49_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_k_weight_to_fp16, x = query_11_cast_fp16)[name = string("linear_49_cast_fp16")];
+            tensor<int32, [4]> var_1400 = const()[name = string("op_1400"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_21_cast_fp16 = reshape(shape = var_1400, x = linear_49_cast_fp16)[name = string("k_21_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273721728)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275818944)))];
+            tensor<fp16, [1, 188, 1024]> linear_50_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_v_weight_to_fp16, x = query_11_cast_fp16)[name = string("linear_50_cast_fp16")];
+            tensor<int32, [4]> var_1405 = const()[name = string("op_1405"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_11_cast_fp16 = reshape(shape = var_1405, x = linear_50_cast_fp16)[name = string("v_11_cast_fp16")];
+            tensor<int32, [4]> value_15_perm_0 = const()[name = string("value_15_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275821056)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1417_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_module_layers_5_self_attn_pos_bias_u_to_fp16)[name = string("op_1417_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275823168)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1419_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_module_layers_5_self_attn_pos_bias_v_to_fp16)[name = string("op_1419_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_11_perm_0 = const()[name = string("q_with_bias_v_11_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            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<fp16, [1, 8, 128, 375]> var_1421_to_fp16 = const()[name = string("op_1421_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275825280)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1419_cast_fp16)[name = string("transpose_252")];
+            tensor<fp16, [1, 8, 188, 375]> 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 = var_1421_to_fp16)[name = string("x_137_cast_fp16")];
+            tensor<int32, [8]> x_139_pad_0 = const()[name = string("x_139_pad_0"), val = tensor<int32, [8]>([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_138_to_fp16 = const()[name = string("const_138_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_139_cast_fp16 = pad(constant_val = const_138_to_fp16, mode = x_139_mode_0, pad = x_139_pad_0, x = x_137_cast_fp16)[name = string("x_139_cast_fp16")];
+            tensor<int32, [4]> var_1429 = const()[name = string("op_1429"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_141_cast_fp16 = reshape(shape = var_1429, x = x_139_cast_fp16)[name = string("x_141_cast_fp16")];
+            tensor<int32, [4]> var_1433_begin_0 = const()[name = string("op_1433_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1433_end_0 = const()[name = string("op_1433_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1433_end_mask_0 = const()[name = string("op_1433_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1433_cast_fp16 = slice_by_index(begin = var_1433_begin_0, end = var_1433_end_0, end_mask = var_1433_end_mask_0, x = x_141_cast_fp16)[name = string("op_1433_cast_fp16")];
+            tensor<int32, [4]> var_1434 = const()[name = string("op_1434"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_21_cast_fp16 = reshape(shape = var_1434, x = var_1433_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<int32, [4]> transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_83 = transpose(perm = transpose_83_perm_0, x = k_21_cast_fp16)[name = string("transpose_250")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_82 = transpose(perm = transpose_82_perm_0, x = var_1417_cast_fp16)[name = string("transpose_251")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_11_cast_fp16 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_82, y = transpose_83)[name = string("matrix_ac_11_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_23_end_0 = const()[name = string("matrix_bd_23_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1443_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = string("op_1443_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<fp16, [1, 8, 188, 188]> _inversed_scores_21_cast_fp16 = mul(x = var_1443_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = string("_inversed_scores_21_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_23_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_15)[name = string("scores_23_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1449_cast_fp16 = softmax(axis = var_152, x = scores_23_cast_fp16)[name = string("op_1449_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_301_cast_fp16 = select(a = var_164_to_fp16, b = var_1449_cast_fp16, cond = mask_15)[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<fp16, [1, 8, 188, 128]> value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_11_cast_fp16)[name = string("transpose_253")];
+            tensor<fp16, [1, 8, 188, 128]> 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_15_cast_fp16)[name = string("x_143_cast_fp16")];
+            tensor<int32, [4]> var_1453_perm_0 = const()[name = string("op_1453_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1454 = const()[name = string("op_1454"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1453_cast_fp16 = transpose(perm = var_1453_perm_0, x = x_143_cast_fp16)[name = string("transpose_249")];
+            tensor<fp16, [1, 188, 1024]> input_303_cast_fp16 = reshape(shape = var_1454, x = var_1453_cast_fp16)[name = string("input_303_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276593344)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278690560)))];
+            tensor<fp16, [1, 188, 1024]> linear_52_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_out_weight_to_fp16, x = input_303_cast_fp16)[name = string("linear_52_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_307_cast_fp16 = add(x = input_299_cast_fp16, y = linear_52_cast_fp16)[name = string("input_307_cast_fp16")];
+            tensor<int32, [1]> x_147_axes_0 = const()[name = string("x_147_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278692672)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278694784)))];
+            tensor<fp16, [1, 188, 1024]> x_147_cast_fp16 = layer_norm(axes = x_147_axes_0, beta = encoder_module_layers_5_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_conv_weight_to_fp16, x = input_307_cast_fp16)[name = string("x_147_cast_fp16")];
+            tensor<int32, [3]> input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_311_pad_type_0 = const()[name = string("input_311_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_311_strides_0 = const()[name = string("input_311_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_311_pad_0 = const()[name = string("input_311_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_311_dilations_0 = const()[name = string("input_311_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_311_groups_0 = const()[name = string("input_311_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_5_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_5_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278696896)))];
+            tensor<fp16, [2048]> encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282891264)))];
+            tensor<fp16, [1, 1024, 188]> input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_147_cast_fp16)[name = string("transpose_248")];
+            tensor<fp16, [1, 2048, 188]> input_311_cast_fp16 = conv(bias = encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_5_conv_pointwise_conv1_weight_to_fp16, 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<fp16, [1, 1024, 188]> x_149_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> input_313_cast_fp16 = select(a = var_164_to_fp16, b = x_149_cast_fp16, cond = var_608)[name = string("input_313_cast_fp16")];
+            tensor<int32, [6]> input_315_pad_0 = const()[name = string("input_315_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_315_mode_0 = const()[name = string("input_315_mode_0"), val = string("constant")];
+            fp16 const_141_to_fp16 = const()[name = string("const_141_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_315_cast_fp16 = pad(constant_val = const_141_to_fp16, mode = input_315_mode_0, pad = input_315_pad_0, x = input_313_cast_fp16)[name = string("input_315_cast_fp16")];
+            string input_317_pad_type_0 = const()[name = string("input_317_pad_type_0"), val = string("valid")];
+            int32 input_317_groups_0 = const()[name = string("input_317_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_317_strides_0 = const()[name = string("input_317_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_317_pad_0 = const()[name = string("input_317_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_317_dilations_0 = const()[name = string("input_317_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_332_to_fp16 = const()[name = string("const_332_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282895424)))];
+            tensor<fp16, [1024]> const_333_to_fp16 = const()[name = string("const_333_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282913920)))];
+            tensor<fp16, [1, 1024, 188]> input_319_cast_fp16 = conv(bias = const_333_to_fp16, dilations = input_317_dilations_0, groups = input_317_groups_0, pad = input_317_pad_0, pad_type = input_317_pad_type_0, strides = input_317_strides_0, weight = const_332_to_fp16, x = input_315_cast_fp16)[name = string("input_319_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_321_cast_fp16 = silu(x = input_319_cast_fp16)[name = string("input_321_cast_fp16")];
+            string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_5_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_5_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282916032)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285013248)))];
+            tensor<fp16, [1, 1024, 188]> x_151_cast_fp16 = conv(bias = encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_5_conv_pointwise_conv2_weight_to_fp16, x = input_321_cast_fp16)[name = string("x_151_cast_fp16")];
+            tensor<int32, [3]> input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = x_151_cast_fp16)[name = string("transpose_247")];
+            tensor<fp16, [1, 188, 1024]> input_325_cast_fp16 = add(x = input_307_cast_fp16, y = input_323_cast_fp16)[name = string("input_325_cast_fp16")];
+            tensor<int32, [1]> input_327_axes_0 = const()[name = string("input_327_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285015360)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285017472)))];
+            tensor<fp16, [1, 188, 1024]> input_327_cast_fp16 = layer_norm(axes = input_327_axes_0, beta = encoder_module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_325_cast_fp16)[name = string("input_327_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285019584)))];
+            tensor<fp16, [4096]> encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293408256)))];
+            tensor<fp16, [1, 188, 4096]> linear_53_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16, x = input_327_cast_fp16)[name = string("linear_53_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_331_cast_fp16 = silu(x = linear_53_cast_fp16)[name = string("input_331_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293416512)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301805184)))];
+            tensor<fp16, [1, 188, 1024]> linear_54_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16, x = input_331_cast_fp16)[name = string("linear_54_cast_fp16")];
+            fp16 var_1520_to_fp16 = const()[name = string("op_1520_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1521_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1520_to_fp16)[name = string("op_1521_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_337_cast_fp16 = add(x = input_325_cast_fp16, y = var_1521_cast_fp16)[name = string("input_337_cast_fp16")];
+            tensor<int32, [1]> input_339_axes_0 = const()[name = string("input_339_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301807296)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301809408)))];
+            tensor<fp16, [1, 188, 1024]> input_339_cast_fp16 = layer_norm(axes = input_339_axes_0, beta = encoder_module_layers_5_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_out_weight_to_fp16, x = input_337_cast_fp16)[name = string("input_339_cast_fp16")];
+            tensor<int32, [1]> input_341_axes_0 = const()[name = string("input_341_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301811520)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301813632)))];
+            tensor<fp16, [1, 188, 1024]> input_341_cast_fp16 = layer_norm(axes = input_341_axes_0, beta = encoder_module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_339_cast_fp16)[name = string("input_341_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301815744)))];
+            tensor<fp16, [4096]> encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310204416)))];
+            tensor<fp16, [1, 188, 4096]> linear_55_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16, x = input_341_cast_fp16)[name = string("linear_55_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_345_cast_fp16 = silu(x = linear_55_cast_fp16)[name = string("input_345_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310212672)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318601344)))];
+            tensor<fp16, [1, 188, 1024]> linear_56_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16, x = input_345_cast_fp16)[name = string("linear_56_cast_fp16")];
+            fp16 var_1551_to_fp16 = const()[name = string("op_1551_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1552_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1551_to_fp16)[name = string("op_1552_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_351_cast_fp16 = add(x = input_339_cast_fp16, y = var_1552_cast_fp16)[name = string("input_351_cast_fp16")];
+            tensor<int32, [1]> query_13_axes_0 = const()[name = string("query_13_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318603456)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318605568)))];
+            tensor<fp16, [1, 188, 1024]> query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = encoder_module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_self_att_weight_to_fp16, x = input_351_cast_fp16)[name = string("query_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318607680)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320704896)))];
+            tensor<fp16, [1, 188, 1024]> linear_57_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_q_weight_to_fp16, x = query_13_cast_fp16)[name = string("linear_57_cast_fp16")];
+            tensor<int32, [4]> var_1569 = const()[name = string("op_1569"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_37_cast_fp16 = reshape(shape = var_1569, x = linear_57_cast_fp16)[name = string("q_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320707008)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322804224)))];
+            tensor<fp16, [1, 188, 1024]> linear_58_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_k_weight_to_fp16, x = query_13_cast_fp16)[name = string("linear_58_cast_fp16")];
+            tensor<int32, [4]> var_1574 = const()[name = string("op_1574"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_25_cast_fp16 = reshape(shape = var_1574, x = linear_58_cast_fp16)[name = string("k_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322806336)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324903552)))];
+            tensor<fp16, [1, 188, 1024]> linear_59_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_v_weight_to_fp16, x = query_13_cast_fp16)[name = string("linear_59_cast_fp16")];
+            tensor<int32, [4]> var_1579 = const()[name = string("op_1579"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_13_cast_fp16 = reshape(shape = var_1579, x = linear_59_cast_fp16)[name = string("v_13_cast_fp16")];
+            tensor<int32, [4]> value_17_perm_0 = const()[name = string("value_17_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324905664)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1591_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_module_layers_6_self_attn_pos_bias_u_to_fp16)[name = string("op_1591_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324907776)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1593_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_module_layers_6_self_attn_pos_bias_v_to_fp16)[name = string("op_1593_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_13_perm_0 = const()[name = string("q_with_bias_v_13_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_159_transpose_x_0 = const()[name = string("x_159_transpose_x_0"), val = bool(false)];
+            bool x_159_transpose_y_0 = const()[name = string("x_159_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_1595_to_fp16 = const()[name = string("op_1595_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324909888)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1593_cast_fp16)[name = string("transpose_245")];
+            tensor<fp16, [1, 8, 188, 375]> x_159_cast_fp16 = matmul(transpose_x = x_159_transpose_x_0, transpose_y = x_159_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = var_1595_to_fp16)[name = string("x_159_cast_fp16")];
+            tensor<int32, [8]> x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_161_mode_0 = const()[name = string("x_161_mode_0"), val = string("constant")];
+            fp16 const_148_to_fp16 = const()[name = string("const_148_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_161_cast_fp16 = pad(constant_val = const_148_to_fp16, mode = x_161_mode_0, pad = x_161_pad_0, x = x_159_cast_fp16)[name = string("x_161_cast_fp16")];
+            tensor<int32, [4]> var_1603 = const()[name = string("op_1603"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_163_cast_fp16 = reshape(shape = var_1603, x = x_161_cast_fp16)[name = string("x_163_cast_fp16")];
+            tensor<int32, [4]> var_1607_begin_0 = const()[name = string("op_1607_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1607_end_0 = const()[name = string("op_1607_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1607_end_mask_0 = const()[name = string("op_1607_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1607_cast_fp16 = slice_by_index(begin = var_1607_begin_0, end = var_1607_end_0, end_mask = var_1607_end_mask_0, x = x_163_cast_fp16)[name = string("op_1607_cast_fp16")];
+            tensor<int32, [4]> var_1608 = const()[name = string("op_1608"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_25_cast_fp16 = reshape(shape = var_1608, x = var_1607_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<int32, [4]> transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_85 = transpose(perm = transpose_85_perm_0, x = k_25_cast_fp16)[name = string("transpose_243")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_84 = transpose(perm = transpose_84_perm_0, x = var_1591_cast_fp16)[name = string("transpose_244")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_13_cast_fp16 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_84, y = transpose_85)[name = string("matrix_ac_13_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_27_end_0 = const()[name = string("matrix_bd_27_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1617_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = string("op_1617_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<fp16, [1, 8, 188, 188]> _inversed_scores_25_cast_fp16 = mul(x = var_1617_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = string("_inversed_scores_25_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_27_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_15)[name = string("scores_27_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1623_cast_fp16 = softmax(axis = var_152, x = scores_27_cast_fp16)[name = string("op_1623_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_353_cast_fp16 = select(a = var_164_to_fp16, b = var_1623_cast_fp16, cond = mask_15)[name = string("input_353_cast_fp16")];
+            bool x_165_transpose_x_0 = const()[name = string("x_165_transpose_x_0"), val = bool(false)];
+            bool x_165_transpose_y_0 = const()[name = string("x_165_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_13_cast_fp16)[name = string("transpose_246")];
+            tensor<fp16, [1, 8, 188, 128]> x_165_cast_fp16 = matmul(transpose_x = x_165_transpose_x_0, transpose_y = x_165_transpose_y_0, x = input_353_cast_fp16, y = value_17_cast_fp16)[name = string("x_165_cast_fp16")];
+            tensor<int32, [4]> var_1627_perm_0 = const()[name = string("op_1627_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1628 = const()[name = string("op_1628"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1627_cast_fp16 = transpose(perm = var_1627_perm_0, x = x_165_cast_fp16)[name = string("transpose_242")];
+            tensor<fp16, [1, 188, 1024]> input_355_cast_fp16 = reshape(shape = var_1628, x = var_1627_cast_fp16)[name = string("input_355_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325677952)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327775168)))];
+            tensor<fp16, [1, 188, 1024]> linear_61_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_out_weight_to_fp16, x = input_355_cast_fp16)[name = string("linear_61_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_359_cast_fp16 = add(x = input_351_cast_fp16, y = linear_61_cast_fp16)[name = string("input_359_cast_fp16")];
+            tensor<int32, [1]> x_169_axes_0 = const()[name = string("x_169_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327777280)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327779392)))];
+            tensor<fp16, [1, 188, 1024]> x_169_cast_fp16 = layer_norm(axes = x_169_axes_0, beta = encoder_module_layers_6_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_conv_weight_to_fp16, x = input_359_cast_fp16)[name = string("x_169_cast_fp16")];
+            tensor<int32, [3]> input_361_perm_0 = const()[name = string("input_361_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_363_pad_type_0 = const()[name = string("input_363_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_363_strides_0 = const()[name = string("input_363_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_363_pad_0 = const()[name = string("input_363_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_363_dilations_0 = const()[name = string("input_363_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_363_groups_0 = const()[name = string("input_363_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_6_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_6_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327781504)))];
+            tensor<fp16, [2048]> encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331975872)))];
+            tensor<fp16, [1, 1024, 188]> input_361_cast_fp16 = transpose(perm = input_361_perm_0, x = x_169_cast_fp16)[name = string("transpose_241")];
+            tensor<fp16, [1, 2048, 188]> input_363_cast_fp16 = conv(bias = encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_6_conv_pointwise_conv1_weight_to_fp16, x = input_361_cast_fp16)[name = string("input_363_cast_fp16")];
+            int32 x_171_split_num_splits_0 = const()[name = string("x_171_split_num_splits_0"), val = int32(2)];
+            int32 x_171_split_axis_0 = const()[name = string("x_171_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_171_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_171_split_cast_fp16_1 = split(axis = x_171_split_axis_0, num_splits = x_171_split_num_splits_0, x = input_363_cast_fp16)[name = string("x_171_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_171_split_1_sigmoid_cast_fp16 = sigmoid(x = x_171_split_cast_fp16_1)[name = string("x_171_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_171_cast_fp16 = mul(x = x_171_split_cast_fp16_0, y = x_171_split_1_sigmoid_cast_fp16)[name = string("x_171_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_365_cast_fp16 = select(a = var_164_to_fp16, b = x_171_cast_fp16, cond = var_608)[name = string("input_365_cast_fp16")];
+            tensor<int32, [6]> input_367_pad_0 = const()[name = string("input_367_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_367_mode_0 = const()[name = string("input_367_mode_0"), val = string("constant")];
+            fp16 const_151_to_fp16 = const()[name = string("const_151_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_367_cast_fp16 = pad(constant_val = const_151_to_fp16, mode = input_367_mode_0, pad = input_367_pad_0, x = input_365_cast_fp16)[name = string("input_367_cast_fp16")];
+            string input_369_pad_type_0 = const()[name = string("input_369_pad_type_0"), val = string("valid")];
+            int32 input_369_groups_0 = const()[name = string("input_369_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_369_strides_0 = const()[name = string("input_369_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_369_dilations_0 = const()[name = string("input_369_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_334_to_fp16 = const()[name = string("const_334_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331980032)))];
+            tensor<fp16, [1024]> const_335_to_fp16 = const()[name = string("const_335_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331998528)))];
+            tensor<fp16, [1, 1024, 188]> input_371_cast_fp16 = conv(bias = const_335_to_fp16, dilations = input_369_dilations_0, groups = input_369_groups_0, pad = input_369_pad_0, pad_type = input_369_pad_type_0, strides = input_369_strides_0, weight = const_334_to_fp16, x = input_367_cast_fp16)[name = string("input_371_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_373_cast_fp16 = silu(x = input_371_cast_fp16)[name = string("input_373_cast_fp16")];
+            string x_173_pad_type_0 = const()[name = string("x_173_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_173_strides_0 = const()[name = string("x_173_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_173_pad_0 = const()[name = string("x_173_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_173_dilations_0 = const()[name = string("x_173_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_173_groups_0 = const()[name = string("x_173_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332000640)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334097856)))];
+            tensor<fp16, [1, 1024, 188]> x_173_cast_fp16 = conv(bias = encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16, dilations = x_173_dilations_0, groups = x_173_groups_0, pad = x_173_pad_0, pad_type = x_173_pad_type_0, strides = x_173_strides_0, weight = encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16, x = input_373_cast_fp16)[name = string("x_173_cast_fp16")];
+            tensor<int32, [3]> input_375_perm_0 = const()[name = string("input_375_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_375_cast_fp16 = transpose(perm = input_375_perm_0, x = x_173_cast_fp16)[name = string("transpose_240")];
+            tensor<fp16, [1, 188, 1024]> input_377_cast_fp16 = add(x = input_359_cast_fp16, y = input_375_cast_fp16)[name = string("input_377_cast_fp16")];
+            tensor<int32, [1]> input_379_axes_0 = const()[name = string("input_379_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334099968)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334102080)))];
+            tensor<fp16, [1, 188, 1024]> input_379_cast_fp16 = layer_norm(axes = input_379_axes_0, beta = encoder_module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_377_cast_fp16)[name = string("input_379_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334104192)))];
+            tensor<fp16, [4096]> encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342492864)))];
+            tensor<fp16, [1, 188, 4096]> linear_62_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16, x = input_379_cast_fp16)[name = string("linear_62_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_383_cast_fp16 = silu(x = linear_62_cast_fp16)[name = string("input_383_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342501120)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350889792)))];
+            tensor<fp16, [1, 188, 1024]> linear_63_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16, x = input_383_cast_fp16)[name = string("linear_63_cast_fp16")];
+            fp16 var_1694_to_fp16 = const()[name = string("op_1694_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1695_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1694_to_fp16)[name = string("op_1695_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_389_cast_fp16 = add(x = input_377_cast_fp16, y = var_1695_cast_fp16)[name = string("input_389_cast_fp16")];
+            tensor<int32, [1]> input_391_axes_0 = const()[name = string("input_391_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350891904)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350894016)))];
+            tensor<fp16, [1, 188, 1024]> input_391_cast_fp16 = layer_norm(axes = input_391_axes_0, beta = encoder_module_layers_6_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_out_weight_to_fp16, x = input_389_cast_fp16)[name = string("input_391_cast_fp16")];
+            tensor<int32, [1]> input_393_axes_0 = const()[name = string("input_393_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350896128)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350898240)))];
+            tensor<fp16, [1, 188, 1024]> input_393_cast_fp16 = layer_norm(axes = input_393_axes_0, beta = encoder_module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_391_cast_fp16)[name = string("input_393_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350900352)))];
+            tensor<fp16, [4096]> encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(359289024)))];
+            tensor<fp16, [1, 188, 4096]> linear_64_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16, x = input_393_cast_fp16)[name = string("linear_64_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_397_cast_fp16 = silu(x = linear_64_cast_fp16)[name = string("input_397_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(359297280)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367685952)))];
+            tensor<fp16, [1, 188, 1024]> linear_65_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16, x = input_397_cast_fp16)[name = string("linear_65_cast_fp16")];
+            fp16 var_1725_to_fp16 = const()[name = string("op_1725_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1726_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1725_to_fp16)[name = string("op_1726_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_403_cast_fp16 = add(x = input_391_cast_fp16, y = var_1726_cast_fp16)[name = string("input_403_cast_fp16")];
+            tensor<int32, [1]> query_15_axes_0 = const()[name = string("query_15_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367688064)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367690176)))];
+            tensor<fp16, [1, 188, 1024]> query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = encoder_module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_self_att_weight_to_fp16, x = input_403_cast_fp16)[name = string("query_15_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367692288)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369789504)))];
+            tensor<fp16, [1, 188, 1024]> linear_66_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_q_weight_to_fp16, x = query_15_cast_fp16)[name = string("linear_66_cast_fp16")];
+            tensor<int32, [4]> var_1743 = const()[name = string("op_1743"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_43_cast_fp16 = reshape(shape = var_1743, x = linear_66_cast_fp16)[name = string("q_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369791616)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371888832)))];
+            tensor<fp16, [1, 188, 1024]> linear_67_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_k_weight_to_fp16, x = query_15_cast_fp16)[name = string("linear_67_cast_fp16")];
+            tensor<int32, [4]> var_1748 = const()[name = string("op_1748"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_29_cast_fp16 = reshape(shape = var_1748, x = linear_67_cast_fp16)[name = string("k_29_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371890944)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373988160)))];
+            tensor<fp16, [1, 188, 1024]> linear_68_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_v_weight_to_fp16, x = query_15_cast_fp16)[name = string("linear_68_cast_fp16")];
+            tensor<int32, [4]> var_1753 = const()[name = string("op_1753"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_15_cast_fp16 = reshape(shape = var_1753, x = linear_68_cast_fp16)[name = string("v_15_cast_fp16")];
+            tensor<int32, [4]> value_19_perm_0 = const()[name = string("value_19_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373990272)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1765_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_module_layers_7_self_attn_pos_bias_u_to_fp16)[name = string("op_1765_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373992384)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1767_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_module_layers_7_self_attn_pos_bias_v_to_fp16)[name = string("op_1767_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_15_perm_0 = const()[name = string("q_with_bias_v_15_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_181_transpose_x_0 = const()[name = string("x_181_transpose_x_0"), val = bool(false)];
+            bool x_181_transpose_y_0 = const()[name = string("x_181_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_1769_to_fp16 = const()[name = string("op_1769_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373994496)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1767_cast_fp16)[name = string("transpose_238")];
+            tensor<fp16, [1, 8, 188, 375]> x_181_cast_fp16 = matmul(transpose_x = x_181_transpose_x_0, transpose_y = x_181_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = var_1769_to_fp16)[name = string("x_181_cast_fp16")];
+            tensor<int32, [8]> x_183_pad_0 = const()[name = string("x_183_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_183_mode_0 = const()[name = string("x_183_mode_0"), val = string("constant")];
+            fp16 const_158_to_fp16 = const()[name = string("const_158_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_183_cast_fp16 = pad(constant_val = const_158_to_fp16, mode = x_183_mode_0, pad = x_183_pad_0, x = x_181_cast_fp16)[name = string("x_183_cast_fp16")];
+            tensor<int32, [4]> var_1777 = const()[name = string("op_1777"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_185_cast_fp16 = reshape(shape = var_1777, x = x_183_cast_fp16)[name = string("x_185_cast_fp16")];
+            tensor<int32, [4]> var_1781_begin_0 = const()[name = string("op_1781_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1781_end_0 = const()[name = string("op_1781_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1781_end_mask_0 = const()[name = string("op_1781_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1781_cast_fp16 = slice_by_index(begin = var_1781_begin_0, end = var_1781_end_0, end_mask = var_1781_end_mask_0, x = x_185_cast_fp16)[name = string("op_1781_cast_fp16")];
+            tensor<int32, [4]> var_1782 = const()[name = string("op_1782"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_29_cast_fp16 = reshape(shape = var_1782, x = var_1781_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<int32, [4]> transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_87 = transpose(perm = transpose_87_perm_0, x = k_29_cast_fp16)[name = string("transpose_236")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_86 = transpose(perm = transpose_86_perm_0, x = var_1765_cast_fp16)[name = string("transpose_237")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_15_cast_fp16 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_86, y = transpose_87)[name = string("matrix_ac_15_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_31_end_0 = const()[name = string("matrix_bd_31_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1791_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = string("op_1791_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<fp16, [1, 8, 188, 188]> _inversed_scores_29_cast_fp16 = mul(x = var_1791_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = string("_inversed_scores_29_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_31_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_15)[name = string("scores_31_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1797_cast_fp16 = softmax(axis = var_152, x = scores_31_cast_fp16)[name = string("op_1797_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_405_cast_fp16 = select(a = var_164_to_fp16, b = var_1797_cast_fp16, cond = mask_15)[name = string("input_405_cast_fp16")];
+            bool x_187_transpose_x_0 = const()[name = string("x_187_transpose_x_0"), val = bool(false)];
+            bool x_187_transpose_y_0 = const()[name = string("x_187_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_15_cast_fp16)[name = string("transpose_239")];
+            tensor<fp16, [1, 8, 188, 128]> x_187_cast_fp16 = matmul(transpose_x = x_187_transpose_x_0, transpose_y = x_187_transpose_y_0, x = input_405_cast_fp16, y = value_19_cast_fp16)[name = string("x_187_cast_fp16")];
+            tensor<int32, [4]> var_1801_perm_0 = const()[name = string("op_1801_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1802 = const()[name = string("op_1802"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1801_cast_fp16 = transpose(perm = var_1801_perm_0, x = x_187_cast_fp16)[name = string("transpose_235")];
+            tensor<fp16, [1, 188, 1024]> input_407_cast_fp16 = reshape(shape = var_1802, x = var_1801_cast_fp16)[name = string("input_407_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(374762560)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(376859776)))];
+            tensor<fp16, [1, 188, 1024]> linear_70_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_out_weight_to_fp16, x = input_407_cast_fp16)[name = string("linear_70_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_411_cast_fp16 = add(x = input_403_cast_fp16, y = linear_70_cast_fp16)[name = string("input_411_cast_fp16")];
+            tensor<int32, [1]> x_191_axes_0 = const()[name = string("x_191_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(376861888)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(376864000)))];
+            tensor<fp16, [1, 188, 1024]> x_191_cast_fp16 = layer_norm(axes = x_191_axes_0, beta = encoder_module_layers_7_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_conv_weight_to_fp16, x = input_411_cast_fp16)[name = string("x_191_cast_fp16")];
+            tensor<int32, [3]> input_413_perm_0 = const()[name = string("input_413_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_415_pad_type_0 = const()[name = string("input_415_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_415_strides_0 = const()[name = string("input_415_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_415_pad_0 = const()[name = string("input_415_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_415_dilations_0 = const()[name = string("input_415_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_415_groups_0 = const()[name = string("input_415_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_7_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_7_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(376866112)))];
+            tensor<fp16, [2048]> encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381060480)))];
+            tensor<fp16, [1, 1024, 188]> input_413_cast_fp16 = transpose(perm = input_413_perm_0, x = x_191_cast_fp16)[name = string("transpose_234")];
+            tensor<fp16, [1, 2048, 188]> input_415_cast_fp16 = conv(bias = encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_7_conv_pointwise_conv1_weight_to_fp16, x = input_413_cast_fp16)[name = string("input_415_cast_fp16")];
+            int32 x_193_split_num_splits_0 = const()[name = string("x_193_split_num_splits_0"), val = int32(2)];
+            int32 x_193_split_axis_0 = const()[name = string("x_193_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_193_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_193_split_cast_fp16_1 = split(axis = x_193_split_axis_0, num_splits = x_193_split_num_splits_0, x = input_415_cast_fp16)[name = string("x_193_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_193_split_1_sigmoid_cast_fp16 = sigmoid(x = x_193_split_cast_fp16_1)[name = string("x_193_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_193_cast_fp16 = mul(x = x_193_split_cast_fp16_0, y = x_193_split_1_sigmoid_cast_fp16)[name = string("x_193_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_417_cast_fp16 = select(a = var_164_to_fp16, b = x_193_cast_fp16, cond = var_608)[name = string("input_417_cast_fp16")];
+            tensor<int32, [6]> input_419_pad_0 = const()[name = string("input_419_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("constant")];
+            fp16 const_161_to_fp16 = const()[name = string("const_161_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_419_cast_fp16 = pad(constant_val = const_161_to_fp16, mode = input_419_mode_0, pad = input_419_pad_0, x = input_417_cast_fp16)[name = string("input_419_cast_fp16")];
+            string input_421_pad_type_0 = const()[name = string("input_421_pad_type_0"), val = string("valid")];
+            int32 input_421_groups_0 = const()[name = string("input_421_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_421_strides_0 = const()[name = string("input_421_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_421_pad_0 = const()[name = string("input_421_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_421_dilations_0 = const()[name = string("input_421_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_336_to_fp16 = const()[name = string("const_336_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381064640)))];
+            tensor<fp16, [1024]> const_337_to_fp16 = const()[name = string("const_337_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381083136)))];
+            tensor<fp16, [1, 1024, 188]> input_423_cast_fp16 = conv(bias = const_337_to_fp16, dilations = input_421_dilations_0, groups = input_421_groups_0, pad = input_421_pad_0, pad_type = input_421_pad_type_0, strides = input_421_strides_0, weight = const_336_to_fp16, x = input_419_cast_fp16)[name = string("input_423_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_425_cast_fp16 = silu(x = input_423_cast_fp16)[name = string("input_425_cast_fp16")];
+            string x_195_pad_type_0 = const()[name = string("x_195_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_195_strides_0 = const()[name = string("x_195_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_195_pad_0 = const()[name = string("x_195_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_195_dilations_0 = const()[name = string("x_195_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_195_groups_0 = const()[name = string("x_195_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381085248)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383182464)))];
+            tensor<fp16, [1, 1024, 188]> x_195_cast_fp16 = conv(bias = encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16, dilations = x_195_dilations_0, groups = x_195_groups_0, pad = x_195_pad_0, pad_type = x_195_pad_type_0, strides = x_195_strides_0, weight = encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16, x = input_425_cast_fp16)[name = string("x_195_cast_fp16")];
+            tensor<int32, [3]> input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_195_cast_fp16)[name = string("transpose_233")];
+            tensor<fp16, [1, 188, 1024]> input_429_cast_fp16 = add(x = input_411_cast_fp16, y = input_427_cast_fp16)[name = string("input_429_cast_fp16")];
+            tensor<int32, [1]> input_431_axes_0 = const()[name = string("input_431_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383184576)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383186688)))];
+            tensor<fp16, [1, 188, 1024]> input_431_cast_fp16 = layer_norm(axes = input_431_axes_0, beta = encoder_module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_429_cast_fp16)[name = string("input_431_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383188800)))];
+            tensor<fp16, [4096]> encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391577472)))];
+            tensor<fp16, [1, 188, 4096]> linear_71_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16, x = input_431_cast_fp16)[name = string("linear_71_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_435_cast_fp16 = silu(x = linear_71_cast_fp16)[name = string("input_435_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(391585728)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399974400)))];
+            tensor<fp16, [1, 188, 1024]> linear_72_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16, x = input_435_cast_fp16)[name = string("linear_72_cast_fp16")];
+            fp16 var_1868_to_fp16 = const()[name = string("op_1868_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1869_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1868_to_fp16)[name = string("op_1869_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_441_cast_fp16 = add(x = input_429_cast_fp16, y = var_1869_cast_fp16)[name = string("input_441_cast_fp16")];
+            tensor<int32, [1]> input_443_axes_0 = const()[name = string("input_443_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399976512)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399978624)))];
+            tensor<fp16, [1, 188, 1024]> input_443_cast_fp16 = layer_norm(axes = input_443_axes_0, beta = encoder_module_layers_7_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_out_weight_to_fp16, x = input_441_cast_fp16)[name = string("input_443_cast_fp16")];
+            tensor<int32, [1]> input_445_axes_0 = const()[name = string("input_445_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399980736)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399982848)))];
+            tensor<fp16, [1, 188, 1024]> input_445_cast_fp16 = layer_norm(axes = input_445_axes_0, beta = encoder_module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_443_cast_fp16)[name = string("input_445_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399984960)))];
+            tensor<fp16, [4096]> encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408373632)))];
+            tensor<fp16, [1, 188, 4096]> linear_73_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16, x = input_445_cast_fp16)[name = string("linear_73_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_449_cast_fp16 = silu(x = linear_73_cast_fp16)[name = string("input_449_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408381888)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416770560)))];
+            tensor<fp16, [1, 188, 1024]> linear_74_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16, x = input_449_cast_fp16)[name = string("linear_74_cast_fp16")];
+            fp16 var_1899_to_fp16 = const()[name = string("op_1899_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1900_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1899_to_fp16)[name = string("op_1900_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_455_cast_fp16 = add(x = input_443_cast_fp16, y = var_1900_cast_fp16)[name = string("input_455_cast_fp16")];
+            tensor<int32, [1]> query_17_axes_0 = const()[name = string("query_17_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416772672)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416774784)))];
+            tensor<fp16, [1, 188, 1024]> query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = encoder_module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_self_att_weight_to_fp16, x = input_455_cast_fp16)[name = string("query_17_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416776896)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418874112)))];
+            tensor<fp16, [1, 188, 1024]> linear_75_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_q_weight_to_fp16, x = query_17_cast_fp16)[name = string("linear_75_cast_fp16")];
+            tensor<int32, [4]> var_1917 = const()[name = string("op_1917"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_49_cast_fp16 = reshape(shape = var_1917, x = linear_75_cast_fp16)[name = string("q_49_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418876224)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420973440)))];
+            tensor<fp16, [1, 188, 1024]> linear_76_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_k_weight_to_fp16, x = query_17_cast_fp16)[name = string("linear_76_cast_fp16")];
+            tensor<int32, [4]> var_1922 = const()[name = string("op_1922"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_33_cast_fp16 = reshape(shape = var_1922, x = linear_76_cast_fp16)[name = string("k_33_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420975552)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423072768)))];
+            tensor<fp16, [1, 188, 1024]> linear_77_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_v_weight_to_fp16, x = query_17_cast_fp16)[name = string("linear_77_cast_fp16")];
+            tensor<int32, [4]> var_1927 = const()[name = string("op_1927"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_17_cast_fp16 = reshape(shape = var_1927, x = linear_77_cast_fp16)[name = string("v_17_cast_fp16")];
+            tensor<int32, [4]> value_21_perm_0 = const()[name = string("value_21_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423074880)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1939_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_module_layers_8_self_attn_pos_bias_u_to_fp16)[name = string("op_1939_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423076992)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1941_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_module_layers_8_self_attn_pos_bias_v_to_fp16)[name = string("op_1941_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_17_perm_0 = const()[name = string("q_with_bias_v_17_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_203_transpose_x_0 = const()[name = string("x_203_transpose_x_0"), val = bool(false)];
+            bool x_203_transpose_y_0 = const()[name = string("x_203_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_1943_to_fp16 = const()[name = string("op_1943_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423079104)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1941_cast_fp16)[name = string("transpose_231")];
+            tensor<fp16, [1, 8, 188, 375]> x_203_cast_fp16 = matmul(transpose_x = x_203_transpose_x_0, transpose_y = x_203_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = var_1943_to_fp16)[name = string("x_203_cast_fp16")];
+            tensor<int32, [8]> x_205_pad_0 = const()[name = string("x_205_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_205_mode_0 = const()[name = string("x_205_mode_0"), val = string("constant")];
+            fp16 const_168_to_fp16 = const()[name = string("const_168_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_205_cast_fp16 = pad(constant_val = const_168_to_fp16, mode = x_205_mode_0, pad = x_205_pad_0, x = x_203_cast_fp16)[name = string("x_205_cast_fp16")];
+            tensor<int32, [4]> var_1951 = const()[name = string("op_1951"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_207_cast_fp16 = reshape(shape = var_1951, x = x_205_cast_fp16)[name = string("x_207_cast_fp16")];
+            tensor<int32, [4]> var_1955_begin_0 = const()[name = string("op_1955_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1955_end_0 = const()[name = string("op_1955_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1955_end_mask_0 = const()[name = string("op_1955_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1955_cast_fp16 = slice_by_index(begin = var_1955_begin_0, end = var_1955_end_0, end_mask = var_1955_end_mask_0, x = x_207_cast_fp16)[name = string("op_1955_cast_fp16")];
+            tensor<int32, [4]> var_1956 = const()[name = string("op_1956"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_33_cast_fp16 = reshape(shape = var_1956, x = var_1955_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<int32, [4]> transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_89 = transpose(perm = transpose_89_perm_0, x = k_33_cast_fp16)[name = string("transpose_229")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_88 = transpose(perm = transpose_88_perm_0, x = var_1939_cast_fp16)[name = string("transpose_230")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_17_cast_fp16 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_88, y = transpose_89)[name = string("matrix_ac_17_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_35_end_0 = const()[name = string("matrix_bd_35_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1965_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = string("op_1965_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<fp16, [1, 8, 188, 188]> _inversed_scores_33_cast_fp16 = mul(x = var_1965_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = string("_inversed_scores_33_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_35_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_15)[name = string("scores_35_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1971_cast_fp16 = softmax(axis = var_152, x = scores_35_cast_fp16)[name = string("op_1971_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_457_cast_fp16 = select(a = var_164_to_fp16, b = var_1971_cast_fp16, cond = mask_15)[name = string("input_457_cast_fp16")];
+            bool x_209_transpose_x_0 = const()[name = string("x_209_transpose_x_0"), val = bool(false)];
+            bool x_209_transpose_y_0 = const()[name = string("x_209_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_17_cast_fp16)[name = string("transpose_232")];
+            tensor<fp16, [1, 8, 188, 128]> x_209_cast_fp16 = matmul(transpose_x = x_209_transpose_x_0, transpose_y = x_209_transpose_y_0, x = input_457_cast_fp16, y = value_21_cast_fp16)[name = string("x_209_cast_fp16")];
+            tensor<int32, [4]> var_1975_perm_0 = const()[name = string("op_1975_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1976 = const()[name = string("op_1976"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1975_cast_fp16 = transpose(perm = var_1975_perm_0, x = x_209_cast_fp16)[name = string("transpose_228")];
+            tensor<fp16, [1, 188, 1024]> input_459_cast_fp16 = reshape(shape = var_1976, x = var_1975_cast_fp16)[name = string("input_459_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(423847168)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(425944384)))];
+            tensor<fp16, [1, 188, 1024]> linear_79_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_out_weight_to_fp16, x = input_459_cast_fp16)[name = string("linear_79_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_463_cast_fp16 = add(x = input_455_cast_fp16, y = linear_79_cast_fp16)[name = string("input_463_cast_fp16")];
+            tensor<int32, [1]> x_213_axes_0 = const()[name = string("x_213_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(425946496)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(425948608)))];
+            tensor<fp16, [1, 188, 1024]> x_213_cast_fp16 = layer_norm(axes = x_213_axes_0, beta = encoder_module_layers_8_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_conv_weight_to_fp16, x = input_463_cast_fp16)[name = string("x_213_cast_fp16")];
+            tensor<int32, [3]> input_465_perm_0 = const()[name = string("input_465_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_467_pad_type_0 = const()[name = string("input_467_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_467_strides_0 = const()[name = string("input_467_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_467_pad_0 = const()[name = string("input_467_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_467_dilations_0 = const()[name = string("input_467_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_467_groups_0 = const()[name = string("input_467_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_8_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_8_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(425950720)))];
+            tensor<fp16, [2048]> encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(430145088)))];
+            tensor<fp16, [1, 1024, 188]> input_465_cast_fp16 = transpose(perm = input_465_perm_0, x = x_213_cast_fp16)[name = string("transpose_227")];
+            tensor<fp16, [1, 2048, 188]> input_467_cast_fp16 = conv(bias = encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_8_conv_pointwise_conv1_weight_to_fp16, x = input_465_cast_fp16)[name = string("input_467_cast_fp16")];
+            int32 x_215_split_num_splits_0 = const()[name = string("x_215_split_num_splits_0"), val = int32(2)];
+            int32 x_215_split_axis_0 = const()[name = string("x_215_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_215_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_215_split_cast_fp16_1 = split(axis = x_215_split_axis_0, num_splits = x_215_split_num_splits_0, x = input_467_cast_fp16)[name = string("x_215_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_215_split_1_sigmoid_cast_fp16 = sigmoid(x = x_215_split_cast_fp16_1)[name = string("x_215_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_215_cast_fp16 = mul(x = x_215_split_cast_fp16_0, y = x_215_split_1_sigmoid_cast_fp16)[name = string("x_215_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_469_cast_fp16 = select(a = var_164_to_fp16, b = x_215_cast_fp16, cond = var_608)[name = string("input_469_cast_fp16")];
+            tensor<int32, [6]> input_471_pad_0 = const()[name = string("input_471_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_471_mode_0 = const()[name = string("input_471_mode_0"), val = string("constant")];
+            fp16 const_171_to_fp16 = const()[name = string("const_171_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_471_cast_fp16 = pad(constant_val = const_171_to_fp16, mode = input_471_mode_0, pad = input_471_pad_0, x = input_469_cast_fp16)[name = string("input_471_cast_fp16")];
+            string input_473_pad_type_0 = const()[name = string("input_473_pad_type_0"), val = string("valid")];
+            int32 input_473_groups_0 = const()[name = string("input_473_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_473_strides_0 = const()[name = string("input_473_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_473_pad_0 = const()[name = string("input_473_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_473_dilations_0 = const()[name = string("input_473_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_338_to_fp16 = const()[name = string("const_338_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(430149248)))];
+            tensor<fp16, [1024]> const_339_to_fp16 = const()[name = string("const_339_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(430167744)))];
+            tensor<fp16, [1, 1024, 188]> input_475_cast_fp16 = conv(bias = const_339_to_fp16, dilations = input_473_dilations_0, groups = input_473_groups_0, pad = input_473_pad_0, pad_type = input_473_pad_type_0, strides = input_473_strides_0, weight = const_338_to_fp16, x = input_471_cast_fp16)[name = string("input_475_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_477_cast_fp16 = silu(x = input_475_cast_fp16)[name = string("input_477_cast_fp16")];
+            string x_217_pad_type_0 = const()[name = string("x_217_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_217_strides_0 = const()[name = string("x_217_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_217_pad_0 = const()[name = string("x_217_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_217_dilations_0 = const()[name = string("x_217_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_217_groups_0 = const()[name = string("x_217_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(430169856)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432267072)))];
+            tensor<fp16, [1, 1024, 188]> x_217_cast_fp16 = conv(bias = encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16, dilations = x_217_dilations_0, groups = x_217_groups_0, pad = x_217_pad_0, pad_type = x_217_pad_type_0, strides = x_217_strides_0, weight = encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16, x = input_477_cast_fp16)[name = string("x_217_cast_fp16")];
+            tensor<int32, [3]> input_479_perm_0 = const()[name = string("input_479_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_479_cast_fp16 = transpose(perm = input_479_perm_0, x = x_217_cast_fp16)[name = string("transpose_226")];
+            tensor<fp16, [1, 188, 1024]> input_481_cast_fp16 = add(x = input_463_cast_fp16, y = input_479_cast_fp16)[name = string("input_481_cast_fp16")];
+            tensor<int32, [1]> input_483_axes_0 = const()[name = string("input_483_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432269184)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432271296)))];
+            tensor<fp16, [1, 188, 1024]> input_483_cast_fp16 = layer_norm(axes = input_483_axes_0, beta = encoder_module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_481_cast_fp16)[name = string("input_483_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432273408)))];
+            tensor<fp16, [4096]> encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440662080)))];
+            tensor<fp16, [1, 188, 4096]> linear_80_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16, x = input_483_cast_fp16)[name = string("linear_80_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_487_cast_fp16 = silu(x = linear_80_cast_fp16)[name = string("input_487_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(440670336)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(449059008)))];
+            tensor<fp16, [1, 188, 1024]> linear_81_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16, x = input_487_cast_fp16)[name = string("linear_81_cast_fp16")];
+            fp16 var_2042_to_fp16 = const()[name = string("op_2042_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2043_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_2042_to_fp16)[name = string("op_2043_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_493_cast_fp16 = add(x = input_481_cast_fp16, y = var_2043_cast_fp16)[name = string("input_493_cast_fp16")];
+            tensor<int32, [1]> input_495_axes_0 = const()[name = string("input_495_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(449061120)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(449063232)))];
+            tensor<fp16, [1, 188, 1024]> input_495_cast_fp16 = layer_norm(axes = input_495_axes_0, beta = encoder_module_layers_8_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_out_weight_to_fp16, x = input_493_cast_fp16)[name = string("input_495_cast_fp16")];
+            tensor<int32, [1]> input_497_axes_0 = const()[name = string("input_497_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(449065344)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(449067456)))];
+            tensor<fp16, [1, 188, 1024]> input_497_cast_fp16 = layer_norm(axes = input_497_axes_0, beta = encoder_module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_495_cast_fp16)[name = string("input_497_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(449069568)))];
+            tensor<fp16, [4096]> encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457458240)))];
+            tensor<fp16, [1, 188, 4096]> linear_82_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16, x = input_497_cast_fp16)[name = string("linear_82_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_501_cast_fp16 = silu(x = linear_82_cast_fp16)[name = string("input_501_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457466496)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465855168)))];
+            tensor<fp16, [1, 188, 1024]> linear_83_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16, x = input_501_cast_fp16)[name = string("linear_83_cast_fp16")];
+            fp16 var_2073_to_fp16 = const()[name = string("op_2073_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2074_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_2073_to_fp16)[name = string("op_2074_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_507_cast_fp16 = add(x = input_495_cast_fp16, y = var_2074_cast_fp16)[name = string("input_507_cast_fp16")];
+            tensor<int32, [1]> query_19_axes_0 = const()[name = string("query_19_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465857280)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465859392)))];
+            tensor<fp16, [1, 188, 1024]> query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = encoder_module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_self_att_weight_to_fp16, x = input_507_cast_fp16)[name = string("query_19_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(465861504)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467958720)))];
+            tensor<fp16, [1, 188, 1024]> linear_84_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_q_weight_to_fp16, x = query_19_cast_fp16)[name = string("linear_84_cast_fp16")];
+            tensor<int32, [4]> var_2091 = const()[name = string("op_2091"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_55_cast_fp16 = reshape(shape = var_2091, x = linear_84_cast_fp16)[name = string("q_55_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467960832)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(470058048)))];
+            tensor<fp16, [1, 188, 1024]> linear_85_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_k_weight_to_fp16, x = query_19_cast_fp16)[name = string("linear_85_cast_fp16")];
+            tensor<int32, [4]> var_2096 = const()[name = string("op_2096"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_37_cast_fp16 = reshape(shape = var_2096, x = linear_85_cast_fp16)[name = string("k_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(470060160)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472157376)))];
+            tensor<fp16, [1, 188, 1024]> linear_86_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_v_weight_to_fp16, x = query_19_cast_fp16)[name = string("linear_86_cast_fp16")];
+            tensor<int32, [4]> var_2101 = const()[name = string("op_2101"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_19_cast_fp16 = reshape(shape = var_2101, x = linear_86_cast_fp16)[name = string("v_19_cast_fp16")];
+            tensor<int32, [4]> value_23_perm_0 = const()[name = string("value_23_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472159488)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2113_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_module_layers_9_self_attn_pos_bias_u_to_fp16)[name = string("op_2113_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472161600)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2115_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_module_layers_9_self_attn_pos_bias_v_to_fp16)[name = string("op_2115_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_19_perm_0 = const()[name = string("q_with_bias_v_19_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_225_transpose_x_0 = const()[name = string("x_225_transpose_x_0"), val = bool(false)];
+            bool x_225_transpose_y_0 = const()[name = string("x_225_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_2117_to_fp16 = const()[name = string("op_2117_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472163712)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_2115_cast_fp16)[name = string("transpose_224")];
+            tensor<fp16, [1, 8, 188, 375]> x_225_cast_fp16 = matmul(transpose_x = x_225_transpose_x_0, transpose_y = x_225_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = var_2117_to_fp16)[name = string("x_225_cast_fp16")];
+            tensor<int32, [8]> x_227_pad_0 = const()[name = string("x_227_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_227_mode_0 = const()[name = string("x_227_mode_0"), val = string("constant")];
+            fp16 const_178_to_fp16 = const()[name = string("const_178_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_227_cast_fp16 = pad(constant_val = const_178_to_fp16, mode = x_227_mode_0, pad = x_227_pad_0, x = x_225_cast_fp16)[name = string("x_227_cast_fp16")];
+            tensor<int32, [4]> var_2125 = const()[name = string("op_2125"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_229_cast_fp16 = reshape(shape = var_2125, x = x_227_cast_fp16)[name = string("x_229_cast_fp16")];
+            tensor<int32, [4]> var_2129_begin_0 = const()[name = string("op_2129_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2129_end_0 = const()[name = string("op_2129_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2129_end_mask_0 = const()[name = string("op_2129_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2129_cast_fp16 = slice_by_index(begin = var_2129_begin_0, end = var_2129_end_0, end_mask = var_2129_end_mask_0, x = x_229_cast_fp16)[name = string("op_2129_cast_fp16")];
+            tensor<int32, [4]> var_2130 = const()[name = string("op_2130"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_37_cast_fp16 = reshape(shape = var_2130, x = var_2129_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<int32, [4]> transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_91 = transpose(perm = transpose_91_perm_0, x = k_37_cast_fp16)[name = string("transpose_222")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_90 = transpose(perm = transpose_90_perm_0, x = var_2113_cast_fp16)[name = string("transpose_223")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_19_cast_fp16 = matmul(transpose_x = matrix_ac_19_transpose_x_0, transpose_y = matrix_ac_19_transpose_y_0, x = transpose_90, y = transpose_91)[name = string("matrix_ac_19_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_39_begin_0 = const()[name = string("matrix_bd_39_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_39_end_0 = const()[name = string("matrix_bd_39_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_39_end_mask_0 = const()[name = string("matrix_bd_39_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2139_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = string("op_2139_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<fp16, [1, 8, 188, 188]> _inversed_scores_37_cast_fp16 = mul(x = var_2139_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = string("_inversed_scores_37_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_39_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_15)[name = string("scores_39_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2145_cast_fp16 = softmax(axis = var_152, x = scores_39_cast_fp16)[name = string("op_2145_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_509_cast_fp16 = select(a = var_164_to_fp16, b = var_2145_cast_fp16, cond = mask_15)[name = string("input_509_cast_fp16")];
+            bool x_231_transpose_x_0 = const()[name = string("x_231_transpose_x_0"), val = bool(false)];
+            bool x_231_transpose_y_0 = const()[name = string("x_231_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_19_cast_fp16)[name = string("transpose_225")];
+            tensor<fp16, [1, 8, 188, 128]> x_231_cast_fp16 = matmul(transpose_x = x_231_transpose_x_0, transpose_y = x_231_transpose_y_0, x = input_509_cast_fp16, y = value_23_cast_fp16)[name = string("x_231_cast_fp16")];
+            tensor<int32, [4]> var_2149_perm_0 = const()[name = string("op_2149_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2150 = const()[name = string("op_2150"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2149_cast_fp16 = transpose(perm = var_2149_perm_0, x = x_231_cast_fp16)[name = string("transpose_221")];
+            tensor<fp16, [1, 188, 1024]> input_511_cast_fp16 = reshape(shape = var_2150, x = var_2149_cast_fp16)[name = string("input_511_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472931776)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(475028992)))];
+            tensor<fp16, [1, 188, 1024]> linear_88_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_out_weight_to_fp16, x = input_511_cast_fp16)[name = string("linear_88_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_515_cast_fp16 = add(x = input_507_cast_fp16, y = linear_88_cast_fp16)[name = string("input_515_cast_fp16")];
+            tensor<int32, [1]> x_235_axes_0 = const()[name = string("x_235_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(475031104)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(475033216)))];
+            tensor<fp16, [1, 188, 1024]> x_235_cast_fp16 = layer_norm(axes = x_235_axes_0, beta = encoder_module_layers_9_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_conv_weight_to_fp16, x = input_515_cast_fp16)[name = string("x_235_cast_fp16")];
+            tensor<int32, [3]> input_517_perm_0 = const()[name = string("input_517_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_519_pad_type_0 = const()[name = string("input_519_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_519_strides_0 = const()[name = string("input_519_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_519_pad_0 = const()[name = string("input_519_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_519_dilations_0 = const()[name = string("input_519_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_519_groups_0 = const()[name = string("input_519_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_9_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_9_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(475035328)))];
+            tensor<fp16, [2048]> encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479229696)))];
+            tensor<fp16, [1, 1024, 188]> input_517_cast_fp16 = transpose(perm = input_517_perm_0, x = x_235_cast_fp16)[name = string("transpose_220")];
+            tensor<fp16, [1, 2048, 188]> input_519_cast_fp16 = conv(bias = encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_9_conv_pointwise_conv1_weight_to_fp16, x = input_517_cast_fp16)[name = string("input_519_cast_fp16")];
+            int32 x_237_split_num_splits_0 = const()[name = string("x_237_split_num_splits_0"), val = int32(2)];
+            int32 x_237_split_axis_0 = const()[name = string("x_237_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_237_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_237_split_cast_fp16_1 = split(axis = x_237_split_axis_0, num_splits = x_237_split_num_splits_0, x = input_519_cast_fp16)[name = string("x_237_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_237_split_1_sigmoid_cast_fp16 = sigmoid(x = x_237_split_cast_fp16_1)[name = string("x_237_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_237_cast_fp16 = mul(x = x_237_split_cast_fp16_0, y = x_237_split_1_sigmoid_cast_fp16)[name = string("x_237_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_521_cast_fp16 = select(a = var_164_to_fp16, b = x_237_cast_fp16, cond = var_608)[name = string("input_521_cast_fp16")];
+            tensor<int32, [6]> input_523_pad_0 = const()[name = string("input_523_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_523_mode_0 = const()[name = string("input_523_mode_0"), val = string("constant")];
+            fp16 const_181_to_fp16 = const()[name = string("const_181_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_523_cast_fp16 = pad(constant_val = const_181_to_fp16, mode = input_523_mode_0, pad = input_523_pad_0, x = input_521_cast_fp16)[name = string("input_523_cast_fp16")];
+            string input_525_pad_type_0 = const()[name = string("input_525_pad_type_0"), val = string("valid")];
+            int32 input_525_groups_0 = const()[name = string("input_525_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_525_strides_0 = const()[name = string("input_525_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_525_pad_0 = const()[name = string("input_525_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_525_dilations_0 = const()[name = string("input_525_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_340_to_fp16 = const()[name = string("const_340_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479233856)))];
+            tensor<fp16, [1024]> const_341_to_fp16 = const()[name = string("const_341_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479252352)))];
+            tensor<fp16, [1, 1024, 188]> input_527_cast_fp16 = conv(bias = const_341_to_fp16, dilations = input_525_dilations_0, groups = input_525_groups_0, pad = input_525_pad_0, pad_type = input_525_pad_type_0, strides = input_525_strides_0, weight = const_340_to_fp16, x = input_523_cast_fp16)[name = string("input_527_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_529_cast_fp16 = silu(x = input_527_cast_fp16)[name = string("input_529_cast_fp16")];
+            string x_239_pad_type_0 = const()[name = string("x_239_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_239_strides_0 = const()[name = string("x_239_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_239_pad_0 = const()[name = string("x_239_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_239_dilations_0 = const()[name = string("x_239_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_239_groups_0 = const()[name = string("x_239_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479254464)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481351680)))];
+            tensor<fp16, [1, 1024, 188]> x_239_cast_fp16 = conv(bias = encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16, dilations = x_239_dilations_0, groups = x_239_groups_0, pad = x_239_pad_0, pad_type = x_239_pad_type_0, strides = x_239_strides_0, weight = encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16, x = input_529_cast_fp16)[name = string("x_239_cast_fp16")];
+            tensor<int32, [3]> input_531_perm_0 = const()[name = string("input_531_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_531_cast_fp16 = transpose(perm = input_531_perm_0, x = x_239_cast_fp16)[name = string("transpose_219")];
+            tensor<fp16, [1, 188, 1024]> input_533_cast_fp16 = add(x = input_515_cast_fp16, y = input_531_cast_fp16)[name = string("input_533_cast_fp16")];
+            tensor<int32, [1]> input_535_axes_0 = const()[name = string("input_535_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481353792)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481355904)))];
+            tensor<fp16, [1, 188, 1024]> input_535_cast_fp16 = layer_norm(axes = input_535_axes_0, beta = encoder_module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_533_cast_fp16)[name = string("input_535_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481358016)))];
+            tensor<fp16, [4096]> encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(489746688)))];
+            tensor<fp16, [1, 188, 4096]> linear_89_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16, x = input_535_cast_fp16)[name = string("linear_89_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_539_cast_fp16 = silu(x = linear_89_cast_fp16)[name = string("input_539_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(489754944)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(498143616)))];
+            tensor<fp16, [1, 188, 1024]> linear_90_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16, x = input_539_cast_fp16)[name = string("linear_90_cast_fp16")];
+            fp16 var_2216_to_fp16 = const()[name = string("op_2216_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2217_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_2216_to_fp16)[name = string("op_2217_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_545_cast_fp16 = add(x = input_533_cast_fp16, y = var_2217_cast_fp16)[name = string("input_545_cast_fp16")];
+            tensor<int32, [1]> input_547_axes_0 = const()[name = string("input_547_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(498145728)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(498147840)))];
+            tensor<fp16, [1, 188, 1024]> input_547_cast_fp16 = layer_norm(axes = input_547_axes_0, beta = encoder_module_layers_9_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_out_weight_to_fp16, x = input_545_cast_fp16)[name = string("input_547_cast_fp16")];
+            tensor<int32, [1]> input_549_axes_0 = const()[name = string("input_549_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(498149952)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(498152064)))];
+            tensor<fp16, [1, 188, 1024]> input_549_cast_fp16 = layer_norm(axes = input_549_axes_0, beta = encoder_module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_547_cast_fp16)[name = string("input_549_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(498154176)))];
+            tensor<fp16, [4096]> encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(506542848)))];
+            tensor<fp16, [1, 188, 4096]> linear_91_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16, x = input_549_cast_fp16)[name = string("linear_91_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_553_cast_fp16 = silu(x = linear_91_cast_fp16)[name = string("input_553_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(506551104)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(514939776)))];
+            tensor<fp16, [1, 188, 1024]> linear_92_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16, x = input_553_cast_fp16)[name = string("linear_92_cast_fp16")];
+            fp16 var_2247_to_fp16 = const()[name = string("op_2247_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2248_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_2247_to_fp16)[name = string("op_2248_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_559_cast_fp16 = add(x = input_547_cast_fp16, y = var_2248_cast_fp16)[name = string("input_559_cast_fp16")];
+            tensor<int32, [1]> query_21_axes_0 = const()[name = string("query_21_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(514941888)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(514944000)))];
+            tensor<fp16, [1, 188, 1024]> query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = encoder_module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_self_att_weight_to_fp16, x = input_559_cast_fp16)[name = string("query_21_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(514946112)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(517043328)))];
+            tensor<fp16, [1, 188, 1024]> linear_93_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_q_weight_to_fp16, x = query_21_cast_fp16)[name = string("linear_93_cast_fp16")];
+            tensor<int32, [4]> var_2265 = const()[name = string("op_2265"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_61_cast_fp16 = reshape(shape = var_2265, x = linear_93_cast_fp16)[name = string("q_61_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(517045440)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(519142656)))];
+            tensor<fp16, [1, 188, 1024]> linear_94_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_k_weight_to_fp16, x = query_21_cast_fp16)[name = string("linear_94_cast_fp16")];
+            tensor<int32, [4]> var_2270 = const()[name = string("op_2270"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_41_cast_fp16 = reshape(shape = var_2270, x = linear_94_cast_fp16)[name = string("k_41_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(519144768)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(521241984)))];
+            tensor<fp16, [1, 188, 1024]> linear_95_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_v_weight_to_fp16, x = query_21_cast_fp16)[name = string("linear_95_cast_fp16")];
+            tensor<int32, [4]> var_2275 = const()[name = string("op_2275"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_21_cast_fp16 = reshape(shape = var_2275, x = linear_95_cast_fp16)[name = string("v_21_cast_fp16")];
+            tensor<int32, [4]> value_25_perm_0 = const()[name = string("value_25_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(521244096)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2287_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_module_layers_10_self_attn_pos_bias_u_to_fp16)[name = string("op_2287_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(521246208)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2289_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_module_layers_10_self_attn_pos_bias_v_to_fp16)[name = string("op_2289_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_21_perm_0 = const()[name = string("q_with_bias_v_21_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            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<fp16, [1, 8, 128, 375]> var_2291_to_fp16 = const()[name = string("op_2291_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(521248320)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2289_cast_fp16)[name = string("transpose_217")];
+            tensor<fp16, [1, 8, 188, 375]> x_247_cast_fp16 = matmul(transpose_x = x_247_transpose_x_0, transpose_y = x_247_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = var_2291_to_fp16)[name = string("x_247_cast_fp16")];
+            tensor<int32, [8]> x_249_pad_0 = const()[name = string("x_249_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_249_mode_0 = const()[name = string("x_249_mode_0"), val = string("constant")];
+            fp16 const_188_to_fp16 = const()[name = string("const_188_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_249_cast_fp16 = pad(constant_val = const_188_to_fp16, mode = x_249_mode_0, pad = x_249_pad_0, x = x_247_cast_fp16)[name = string("x_249_cast_fp16")];
+            tensor<int32, [4]> var_2299 = const()[name = string("op_2299"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_251_cast_fp16 = reshape(shape = var_2299, x = x_249_cast_fp16)[name = string("x_251_cast_fp16")];
+            tensor<int32, [4]> var_2303_begin_0 = const()[name = string("op_2303_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2303_end_0 = const()[name = string("op_2303_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2303_end_mask_0 = const()[name = string("op_2303_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2303_cast_fp16 = slice_by_index(begin = var_2303_begin_0, end = var_2303_end_0, end_mask = var_2303_end_mask_0, x = x_251_cast_fp16)[name = string("op_2303_cast_fp16")];
+            tensor<int32, [4]> var_2304 = const()[name = string("op_2304"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_41_cast_fp16 = reshape(shape = var_2304, x = var_2303_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<int32, [4]> transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_93 = transpose(perm = transpose_93_perm_0, x = k_41_cast_fp16)[name = string("transpose_215")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_92 = transpose(perm = transpose_92_perm_0, x = var_2287_cast_fp16)[name = string("transpose_216")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_21_cast_fp16 = matmul(transpose_x = matrix_ac_21_transpose_x_0, transpose_y = matrix_ac_21_transpose_y_0, x = transpose_92, y = transpose_93)[name = string("matrix_ac_21_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_43_begin_0 = const()[name = string("matrix_bd_43_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_43_end_0 = const()[name = string("matrix_bd_43_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_43_end_mask_0 = const()[name = string("matrix_bd_43_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2313_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = string("op_2313_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<fp16, [1, 8, 188, 188]> _inversed_scores_41_cast_fp16 = mul(x = var_2313_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = string("_inversed_scores_41_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_43_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_15)[name = string("scores_43_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2319_cast_fp16 = softmax(axis = var_152, x = scores_43_cast_fp16)[name = string("op_2319_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_561_cast_fp16 = select(a = var_164_to_fp16, b = var_2319_cast_fp16, cond = mask_15)[name = string("input_561_cast_fp16")];
+            bool x_253_transpose_x_0 = const()[name = string("x_253_transpose_x_0"), val = bool(false)];
+            bool x_253_transpose_y_0 = const()[name = string("x_253_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_21_cast_fp16)[name = string("transpose_218")];
+            tensor<fp16, [1, 8, 188, 128]> x_253_cast_fp16 = matmul(transpose_x = x_253_transpose_x_0, transpose_y = x_253_transpose_y_0, x = input_561_cast_fp16, y = value_25_cast_fp16)[name = string("x_253_cast_fp16")];
+            tensor<int32, [4]> var_2323_perm_0 = const()[name = string("op_2323_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2324 = const()[name = string("op_2324"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2323_cast_fp16 = transpose(perm = var_2323_perm_0, x = x_253_cast_fp16)[name = string("transpose_214")];
+            tensor<fp16, [1, 188, 1024]> input_563_cast_fp16 = reshape(shape = var_2324, x = var_2323_cast_fp16)[name = string("input_563_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(522016384)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524113600)))];
+            tensor<fp16, [1, 188, 1024]> linear_97_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_out_weight_to_fp16, x = input_563_cast_fp16)[name = string("linear_97_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_567_cast_fp16 = add(x = input_559_cast_fp16, y = linear_97_cast_fp16)[name = string("input_567_cast_fp16")];
+            tensor<int32, [1]> x_257_axes_0 = const()[name = string("x_257_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524115712)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524117824)))];
+            tensor<fp16, [1, 188, 1024]> x_257_cast_fp16 = layer_norm(axes = x_257_axes_0, beta = encoder_module_layers_10_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_conv_weight_to_fp16, x = input_567_cast_fp16)[name = string("x_257_cast_fp16")];
+            tensor<int32, [3]> input_569_perm_0 = const()[name = string("input_569_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_571_pad_type_0 = const()[name = string("input_571_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_571_strides_0 = const()[name = string("input_571_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_571_pad_0 = const()[name = string("input_571_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_571_dilations_0 = const()[name = string("input_571_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_571_groups_0 = const()[name = string("input_571_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_10_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_10_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524119936)))];
+            tensor<fp16, [2048]> encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(528314304)))];
+            tensor<fp16, [1, 1024, 188]> input_569_cast_fp16 = transpose(perm = input_569_perm_0, x = x_257_cast_fp16)[name = string("transpose_213")];
+            tensor<fp16, [1, 2048, 188]> input_571_cast_fp16 = conv(bias = encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_10_conv_pointwise_conv1_weight_to_fp16, x = input_569_cast_fp16)[name = string("input_571_cast_fp16")];
+            int32 x_259_split_num_splits_0 = const()[name = string("x_259_split_num_splits_0"), val = int32(2)];
+            int32 x_259_split_axis_0 = const()[name = string("x_259_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_259_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_259_split_cast_fp16_1 = split(axis = x_259_split_axis_0, num_splits = x_259_split_num_splits_0, x = input_571_cast_fp16)[name = string("x_259_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_259_split_1_sigmoid_cast_fp16 = sigmoid(x = x_259_split_cast_fp16_1)[name = string("x_259_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_259_cast_fp16 = mul(x = x_259_split_cast_fp16_0, y = x_259_split_1_sigmoid_cast_fp16)[name = string("x_259_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_573_cast_fp16 = select(a = var_164_to_fp16, b = x_259_cast_fp16, cond = var_608)[name = string("input_573_cast_fp16")];
+            tensor<int32, [6]> input_575_pad_0 = const()[name = string("input_575_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_575_mode_0 = const()[name = string("input_575_mode_0"), val = string("constant")];
+            fp16 const_191_to_fp16 = const()[name = string("const_191_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_575_cast_fp16 = pad(constant_val = const_191_to_fp16, mode = input_575_mode_0, pad = input_575_pad_0, x = input_573_cast_fp16)[name = string("input_575_cast_fp16")];
+            string input_577_pad_type_0 = const()[name = string("input_577_pad_type_0"), val = string("valid")];
+            int32 input_577_groups_0 = const()[name = string("input_577_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_577_strides_0 = const()[name = string("input_577_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_577_pad_0 = const()[name = string("input_577_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_577_dilations_0 = const()[name = string("input_577_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_342_to_fp16 = const()[name = string("const_342_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(528318464)))];
+            tensor<fp16, [1024]> const_343_to_fp16 = const()[name = string("const_343_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(528336960)))];
+            tensor<fp16, [1, 1024, 188]> input_579_cast_fp16 = conv(bias = const_343_to_fp16, dilations = input_577_dilations_0, groups = input_577_groups_0, pad = input_577_pad_0, pad_type = input_577_pad_type_0, strides = input_577_strides_0, weight = const_342_to_fp16, x = input_575_cast_fp16)[name = string("input_579_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_581_cast_fp16 = silu(x = input_579_cast_fp16)[name = string("input_581_cast_fp16")];
+            string x_261_pad_type_0 = const()[name = string("x_261_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_261_strides_0 = const()[name = string("x_261_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_261_pad_0 = const()[name = string("x_261_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_261_dilations_0 = const()[name = string("x_261_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_261_groups_0 = const()[name = string("x_261_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(528339072)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530436288)))];
+            tensor<fp16, [1, 1024, 188]> x_261_cast_fp16 = conv(bias = encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16, dilations = x_261_dilations_0, groups = x_261_groups_0, pad = x_261_pad_0, pad_type = x_261_pad_type_0, strides = x_261_strides_0, weight = encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16, x = input_581_cast_fp16)[name = string("x_261_cast_fp16")];
+            tensor<int32, [3]> input_583_perm_0 = const()[name = string("input_583_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_583_cast_fp16 = transpose(perm = input_583_perm_0, x = x_261_cast_fp16)[name = string("transpose_212")];
+            tensor<fp16, [1, 188, 1024]> input_585_cast_fp16 = add(x = input_567_cast_fp16, y = input_583_cast_fp16)[name = string("input_585_cast_fp16")];
+            tensor<int32, [1]> input_587_axes_0 = const()[name = string("input_587_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530438400)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530440512)))];
+            tensor<fp16, [1, 188, 1024]> input_587_cast_fp16 = layer_norm(axes = input_587_axes_0, beta = encoder_module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_585_cast_fp16)[name = string("input_587_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530442624)))];
+            tensor<fp16, [4096]> encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538831296)))];
+            tensor<fp16, [1, 188, 4096]> linear_98_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16, x = input_587_cast_fp16)[name = string("linear_98_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_591_cast_fp16 = silu(x = linear_98_cast_fp16)[name = string("input_591_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538839552)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547228224)))];
+            tensor<fp16, [1, 188, 1024]> linear_99_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16, x = input_591_cast_fp16)[name = string("linear_99_cast_fp16")];
+            fp16 var_2390_to_fp16 = const()[name = string("op_2390_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2391_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2390_to_fp16)[name = string("op_2391_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_597_cast_fp16 = add(x = input_585_cast_fp16, y = var_2391_cast_fp16)[name = string("input_597_cast_fp16")];
+            tensor<int32, [1]> input_599_axes_0 = const()[name = string("input_599_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547230336)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547232448)))];
+            tensor<fp16, [1, 188, 1024]> input_599_cast_fp16 = layer_norm(axes = input_599_axes_0, beta = encoder_module_layers_10_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_out_weight_to_fp16, x = input_597_cast_fp16)[name = string("input_599_cast_fp16")];
+            tensor<int32, [1]> input_601_axes_0 = const()[name = string("input_601_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547234560)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547236672)))];
+            tensor<fp16, [1, 188, 1024]> input_601_cast_fp16 = layer_norm(axes = input_601_axes_0, beta = encoder_module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_599_cast_fp16)[name = string("input_601_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(547238784)))];
+            tensor<fp16, [4096]> encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555627456)))];
+            tensor<fp16, [1, 188, 4096]> linear_100_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16, x = input_601_cast_fp16)[name = string("linear_100_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_605_cast_fp16 = silu(x = linear_100_cast_fp16)[name = string("input_605_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555635712)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(564024384)))];
+            tensor<fp16, [1, 188, 1024]> linear_101_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16, x = input_605_cast_fp16)[name = string("linear_101_cast_fp16")];
+            fp16 var_2421_to_fp16 = const()[name = string("op_2421_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2422_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2421_to_fp16)[name = string("op_2422_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_611_cast_fp16 = add(x = input_599_cast_fp16, y = var_2422_cast_fp16)[name = string("input_611_cast_fp16")];
+            tensor<int32, [1]> query_23_axes_0 = const()[name = string("query_23_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(564026496)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(564028608)))];
+            tensor<fp16, [1, 188, 1024]> query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = encoder_module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_self_att_weight_to_fp16, x = input_611_cast_fp16)[name = string("query_23_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(564030720)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566127936)))];
+            tensor<fp16, [1, 188, 1024]> linear_102_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_q_weight_to_fp16, x = query_23_cast_fp16)[name = string("linear_102_cast_fp16")];
+            tensor<int32, [4]> var_2439 = const()[name = string("op_2439"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_67_cast_fp16 = reshape(shape = var_2439, x = linear_102_cast_fp16)[name = string("q_67_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566130048)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(568227264)))];
+            tensor<fp16, [1, 188, 1024]> linear_103_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_k_weight_to_fp16, x = query_23_cast_fp16)[name = string("linear_103_cast_fp16")];
+            tensor<int32, [4]> var_2444 = const()[name = string("op_2444"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_45_cast_fp16 = reshape(shape = var_2444, x = linear_103_cast_fp16)[name = string("k_45_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(568229376)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570326592)))];
+            tensor<fp16, [1, 188, 1024]> linear_104_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_v_weight_to_fp16, x = query_23_cast_fp16)[name = string("linear_104_cast_fp16")];
+            tensor<int32, [4]> var_2449 = const()[name = string("op_2449"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_23_cast_fp16 = reshape(shape = var_2449, x = linear_104_cast_fp16)[name = string("v_23_cast_fp16")];
+            tensor<int32, [4]> value_27_perm_0 = const()[name = string("value_27_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570328704)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2461_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_module_layers_11_self_attn_pos_bias_u_to_fp16)[name = string("op_2461_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570330816)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2463_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_module_layers_11_self_attn_pos_bias_v_to_fp16)[name = string("op_2463_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_23_perm_0 = const()[name = string("q_with_bias_v_23_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_269_transpose_x_0 = const()[name = string("x_269_transpose_x_0"), val = bool(false)];
+            bool x_269_transpose_y_0 = const()[name = string("x_269_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_2465_to_fp16 = const()[name = string("op_2465_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570332928)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2463_cast_fp16)[name = string("transpose_210")];
+            tensor<fp16, [1, 8, 188, 375]> x_269_cast_fp16 = matmul(transpose_x = x_269_transpose_x_0, transpose_y = x_269_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = var_2465_to_fp16)[name = string("x_269_cast_fp16")];
+            tensor<int32, [8]> x_271_pad_0 = const()[name = string("x_271_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_271_mode_0 = const()[name = string("x_271_mode_0"), val = string("constant")];
+            fp16 const_198_to_fp16 = const()[name = string("const_198_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_271_cast_fp16 = pad(constant_val = const_198_to_fp16, mode = x_271_mode_0, pad = x_271_pad_0, x = x_269_cast_fp16)[name = string("x_271_cast_fp16")];
+            tensor<int32, [4]> var_2473 = const()[name = string("op_2473"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_273_cast_fp16 = reshape(shape = var_2473, x = x_271_cast_fp16)[name = string("x_273_cast_fp16")];
+            tensor<int32, [4]> var_2477_begin_0 = const()[name = string("op_2477_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2477_end_0 = const()[name = string("op_2477_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2477_end_mask_0 = const()[name = string("op_2477_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2477_cast_fp16 = slice_by_index(begin = var_2477_begin_0, end = var_2477_end_0, end_mask = var_2477_end_mask_0, x = x_273_cast_fp16)[name = string("op_2477_cast_fp16")];
+            tensor<int32, [4]> var_2478 = const()[name = string("op_2478"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_45_cast_fp16 = reshape(shape = var_2478, x = var_2477_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<int32, [4]> transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_95 = transpose(perm = transpose_95_perm_0, x = k_45_cast_fp16)[name = string("transpose_208")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_94 = transpose(perm = transpose_94_perm_0, x = var_2461_cast_fp16)[name = string("transpose_209")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_23_cast_fp16 = matmul(transpose_x = matrix_ac_23_transpose_x_0, transpose_y = matrix_ac_23_transpose_y_0, x = transpose_94, y = transpose_95)[name = string("matrix_ac_23_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_47_begin_0 = const()[name = string("matrix_bd_47_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_47_end_0 = const()[name = string("matrix_bd_47_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_47_end_mask_0 = const()[name = string("matrix_bd_47_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2487_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = string("op_2487_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<fp16, [1, 8, 188, 188]> _inversed_scores_45_cast_fp16 = mul(x = var_2487_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = string("_inversed_scores_45_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_47_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_15)[name = string("scores_47_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2493_cast_fp16 = softmax(axis = var_152, x = scores_47_cast_fp16)[name = string("op_2493_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_613_cast_fp16 = select(a = var_164_to_fp16, b = var_2493_cast_fp16, cond = mask_15)[name = string("input_613_cast_fp16")];
+            bool x_275_transpose_x_0 = const()[name = string("x_275_transpose_x_0"), val = bool(false)];
+            bool x_275_transpose_y_0 = const()[name = string("x_275_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_23_cast_fp16)[name = string("transpose_211")];
+            tensor<fp16, [1, 8, 188, 128]> x_275_cast_fp16 = matmul(transpose_x = x_275_transpose_x_0, transpose_y = x_275_transpose_y_0, x = input_613_cast_fp16, y = value_27_cast_fp16)[name = string("x_275_cast_fp16")];
+            tensor<int32, [4]> var_2497_perm_0 = const()[name = string("op_2497_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2498 = const()[name = string("op_2498"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2497_cast_fp16 = transpose(perm = var_2497_perm_0, x = x_275_cast_fp16)[name = string("transpose_207")];
+            tensor<fp16, [1, 188, 1024]> input_615_cast_fp16 = reshape(shape = var_2498, x = var_2497_cast_fp16)[name = string("input_615_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571100992)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(573198208)))];
+            tensor<fp16, [1, 188, 1024]> linear_106_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_out_weight_to_fp16, x = input_615_cast_fp16)[name = string("linear_106_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_619_cast_fp16 = add(x = input_611_cast_fp16, y = linear_106_cast_fp16)[name = string("input_619_cast_fp16")];
+            tensor<int32, [1]> x_279_axes_0 = const()[name = string("x_279_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(573200320)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(573202432)))];
+            tensor<fp16, [1, 188, 1024]> x_279_cast_fp16 = layer_norm(axes = x_279_axes_0, beta = encoder_module_layers_11_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_conv_weight_to_fp16, x = input_619_cast_fp16)[name = string("x_279_cast_fp16")];
+            tensor<int32, [3]> input_621_perm_0 = const()[name = string("input_621_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_623_pad_type_0 = const()[name = string("input_623_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_623_strides_0 = const()[name = string("input_623_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_623_pad_0 = const()[name = string("input_623_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_623_dilations_0 = const()[name = string("input_623_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_623_groups_0 = const()[name = string("input_623_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_11_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_11_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(573204544)))];
+            tensor<fp16, [2048]> encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(577398912)))];
+            tensor<fp16, [1, 1024, 188]> input_621_cast_fp16 = transpose(perm = input_621_perm_0, x = x_279_cast_fp16)[name = string("transpose_206")];
+            tensor<fp16, [1, 2048, 188]> input_623_cast_fp16 = conv(bias = encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_11_conv_pointwise_conv1_weight_to_fp16, x = input_621_cast_fp16)[name = string("input_623_cast_fp16")];
+            int32 x_281_split_num_splits_0 = const()[name = string("x_281_split_num_splits_0"), val = int32(2)];
+            int32 x_281_split_axis_0 = const()[name = string("x_281_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_281_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_281_split_cast_fp16_1 = split(axis = x_281_split_axis_0, num_splits = x_281_split_num_splits_0, x = input_623_cast_fp16)[name = string("x_281_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_281_split_1_sigmoid_cast_fp16 = sigmoid(x = x_281_split_cast_fp16_1)[name = string("x_281_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_281_cast_fp16 = mul(x = x_281_split_cast_fp16_0, y = x_281_split_1_sigmoid_cast_fp16)[name = string("x_281_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_625_cast_fp16 = select(a = var_164_to_fp16, b = x_281_cast_fp16, cond = var_608)[name = string("input_625_cast_fp16")];
+            tensor<int32, [6]> input_627_pad_0 = const()[name = string("input_627_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_627_mode_0 = const()[name = string("input_627_mode_0"), val = string("constant")];
+            fp16 const_201_to_fp16 = const()[name = string("const_201_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_627_cast_fp16 = pad(constant_val = const_201_to_fp16, mode = input_627_mode_0, pad = input_627_pad_0, x = input_625_cast_fp16)[name = string("input_627_cast_fp16")];
+            string input_629_pad_type_0 = const()[name = string("input_629_pad_type_0"), val = string("valid")];
+            int32 input_629_groups_0 = const()[name = string("input_629_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_629_strides_0 = const()[name = string("input_629_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_629_pad_0 = const()[name = string("input_629_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_629_dilations_0 = const()[name = string("input_629_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_344_to_fp16 = const()[name = string("const_344_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(577403072)))];
+            tensor<fp16, [1024]> const_345_to_fp16 = const()[name = string("const_345_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(577421568)))];
+            tensor<fp16, [1, 1024, 188]> input_631_cast_fp16 = conv(bias = const_345_to_fp16, dilations = input_629_dilations_0, groups = input_629_groups_0, pad = input_629_pad_0, pad_type = input_629_pad_type_0, strides = input_629_strides_0, weight = const_344_to_fp16, x = input_627_cast_fp16)[name = string("input_631_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_633_cast_fp16 = silu(x = input_631_cast_fp16)[name = string("input_633_cast_fp16")];
+            string x_283_pad_type_0 = const()[name = string("x_283_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_283_strides_0 = const()[name = string("x_283_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_283_pad_0 = const()[name = string("x_283_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_283_dilations_0 = const()[name = string("x_283_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_283_groups_0 = const()[name = string("x_283_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(577423680)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(579520896)))];
+            tensor<fp16, [1, 1024, 188]> x_283_cast_fp16 = conv(bias = encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16, dilations = x_283_dilations_0, groups = x_283_groups_0, pad = x_283_pad_0, pad_type = x_283_pad_type_0, strides = x_283_strides_0, weight = encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16, x = input_633_cast_fp16)[name = string("x_283_cast_fp16")];
+            tensor<int32, [3]> input_635_perm_0 = const()[name = string("input_635_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_635_cast_fp16 = transpose(perm = input_635_perm_0, x = x_283_cast_fp16)[name = string("transpose_205")];
+            tensor<fp16, [1, 188, 1024]> input_637_cast_fp16 = add(x = input_619_cast_fp16, y = input_635_cast_fp16)[name = string("input_637_cast_fp16")];
+            tensor<int32, [1]> input_639_axes_0 = const()[name = string("input_639_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(579523008)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(579525120)))];
+            tensor<fp16, [1, 188, 1024]> input_639_cast_fp16 = layer_norm(axes = input_639_axes_0, beta = encoder_module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_637_cast_fp16)[name = string("input_639_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(579527232)))];
+            tensor<fp16, [4096]> encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(587915904)))];
+            tensor<fp16, [1, 188, 4096]> linear_107_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16, x = input_639_cast_fp16)[name = string("linear_107_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_643_cast_fp16 = silu(x = linear_107_cast_fp16)[name = string("input_643_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(587924160)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(596312832)))];
+            tensor<fp16, [1, 188, 1024]> linear_108_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16, x = input_643_cast_fp16)[name = string("linear_108_cast_fp16")];
+            fp16 var_2564_to_fp16 = const()[name = string("op_2564_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2565_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2564_to_fp16)[name = string("op_2565_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_649_cast_fp16 = add(x = input_637_cast_fp16, y = var_2565_cast_fp16)[name = string("input_649_cast_fp16")];
+            tensor<int32, [1]> input_651_axes_0 = const()[name = string("input_651_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(596314944)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(596317056)))];
+            tensor<fp16, [1, 188, 1024]> input_651_cast_fp16 = layer_norm(axes = input_651_axes_0, beta = encoder_module_layers_11_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_out_weight_to_fp16, x = input_649_cast_fp16)[name = string("input_651_cast_fp16")];
+            tensor<int32, [1]> input_653_axes_0 = const()[name = string("input_653_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(596319168)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(596321280)))];
+            tensor<fp16, [1, 188, 1024]> input_653_cast_fp16 = layer_norm(axes = input_653_axes_0, beta = encoder_module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_651_cast_fp16)[name = string("input_653_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(596323392)))];
+            tensor<fp16, [4096]> encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(604712064)))];
+            tensor<fp16, [1, 188, 4096]> linear_109_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16, x = input_653_cast_fp16)[name = string("linear_109_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_657_cast_fp16 = silu(x = linear_109_cast_fp16)[name = string("input_657_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(604720320)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(613108992)))];
+            tensor<fp16, [1, 188, 1024]> linear_110_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16, x = input_657_cast_fp16)[name = string("linear_110_cast_fp16")];
+            fp16 var_2595_to_fp16 = const()[name = string("op_2595_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2596_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2595_to_fp16)[name = string("op_2596_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_663_cast_fp16 = add(x = input_651_cast_fp16, y = var_2596_cast_fp16)[name = string("input_663_cast_fp16")];
+            tensor<int32, [1]> query_25_axes_0 = const()[name = string("query_25_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(613111104)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(613113216)))];
+            tensor<fp16, [1, 188, 1024]> query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = encoder_module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_self_att_weight_to_fp16, x = input_663_cast_fp16)[name = string("query_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(613115328)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(615212544)))];
+            tensor<fp16, [1, 188, 1024]> linear_111_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_q_weight_to_fp16, x = query_25_cast_fp16)[name = string("linear_111_cast_fp16")];
+            tensor<int32, [4]> var_2613 = const()[name = string("op_2613"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_73_cast_fp16 = reshape(shape = var_2613, x = linear_111_cast_fp16)[name = string("q_73_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(615214656)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617311872)))];
+            tensor<fp16, [1, 188, 1024]> linear_112_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_k_weight_to_fp16, x = query_25_cast_fp16)[name = string("linear_112_cast_fp16")];
+            tensor<int32, [4]> var_2618 = const()[name = string("op_2618"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_49_cast_fp16 = reshape(shape = var_2618, x = linear_112_cast_fp16)[name = string("k_49_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617313984)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(619411200)))];
+            tensor<fp16, [1, 188, 1024]> linear_113_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_v_weight_to_fp16, x = query_25_cast_fp16)[name = string("linear_113_cast_fp16")];
+            tensor<int32, [4]> var_2623 = const()[name = string("op_2623"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_25_cast_fp16 = reshape(shape = var_2623, x = linear_113_cast_fp16)[name = string("v_25_cast_fp16")];
+            tensor<int32, [4]> value_29_perm_0 = const()[name = string("value_29_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(619413312)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2635_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_module_layers_12_self_attn_pos_bias_u_to_fp16)[name = string("op_2635_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(619415424)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2637_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_module_layers_12_self_attn_pos_bias_v_to_fp16)[name = string("op_2637_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_25_perm_0 = const()[name = string("q_with_bias_v_25_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_291_transpose_x_0 = const()[name = string("x_291_transpose_x_0"), val = bool(false)];
+            bool x_291_transpose_y_0 = const()[name = string("x_291_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_2639_to_fp16 = const()[name = string("op_2639_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(619417536)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2637_cast_fp16)[name = string("transpose_203")];
+            tensor<fp16, [1, 8, 188, 375]> x_291_cast_fp16 = matmul(transpose_x = x_291_transpose_x_0, transpose_y = x_291_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = var_2639_to_fp16)[name = string("x_291_cast_fp16")];
+            tensor<int32, [8]> x_293_pad_0 = const()[name = string("x_293_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_293_mode_0 = const()[name = string("x_293_mode_0"), val = string("constant")];
+            fp16 const_208_to_fp16 = const()[name = string("const_208_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_293_cast_fp16 = pad(constant_val = const_208_to_fp16, mode = x_293_mode_0, pad = x_293_pad_0, x = x_291_cast_fp16)[name = string("x_293_cast_fp16")];
+            tensor<int32, [4]> var_2647 = const()[name = string("op_2647"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_295_cast_fp16 = reshape(shape = var_2647, x = x_293_cast_fp16)[name = string("x_295_cast_fp16")];
+            tensor<int32, [4]> var_2651_begin_0 = const()[name = string("op_2651_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2651_end_0 = const()[name = string("op_2651_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2651_end_mask_0 = const()[name = string("op_2651_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2651_cast_fp16 = slice_by_index(begin = var_2651_begin_0, end = var_2651_end_0, end_mask = var_2651_end_mask_0, x = x_295_cast_fp16)[name = string("op_2651_cast_fp16")];
+            tensor<int32, [4]> var_2652 = const()[name = string("op_2652"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_49_cast_fp16 = reshape(shape = var_2652, x = var_2651_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<int32, [4]> transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_97 = transpose(perm = transpose_97_perm_0, x = k_49_cast_fp16)[name = string("transpose_201")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_96 = transpose(perm = transpose_96_perm_0, x = var_2635_cast_fp16)[name = string("transpose_202")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_25_cast_fp16 = matmul(transpose_x = matrix_ac_25_transpose_x_0, transpose_y = matrix_ac_25_transpose_y_0, x = transpose_96, y = transpose_97)[name = string("matrix_ac_25_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_51_begin_0 = const()[name = string("matrix_bd_51_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_51_end_0 = const()[name = string("matrix_bd_51_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_51_end_mask_0 = const()[name = string("matrix_bd_51_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2661_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = string("op_2661_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<fp16, [1, 8, 188, 188]> _inversed_scores_49_cast_fp16 = mul(x = var_2661_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = string("_inversed_scores_49_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_51_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_15)[name = string("scores_51_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2667_cast_fp16 = softmax(axis = var_152, x = scores_51_cast_fp16)[name = string("op_2667_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_665_cast_fp16 = select(a = var_164_to_fp16, b = var_2667_cast_fp16, cond = mask_15)[name = string("input_665_cast_fp16")];
+            bool x_297_transpose_x_0 = const()[name = string("x_297_transpose_x_0"), val = bool(false)];
+            bool x_297_transpose_y_0 = const()[name = string("x_297_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_25_cast_fp16)[name = string("transpose_204")];
+            tensor<fp16, [1, 8, 188, 128]> x_297_cast_fp16 = matmul(transpose_x = x_297_transpose_x_0, transpose_y = x_297_transpose_y_0, x = input_665_cast_fp16, y = value_29_cast_fp16)[name = string("x_297_cast_fp16")];
+            tensor<int32, [4]> var_2671_perm_0 = const()[name = string("op_2671_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2672 = const()[name = string("op_2672"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2671_cast_fp16 = transpose(perm = var_2671_perm_0, x = x_297_cast_fp16)[name = string("transpose_200")];
+            tensor<fp16, [1, 188, 1024]> input_667_cast_fp16 = reshape(shape = var_2672, x = var_2671_cast_fp16)[name = string("input_667_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(620185600)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(622282816)))];
+            tensor<fp16, [1, 188, 1024]> linear_115_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_out_weight_to_fp16, x = input_667_cast_fp16)[name = string("linear_115_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_671_cast_fp16 = add(x = input_663_cast_fp16, y = linear_115_cast_fp16)[name = string("input_671_cast_fp16")];
+            tensor<int32, [1]> x_301_axes_0 = const()[name = string("x_301_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(622284928)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(622287040)))];
+            tensor<fp16, [1, 188, 1024]> x_301_cast_fp16 = layer_norm(axes = x_301_axes_0, beta = encoder_module_layers_12_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_conv_weight_to_fp16, x = input_671_cast_fp16)[name = string("x_301_cast_fp16")];
+            tensor<int32, [3]> input_673_perm_0 = const()[name = string("input_673_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_675_pad_type_0 = const()[name = string("input_675_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_675_strides_0 = const()[name = string("input_675_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_675_pad_0 = const()[name = string("input_675_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_675_dilations_0 = const()[name = string("input_675_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_675_groups_0 = const()[name = string("input_675_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_12_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_12_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(622289152)))];
+            tensor<fp16, [2048]> encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(626483520)))];
+            tensor<fp16, [1, 1024, 188]> input_673_cast_fp16 = transpose(perm = input_673_perm_0, x = x_301_cast_fp16)[name = string("transpose_199")];
+            tensor<fp16, [1, 2048, 188]> input_675_cast_fp16 = conv(bias = encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_12_conv_pointwise_conv1_weight_to_fp16, x = input_673_cast_fp16)[name = string("input_675_cast_fp16")];
+            int32 x_303_split_num_splits_0 = const()[name = string("x_303_split_num_splits_0"), val = int32(2)];
+            int32 x_303_split_axis_0 = const()[name = string("x_303_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_303_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_303_split_cast_fp16_1 = split(axis = x_303_split_axis_0, num_splits = x_303_split_num_splits_0, x = input_675_cast_fp16)[name = string("x_303_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_303_split_1_sigmoid_cast_fp16 = sigmoid(x = x_303_split_cast_fp16_1)[name = string("x_303_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_303_cast_fp16 = mul(x = x_303_split_cast_fp16_0, y = x_303_split_1_sigmoid_cast_fp16)[name = string("x_303_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_677_cast_fp16 = select(a = var_164_to_fp16, b = x_303_cast_fp16, cond = var_608)[name = string("input_677_cast_fp16")];
+            tensor<int32, [6]> input_679_pad_0 = const()[name = string("input_679_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_679_mode_0 = const()[name = string("input_679_mode_0"), val = string("constant")];
+            fp16 const_211_to_fp16 = const()[name = string("const_211_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_679_cast_fp16 = pad(constant_val = const_211_to_fp16, mode = input_679_mode_0, pad = input_679_pad_0, x = input_677_cast_fp16)[name = string("input_679_cast_fp16")];
+            string input_681_pad_type_0 = const()[name = string("input_681_pad_type_0"), val = string("valid")];
+            int32 input_681_groups_0 = const()[name = string("input_681_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_681_strides_0 = const()[name = string("input_681_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_681_pad_0 = const()[name = string("input_681_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_681_dilations_0 = const()[name = string("input_681_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_346_to_fp16 = const()[name = string("const_346_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(626487680)))];
+            tensor<fp16, [1024]> const_347_to_fp16 = const()[name = string("const_347_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(626506176)))];
+            tensor<fp16, [1, 1024, 188]> input_683_cast_fp16 = conv(bias = const_347_to_fp16, dilations = input_681_dilations_0, groups = input_681_groups_0, pad = input_681_pad_0, pad_type = input_681_pad_type_0, strides = input_681_strides_0, weight = const_346_to_fp16, x = input_679_cast_fp16)[name = string("input_683_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_685_cast_fp16 = silu(x = input_683_cast_fp16)[name = string("input_685_cast_fp16")];
+            string x_305_pad_type_0 = const()[name = string("x_305_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_305_strides_0 = const()[name = string("x_305_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_305_pad_0 = const()[name = string("x_305_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_305_dilations_0 = const()[name = string("x_305_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_305_groups_0 = const()[name = string("x_305_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(626508288)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(628605504)))];
+            tensor<fp16, [1, 1024, 188]> x_305_cast_fp16 = conv(bias = encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16, dilations = x_305_dilations_0, groups = x_305_groups_0, pad = x_305_pad_0, pad_type = x_305_pad_type_0, strides = x_305_strides_0, weight = encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16, x = input_685_cast_fp16)[name = string("x_305_cast_fp16")];
+            tensor<int32, [3]> input_687_perm_0 = const()[name = string("input_687_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_687_cast_fp16 = transpose(perm = input_687_perm_0, x = x_305_cast_fp16)[name = string("transpose_198")];
+            tensor<fp16, [1, 188, 1024]> input_689_cast_fp16 = add(x = input_671_cast_fp16, y = input_687_cast_fp16)[name = string("input_689_cast_fp16")];
+            tensor<int32, [1]> input_691_axes_0 = const()[name = string("input_691_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(628607616)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(628609728)))];
+            tensor<fp16, [1, 188, 1024]> input_691_cast_fp16 = layer_norm(axes = input_691_axes_0, beta = encoder_module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_689_cast_fp16)[name = string("input_691_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(628611840)))];
+            tensor<fp16, [4096]> encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(637000512)))];
+            tensor<fp16, [1, 188, 4096]> linear_116_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16, x = input_691_cast_fp16)[name = string("linear_116_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_695_cast_fp16 = silu(x = linear_116_cast_fp16)[name = string("input_695_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(637008768)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(645397440)))];
+            tensor<fp16, [1, 188, 1024]> linear_117_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16, x = input_695_cast_fp16)[name = string("linear_117_cast_fp16")];
+            fp16 var_2738_to_fp16 = const()[name = string("op_2738_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2739_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2738_to_fp16)[name = string("op_2739_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_701_cast_fp16 = add(x = input_689_cast_fp16, y = var_2739_cast_fp16)[name = string("input_701_cast_fp16")];
+            tensor<int32, [1]> input_703_axes_0 = const()[name = string("input_703_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(645399552)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(645401664)))];
+            tensor<fp16, [1, 188, 1024]> input_703_cast_fp16 = layer_norm(axes = input_703_axes_0, beta = encoder_module_layers_12_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_out_weight_to_fp16, x = input_701_cast_fp16)[name = string("input_703_cast_fp16")];
+            tensor<int32, [1]> input_705_axes_0 = const()[name = string("input_705_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(645403776)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(645405888)))];
+            tensor<fp16, [1, 188, 1024]> input_705_cast_fp16 = layer_norm(axes = input_705_axes_0, beta = encoder_module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_703_cast_fp16)[name = string("input_705_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(645408000)))];
+            tensor<fp16, [4096]> encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(653796672)))];
+            tensor<fp16, [1, 188, 4096]> linear_118_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16, x = input_705_cast_fp16)[name = string("linear_118_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_709_cast_fp16 = silu(x = linear_118_cast_fp16)[name = string("input_709_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(653804928)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(662193600)))];
+            tensor<fp16, [1, 188, 1024]> linear_119_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16, x = input_709_cast_fp16)[name = string("linear_119_cast_fp16")];
+            fp16 var_2769_to_fp16 = const()[name = string("op_2769_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2770_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2769_to_fp16)[name = string("op_2770_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_715_cast_fp16 = add(x = input_703_cast_fp16, y = var_2770_cast_fp16)[name = string("input_715_cast_fp16")];
+            tensor<int32, [1]> query_27_axes_0 = const()[name = string("query_27_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(662195712)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(662197824)))];
+            tensor<fp16, [1, 188, 1024]> query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = encoder_module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_self_att_weight_to_fp16, x = input_715_cast_fp16)[name = string("query_27_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(662199936)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(664297152)))];
+            tensor<fp16, [1, 188, 1024]> linear_120_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_q_weight_to_fp16, x = query_27_cast_fp16)[name = string("linear_120_cast_fp16")];
+            tensor<int32, [4]> var_2787 = const()[name = string("op_2787"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_79_cast_fp16 = reshape(shape = var_2787, x = linear_120_cast_fp16)[name = string("q_79_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(664299264)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(666396480)))];
+            tensor<fp16, [1, 188, 1024]> linear_121_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_k_weight_to_fp16, x = query_27_cast_fp16)[name = string("linear_121_cast_fp16")];
+            tensor<int32, [4]> var_2792 = const()[name = string("op_2792"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_53_cast_fp16 = reshape(shape = var_2792, x = linear_121_cast_fp16)[name = string("k_53_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(666398592)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(668495808)))];
+            tensor<fp16, [1, 188, 1024]> linear_122_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_v_weight_to_fp16, x = query_27_cast_fp16)[name = string("linear_122_cast_fp16")];
+            tensor<int32, [4]> var_2797 = const()[name = string("op_2797"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_27_cast_fp16 = reshape(shape = var_2797, x = linear_122_cast_fp16)[name = string("v_27_cast_fp16")];
+            tensor<int32, [4]> value_31_perm_0 = const()[name = string("value_31_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(668497920)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2809_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_module_layers_13_self_attn_pos_bias_u_to_fp16)[name = string("op_2809_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(668500032)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2811_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_module_layers_13_self_attn_pos_bias_v_to_fp16)[name = string("op_2811_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_27_perm_0 = const()[name = string("q_with_bias_v_27_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_313_transpose_x_0 = const()[name = string("x_313_transpose_x_0"), val = bool(false)];
+            bool x_313_transpose_y_0 = const()[name = string("x_313_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_2813_to_fp16 = const()[name = string("op_2813_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(668502144)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2811_cast_fp16)[name = string("transpose_196")];
+            tensor<fp16, [1, 8, 188, 375]> x_313_cast_fp16 = matmul(transpose_x = x_313_transpose_x_0, transpose_y = x_313_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = var_2813_to_fp16)[name = string("x_313_cast_fp16")];
+            tensor<int32, [8]> x_315_pad_0 = const()[name = string("x_315_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_315_mode_0 = const()[name = string("x_315_mode_0"), val = string("constant")];
+            fp16 const_218_to_fp16 = const()[name = string("const_218_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_315_cast_fp16 = pad(constant_val = const_218_to_fp16, mode = x_315_mode_0, pad = x_315_pad_0, x = x_313_cast_fp16)[name = string("x_315_cast_fp16")];
+            tensor<int32, [4]> var_2821 = const()[name = string("op_2821"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_317_cast_fp16 = reshape(shape = var_2821, x = x_315_cast_fp16)[name = string("x_317_cast_fp16")];
+            tensor<int32, [4]> var_2825_begin_0 = const()[name = string("op_2825_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2825_end_0 = const()[name = string("op_2825_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2825_end_mask_0 = const()[name = string("op_2825_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2825_cast_fp16 = slice_by_index(begin = var_2825_begin_0, end = var_2825_end_0, end_mask = var_2825_end_mask_0, x = x_317_cast_fp16)[name = string("op_2825_cast_fp16")];
+            tensor<int32, [4]> var_2826 = const()[name = string("op_2826"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_53_cast_fp16 = reshape(shape = var_2826, x = var_2825_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<int32, [4]> transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_99 = transpose(perm = transpose_99_perm_0, x = k_53_cast_fp16)[name = string("transpose_194")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_98 = transpose(perm = transpose_98_perm_0, x = var_2809_cast_fp16)[name = string("transpose_195")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_27_cast_fp16 = matmul(transpose_x = matrix_ac_27_transpose_x_0, transpose_y = matrix_ac_27_transpose_y_0, x = transpose_98, y = transpose_99)[name = string("matrix_ac_27_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_55_begin_0 = const()[name = string("matrix_bd_55_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_55_end_0 = const()[name = string("matrix_bd_55_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_55_end_mask_0 = const()[name = string("matrix_bd_55_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2835_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = string("op_2835_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<fp16, [1, 8, 188, 188]> _inversed_scores_53_cast_fp16 = mul(x = var_2835_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = string("_inversed_scores_53_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_55_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_15)[name = string("scores_55_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2841_cast_fp16 = softmax(axis = var_152, x = scores_55_cast_fp16)[name = string("op_2841_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_717_cast_fp16 = select(a = var_164_to_fp16, b = var_2841_cast_fp16, cond = mask_15)[name = string("input_717_cast_fp16")];
+            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<fp16, [1, 8, 188, 128]> value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_27_cast_fp16)[name = string("transpose_197")];
+            tensor<fp16, [1, 8, 188, 128]> x_319_cast_fp16 = matmul(transpose_x = x_319_transpose_x_0, transpose_y = x_319_transpose_y_0, x = input_717_cast_fp16, y = value_31_cast_fp16)[name = string("x_319_cast_fp16")];
+            tensor<int32, [4]> var_2845_perm_0 = const()[name = string("op_2845_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2846 = const()[name = string("op_2846"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2845_cast_fp16 = transpose(perm = var_2845_perm_0, x = x_319_cast_fp16)[name = string("transpose_193")];
+            tensor<fp16, [1, 188, 1024]> input_719_cast_fp16 = reshape(shape = var_2846, x = var_2845_cast_fp16)[name = string("input_719_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(669270208)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(671367424)))];
+            tensor<fp16, [1, 188, 1024]> linear_124_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_out_weight_to_fp16, x = input_719_cast_fp16)[name = string("linear_124_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_723_cast_fp16 = add(x = input_715_cast_fp16, y = linear_124_cast_fp16)[name = string("input_723_cast_fp16")];
+            tensor<int32, [1]> x_323_axes_0 = const()[name = string("x_323_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(671369536)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(671371648)))];
+            tensor<fp16, [1, 188, 1024]> x_323_cast_fp16 = layer_norm(axes = x_323_axes_0, beta = encoder_module_layers_13_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_conv_weight_to_fp16, x = input_723_cast_fp16)[name = string("x_323_cast_fp16")];
+            tensor<int32, [3]> input_725_perm_0 = const()[name = string("input_725_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_727_pad_type_0 = const()[name = string("input_727_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_727_strides_0 = const()[name = string("input_727_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_727_pad_0 = const()[name = string("input_727_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_727_dilations_0 = const()[name = string("input_727_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_727_groups_0 = const()[name = string("input_727_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_13_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_13_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(671373760)))];
+            tensor<fp16, [2048]> encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(675568128)))];
+            tensor<fp16, [1, 1024, 188]> input_725_cast_fp16 = transpose(perm = input_725_perm_0, x = x_323_cast_fp16)[name = string("transpose_192")];
+            tensor<fp16, [1, 2048, 188]> input_727_cast_fp16 = conv(bias = encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_13_conv_pointwise_conv1_weight_to_fp16, x = input_725_cast_fp16)[name = string("input_727_cast_fp16")];
+            int32 x_325_split_num_splits_0 = const()[name = string("x_325_split_num_splits_0"), val = int32(2)];
+            int32 x_325_split_axis_0 = const()[name = string("x_325_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_325_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_325_split_cast_fp16_1 = split(axis = x_325_split_axis_0, num_splits = x_325_split_num_splits_0, x = input_727_cast_fp16)[name = string("x_325_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_325_split_1_sigmoid_cast_fp16 = sigmoid(x = x_325_split_cast_fp16_1)[name = string("x_325_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_325_cast_fp16 = mul(x = x_325_split_cast_fp16_0, y = x_325_split_1_sigmoid_cast_fp16)[name = string("x_325_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_729_cast_fp16 = select(a = var_164_to_fp16, b = x_325_cast_fp16, cond = var_608)[name = string("input_729_cast_fp16")];
+            tensor<int32, [6]> input_731_pad_0 = const()[name = string("input_731_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_731_mode_0 = const()[name = string("input_731_mode_0"), val = string("constant")];
+            fp16 const_221_to_fp16 = const()[name = string("const_221_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_731_cast_fp16 = pad(constant_val = const_221_to_fp16, mode = input_731_mode_0, pad = input_731_pad_0, x = input_729_cast_fp16)[name = string("input_731_cast_fp16")];
+            string input_733_pad_type_0 = const()[name = string("input_733_pad_type_0"), val = string("valid")];
+            int32 input_733_groups_0 = const()[name = string("input_733_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_733_strides_0 = const()[name = string("input_733_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_733_pad_0 = const()[name = string("input_733_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_733_dilations_0 = const()[name = string("input_733_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_348_to_fp16 = const()[name = string("const_348_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(675572288)))];
+            tensor<fp16, [1024]> const_349_to_fp16 = const()[name = string("const_349_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(675590784)))];
+            tensor<fp16, [1, 1024, 188]> input_735_cast_fp16 = conv(bias = const_349_to_fp16, dilations = input_733_dilations_0, groups = input_733_groups_0, pad = input_733_pad_0, pad_type = input_733_pad_type_0, strides = input_733_strides_0, weight = const_348_to_fp16, x = input_731_cast_fp16)[name = string("input_735_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_737_cast_fp16 = silu(x = input_735_cast_fp16)[name = string("input_737_cast_fp16")];
+            string x_327_pad_type_0 = const()[name = string("x_327_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_327_strides_0 = const()[name = string("x_327_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_327_pad_0 = const()[name = string("x_327_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_327_dilations_0 = const()[name = string("x_327_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_327_groups_0 = const()[name = string("x_327_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(675592896)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(677690112)))];
+            tensor<fp16, [1, 1024, 188]> x_327_cast_fp16 = conv(bias = encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16, dilations = x_327_dilations_0, groups = x_327_groups_0, pad = x_327_pad_0, pad_type = x_327_pad_type_0, strides = x_327_strides_0, weight = encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16, x = input_737_cast_fp16)[name = string("x_327_cast_fp16")];
+            tensor<int32, [3]> input_739_perm_0 = const()[name = string("input_739_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_739_cast_fp16 = transpose(perm = input_739_perm_0, x = x_327_cast_fp16)[name = string("transpose_191")];
+            tensor<fp16, [1, 188, 1024]> input_741_cast_fp16 = add(x = input_723_cast_fp16, y = input_739_cast_fp16)[name = string("input_741_cast_fp16")];
+            tensor<int32, [1]> input_743_axes_0 = const()[name = string("input_743_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(677692224)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(677694336)))];
+            tensor<fp16, [1, 188, 1024]> input_743_cast_fp16 = layer_norm(axes = input_743_axes_0, beta = encoder_module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_741_cast_fp16)[name = string("input_743_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(677696448)))];
+            tensor<fp16, [4096]> encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686085120)))];
+            tensor<fp16, [1, 188, 4096]> linear_125_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16, x = input_743_cast_fp16)[name = string("linear_125_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_747_cast_fp16 = silu(x = linear_125_cast_fp16)[name = string("input_747_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686093376)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694482048)))];
+            tensor<fp16, [1, 188, 1024]> linear_126_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16, x = input_747_cast_fp16)[name = string("linear_126_cast_fp16")];
+            fp16 var_2912_to_fp16 = const()[name = string("op_2912_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2913_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2912_to_fp16)[name = string("op_2913_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_753_cast_fp16 = add(x = input_741_cast_fp16, y = var_2913_cast_fp16)[name = string("input_753_cast_fp16")];
+            tensor<int32, [1]> input_755_axes_0 = const()[name = string("input_755_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694484160)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694486272)))];
+            tensor<fp16, [1, 188, 1024]> input_755_cast_fp16 = layer_norm(axes = input_755_axes_0, beta = encoder_module_layers_13_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_out_weight_to_fp16, x = input_753_cast_fp16)[name = string("input_755_cast_fp16")];
+            tensor<int32, [1]> input_757_axes_0 = const()[name = string("input_757_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694488384)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694490496)))];
+            tensor<fp16, [1, 188, 1024]> input_757_cast_fp16 = layer_norm(axes = input_757_axes_0, beta = encoder_module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_755_cast_fp16)[name = string("input_757_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694492608)))];
+            tensor<fp16, [4096]> encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(702881280)))];
+            tensor<fp16, [1, 188, 4096]> linear_127_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16, x = input_757_cast_fp16)[name = string("linear_127_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_761_cast_fp16 = silu(x = linear_127_cast_fp16)[name = string("input_761_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(702889536)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(711278208)))];
+            tensor<fp16, [1, 188, 1024]> linear_128_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16, x = input_761_cast_fp16)[name = string("linear_128_cast_fp16")];
+            fp16 var_2943_to_fp16 = const()[name = string("op_2943_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2944_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2943_to_fp16)[name = string("op_2944_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_767_cast_fp16 = add(x = input_755_cast_fp16, y = var_2944_cast_fp16)[name = string("input_767_cast_fp16")];
+            tensor<int32, [1]> query_29_axes_0 = const()[name = string("query_29_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(711280320)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(711282432)))];
+            tensor<fp16, [1, 188, 1024]> query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = encoder_module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_self_att_weight_to_fp16, x = input_767_cast_fp16)[name = string("query_29_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(711284544)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(713381760)))];
+            tensor<fp16, [1, 188, 1024]> linear_129_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_q_weight_to_fp16, x = query_29_cast_fp16)[name = string("linear_129_cast_fp16")];
+            tensor<int32, [4]> var_2961 = const()[name = string("op_2961"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_85_cast_fp16 = reshape(shape = var_2961, x = linear_129_cast_fp16)[name = string("q_85_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(713383872)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(715481088)))];
+            tensor<fp16, [1, 188, 1024]> linear_130_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_k_weight_to_fp16, x = query_29_cast_fp16)[name = string("linear_130_cast_fp16")];
+            tensor<int32, [4]> var_2966 = const()[name = string("op_2966"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_57_cast_fp16 = reshape(shape = var_2966, x = linear_130_cast_fp16)[name = string("k_57_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(715483200)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(717580416)))];
+            tensor<fp16, [1, 188, 1024]> linear_131_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_v_weight_to_fp16, x = query_29_cast_fp16)[name = string("linear_131_cast_fp16")];
+            tensor<int32, [4]> var_2971 = const()[name = string("op_2971"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_29_cast_fp16 = reshape(shape = var_2971, x = linear_131_cast_fp16)[name = string("v_29_cast_fp16")];
+            tensor<int32, [4]> value_33_perm_0 = const()[name = string("value_33_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(717582528)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2983_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_module_layers_14_self_attn_pos_bias_u_to_fp16)[name = string("op_2983_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(717584640)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2985_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_module_layers_14_self_attn_pos_bias_v_to_fp16)[name = string("op_2985_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_29_perm_0 = const()[name = string("q_with_bias_v_29_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_335_transpose_x_0 = const()[name = string("x_335_transpose_x_0"), val = bool(false)];
+            bool x_335_transpose_y_0 = const()[name = string("x_335_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_2987_to_fp16 = const()[name = string("op_2987_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(717586752)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2985_cast_fp16)[name = string("transpose_189")];
+            tensor<fp16, [1, 8, 188, 375]> x_335_cast_fp16 = matmul(transpose_x = x_335_transpose_x_0, transpose_y = x_335_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = var_2987_to_fp16)[name = string("x_335_cast_fp16")];
+            tensor<int32, [8]> x_337_pad_0 = const()[name = string("x_337_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_337_mode_0 = const()[name = string("x_337_mode_0"), val = string("constant")];
+            fp16 const_228_to_fp16 = const()[name = string("const_228_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_337_cast_fp16 = pad(constant_val = const_228_to_fp16, mode = x_337_mode_0, pad = x_337_pad_0, x = x_335_cast_fp16)[name = string("x_337_cast_fp16")];
+            tensor<int32, [4]> var_2995 = const()[name = string("op_2995"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_339_cast_fp16 = reshape(shape = var_2995, x = x_337_cast_fp16)[name = string("x_339_cast_fp16")];
+            tensor<int32, [4]> var_2999_begin_0 = const()[name = string("op_2999_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2999_end_0 = const()[name = string("op_2999_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2999_end_mask_0 = const()[name = string("op_2999_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2999_cast_fp16 = slice_by_index(begin = var_2999_begin_0, end = var_2999_end_0, end_mask = var_2999_end_mask_0, x = x_339_cast_fp16)[name = string("op_2999_cast_fp16")];
+            tensor<int32, [4]> var_3000 = const()[name = string("op_3000"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_57_cast_fp16 = reshape(shape = var_3000, x = var_2999_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<int32, [4]> transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_101 = transpose(perm = transpose_101_perm_0, x = k_57_cast_fp16)[name = string("transpose_187")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_100 = transpose(perm = transpose_100_perm_0, x = var_2983_cast_fp16)[name = string("transpose_188")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_29_cast_fp16 = matmul(transpose_x = matrix_ac_29_transpose_x_0, transpose_y = matrix_ac_29_transpose_y_0, x = transpose_100, y = transpose_101)[name = string("matrix_ac_29_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_59_begin_0 = const()[name = string("matrix_bd_59_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_59_end_0 = const()[name = string("matrix_bd_59_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_59_end_mask_0 = const()[name = string("matrix_bd_59_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3009_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = string("op_3009_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<fp16, [1, 8, 188, 188]> _inversed_scores_57_cast_fp16 = mul(x = var_3009_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = string("_inversed_scores_57_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_59_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_15)[name = string("scores_59_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3015_cast_fp16 = softmax(axis = var_152, x = scores_59_cast_fp16)[name = string("op_3015_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_769_cast_fp16 = select(a = var_164_to_fp16, b = var_3015_cast_fp16, cond = mask_15)[name = string("input_769_cast_fp16")];
+            bool x_341_transpose_x_0 = const()[name = string("x_341_transpose_x_0"), val = bool(false)];
+            bool x_341_transpose_y_0 = const()[name = string("x_341_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_29_cast_fp16)[name = string("transpose_190")];
+            tensor<fp16, [1, 8, 188, 128]> x_341_cast_fp16 = matmul(transpose_x = x_341_transpose_x_0, transpose_y = x_341_transpose_y_0, x = input_769_cast_fp16, y = value_33_cast_fp16)[name = string("x_341_cast_fp16")];
+            tensor<int32, [4]> var_3019_perm_0 = const()[name = string("op_3019_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3020 = const()[name = string("op_3020"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3019_cast_fp16 = transpose(perm = var_3019_perm_0, x = x_341_cast_fp16)[name = string("transpose_186")];
+            tensor<fp16, [1, 188, 1024]> input_771_cast_fp16 = reshape(shape = var_3020, x = var_3019_cast_fp16)[name = string("input_771_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(718354816)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(720452032)))];
+            tensor<fp16, [1, 188, 1024]> linear_133_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_out_weight_to_fp16, x = input_771_cast_fp16)[name = string("linear_133_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_775_cast_fp16 = add(x = input_767_cast_fp16, y = linear_133_cast_fp16)[name = string("input_775_cast_fp16")];
+            tensor<int32, [1]> x_345_axes_0 = const()[name = string("x_345_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(720454144)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(720456256)))];
+            tensor<fp16, [1, 188, 1024]> x_345_cast_fp16 = layer_norm(axes = x_345_axes_0, beta = encoder_module_layers_14_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_conv_weight_to_fp16, x = input_775_cast_fp16)[name = string("x_345_cast_fp16")];
+            tensor<int32, [3]> input_777_perm_0 = const()[name = string("input_777_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_779_pad_type_0 = const()[name = string("input_779_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_779_strides_0 = const()[name = string("input_779_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_779_pad_0 = const()[name = string("input_779_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_779_dilations_0 = const()[name = string("input_779_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_779_groups_0 = const()[name = string("input_779_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_14_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_14_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(720458368)))];
+            tensor<fp16, [2048]> encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724652736)))];
+            tensor<fp16, [1, 1024, 188]> input_777_cast_fp16 = transpose(perm = input_777_perm_0, x = x_345_cast_fp16)[name = string("transpose_185")];
+            tensor<fp16, [1, 2048, 188]> input_779_cast_fp16 = conv(bias = encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_14_conv_pointwise_conv1_weight_to_fp16, x = input_777_cast_fp16)[name = string("input_779_cast_fp16")];
+            int32 x_347_split_num_splits_0 = const()[name = string("x_347_split_num_splits_0"), val = int32(2)];
+            int32 x_347_split_axis_0 = const()[name = string("x_347_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_347_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_347_split_cast_fp16_1 = split(axis = x_347_split_axis_0, num_splits = x_347_split_num_splits_0, x = input_779_cast_fp16)[name = string("x_347_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_347_split_1_sigmoid_cast_fp16 = sigmoid(x = x_347_split_cast_fp16_1)[name = string("x_347_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_347_cast_fp16 = mul(x = x_347_split_cast_fp16_0, y = x_347_split_1_sigmoid_cast_fp16)[name = string("x_347_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_781_cast_fp16 = select(a = var_164_to_fp16, b = x_347_cast_fp16, cond = var_608)[name = string("input_781_cast_fp16")];
+            tensor<int32, [6]> input_783_pad_0 = const()[name = string("input_783_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_783_mode_0 = const()[name = string("input_783_mode_0"), val = string("constant")];
+            fp16 const_231_to_fp16 = const()[name = string("const_231_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_783_cast_fp16 = pad(constant_val = const_231_to_fp16, mode = input_783_mode_0, pad = input_783_pad_0, x = input_781_cast_fp16)[name = string("input_783_cast_fp16")];
+            string input_785_pad_type_0 = const()[name = string("input_785_pad_type_0"), val = string("valid")];
+            int32 input_785_groups_0 = const()[name = string("input_785_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_785_strides_0 = const()[name = string("input_785_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_785_pad_0 = const()[name = string("input_785_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_785_dilations_0 = const()[name = string("input_785_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_350_to_fp16 = const()[name = string("const_350_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724656896)))];
+            tensor<fp16, [1024]> const_351_to_fp16 = const()[name = string("const_351_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724675392)))];
+            tensor<fp16, [1, 1024, 188]> input_787_cast_fp16 = conv(bias = const_351_to_fp16, dilations = input_785_dilations_0, groups = input_785_groups_0, pad = input_785_pad_0, pad_type = input_785_pad_type_0, strides = input_785_strides_0, weight = const_350_to_fp16, x = input_783_cast_fp16)[name = string("input_787_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_789_cast_fp16 = silu(x = input_787_cast_fp16)[name = string("input_789_cast_fp16")];
+            string x_349_pad_type_0 = const()[name = string("x_349_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_349_strides_0 = const()[name = string("x_349_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_349_pad_0 = const()[name = string("x_349_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_349_dilations_0 = const()[name = string("x_349_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_349_groups_0 = const()[name = string("x_349_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724677504)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(726774720)))];
+            tensor<fp16, [1, 1024, 188]> x_349_cast_fp16 = conv(bias = encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16, dilations = x_349_dilations_0, groups = x_349_groups_0, pad = x_349_pad_0, pad_type = x_349_pad_type_0, strides = x_349_strides_0, weight = encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16, x = input_789_cast_fp16)[name = string("x_349_cast_fp16")];
+            tensor<int32, [3]> input_791_perm_0 = const()[name = string("input_791_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_791_cast_fp16 = transpose(perm = input_791_perm_0, x = x_349_cast_fp16)[name = string("transpose_184")];
+            tensor<fp16, [1, 188, 1024]> input_793_cast_fp16 = add(x = input_775_cast_fp16, y = input_791_cast_fp16)[name = string("input_793_cast_fp16")];
+            tensor<int32, [1]> input_795_axes_0 = const()[name = string("input_795_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(726776832)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(726778944)))];
+            tensor<fp16, [1, 188, 1024]> input_795_cast_fp16 = layer_norm(axes = input_795_axes_0, beta = encoder_module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_793_cast_fp16)[name = string("input_795_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(726781056)))];
+            tensor<fp16, [4096]> encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(735169728)))];
+            tensor<fp16, [1, 188, 4096]> linear_134_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16, x = input_795_cast_fp16)[name = string("linear_134_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_799_cast_fp16 = silu(x = linear_134_cast_fp16)[name = string("input_799_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(735177984)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(743566656)))];
+            tensor<fp16, [1, 188, 1024]> linear_135_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16, x = input_799_cast_fp16)[name = string("linear_135_cast_fp16")];
+            fp16 var_3086_to_fp16 = const()[name = string("op_3086_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3087_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_3086_to_fp16)[name = string("op_3087_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_805_cast_fp16 = add(x = input_793_cast_fp16, y = var_3087_cast_fp16)[name = string("input_805_cast_fp16")];
+            tensor<int32, [1]> input_807_axes_0 = const()[name = string("input_807_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(743568768)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(743570880)))];
+            tensor<fp16, [1, 188, 1024]> input_807_cast_fp16 = layer_norm(axes = input_807_axes_0, beta = encoder_module_layers_14_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_out_weight_to_fp16, x = input_805_cast_fp16)[name = string("input_807_cast_fp16")];
+            tensor<int32, [1]> input_809_axes_0 = const()[name = string("input_809_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(743572992)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(743575104)))];
+            tensor<fp16, [1, 188, 1024]> input_809_cast_fp16 = layer_norm(axes = input_809_axes_0, beta = encoder_module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_807_cast_fp16)[name = string("input_809_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(743577216)))];
+            tensor<fp16, [4096]> encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(751965888)))];
+            tensor<fp16, [1, 188, 4096]> linear_136_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16, x = input_809_cast_fp16)[name = string("linear_136_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_813_cast_fp16 = silu(x = linear_136_cast_fp16)[name = string("input_813_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(751974144)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(760362816)))];
+            tensor<fp16, [1, 188, 1024]> linear_137_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16, x = input_813_cast_fp16)[name = string("linear_137_cast_fp16")];
+            fp16 var_3117_to_fp16 = const()[name = string("op_3117_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3118_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_3117_to_fp16)[name = string("op_3118_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_819_cast_fp16 = add(x = input_807_cast_fp16, y = var_3118_cast_fp16)[name = string("input_819_cast_fp16")];
+            tensor<int32, [1]> query_31_axes_0 = const()[name = string("query_31_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(760364928)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(760367040)))];
+            tensor<fp16, [1, 188, 1024]> query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = encoder_module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_self_att_weight_to_fp16, x = input_819_cast_fp16)[name = string("query_31_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(760369152)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(762466368)))];
+            tensor<fp16, [1, 188, 1024]> linear_138_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_q_weight_to_fp16, x = query_31_cast_fp16)[name = string("linear_138_cast_fp16")];
+            tensor<int32, [4]> var_3135 = const()[name = string("op_3135"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_91_cast_fp16 = reshape(shape = var_3135, x = linear_138_cast_fp16)[name = string("q_91_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(762468480)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(764565696)))];
+            tensor<fp16, [1, 188, 1024]> linear_139_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_k_weight_to_fp16, x = query_31_cast_fp16)[name = string("linear_139_cast_fp16")];
+            tensor<int32, [4]> var_3140 = const()[name = string("op_3140"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_61_cast_fp16 = reshape(shape = var_3140, x = linear_139_cast_fp16)[name = string("k_61_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(764567808)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766665024)))];
+            tensor<fp16, [1, 188, 1024]> linear_140_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_v_weight_to_fp16, x = query_31_cast_fp16)[name = string("linear_140_cast_fp16")];
+            tensor<int32, [4]> var_3145 = const()[name = string("op_3145"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_31_cast_fp16 = reshape(shape = var_3145, x = linear_140_cast_fp16)[name = string("v_31_cast_fp16")];
+            tensor<int32, [4]> value_35_perm_0 = const()[name = string("value_35_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766667136)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3157_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_module_layers_15_self_attn_pos_bias_u_to_fp16)[name = string("op_3157_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766669248)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3159_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_module_layers_15_self_attn_pos_bias_v_to_fp16)[name = string("op_3159_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_31_perm_0 = const()[name = string("q_with_bias_v_31_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_357_transpose_x_0 = const()[name = string("x_357_transpose_x_0"), val = bool(false)];
+            bool x_357_transpose_y_0 = const()[name = string("x_357_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_3161_to_fp16 = const()[name = string("op_3161_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766671360)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_3159_cast_fp16)[name = string("transpose_182")];
+            tensor<fp16, [1, 8, 188, 375]> x_357_cast_fp16 = matmul(transpose_x = x_357_transpose_x_0, transpose_y = x_357_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = var_3161_to_fp16)[name = string("x_357_cast_fp16")];
+            tensor<int32, [8]> x_359_pad_0 = const()[name = string("x_359_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_359_mode_0 = const()[name = string("x_359_mode_0"), val = string("constant")];
+            fp16 const_238_to_fp16 = const()[name = string("const_238_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_359_cast_fp16 = pad(constant_val = const_238_to_fp16, mode = x_359_mode_0, pad = x_359_pad_0, x = x_357_cast_fp16)[name = string("x_359_cast_fp16")];
+            tensor<int32, [4]> var_3169 = const()[name = string("op_3169"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_361_cast_fp16 = reshape(shape = var_3169, x = x_359_cast_fp16)[name = string("x_361_cast_fp16")];
+            tensor<int32, [4]> var_3173_begin_0 = const()[name = string("op_3173_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3173_end_0 = const()[name = string("op_3173_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3173_end_mask_0 = const()[name = string("op_3173_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3173_cast_fp16 = slice_by_index(begin = var_3173_begin_0, end = var_3173_end_0, end_mask = var_3173_end_mask_0, x = x_361_cast_fp16)[name = string("op_3173_cast_fp16")];
+            tensor<int32, [4]> var_3174 = const()[name = string("op_3174"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_61_cast_fp16 = reshape(shape = var_3174, x = var_3173_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<int32, [4]> transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_103 = transpose(perm = transpose_103_perm_0, x = k_61_cast_fp16)[name = string("transpose_180")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_102 = transpose(perm = transpose_102_perm_0, x = var_3157_cast_fp16)[name = string("transpose_181")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_31_cast_fp16 = matmul(transpose_x = matrix_ac_31_transpose_x_0, transpose_y = matrix_ac_31_transpose_y_0, x = transpose_102, y = transpose_103)[name = string("matrix_ac_31_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_63_begin_0 = const()[name = string("matrix_bd_63_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_63_end_0 = const()[name = string("matrix_bd_63_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_63_end_mask_0 = const()[name = string("matrix_bd_63_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3183_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = string("op_3183_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<fp16, [1, 8, 188, 188]> _inversed_scores_61_cast_fp16 = mul(x = var_3183_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = string("_inversed_scores_61_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_63_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_15)[name = string("scores_63_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3189_cast_fp16 = softmax(axis = var_152, x = scores_63_cast_fp16)[name = string("op_3189_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_821_cast_fp16 = select(a = var_164_to_fp16, b = var_3189_cast_fp16, cond = mask_15)[name = string("input_821_cast_fp16")];
+            bool x_363_transpose_x_0 = const()[name = string("x_363_transpose_x_0"), val = bool(false)];
+            bool x_363_transpose_y_0 = const()[name = string("x_363_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_31_cast_fp16)[name = string("transpose_183")];
+            tensor<fp16, [1, 8, 188, 128]> x_363_cast_fp16 = matmul(transpose_x = x_363_transpose_x_0, transpose_y = x_363_transpose_y_0, x = input_821_cast_fp16, y = value_35_cast_fp16)[name = string("x_363_cast_fp16")];
+            tensor<int32, [4]> var_3193_perm_0 = const()[name = string("op_3193_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3194 = const()[name = string("op_3194"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3193_cast_fp16 = transpose(perm = var_3193_perm_0, x = x_363_cast_fp16)[name = string("transpose_179")];
+            tensor<fp16, [1, 188, 1024]> input_823_cast_fp16 = reshape(shape = var_3194, x = var_3193_cast_fp16)[name = string("input_823_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(767439424)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(769536640)))];
+            tensor<fp16, [1, 188, 1024]> linear_142_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_out_weight_to_fp16, x = input_823_cast_fp16)[name = string("linear_142_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_827_cast_fp16 = add(x = input_819_cast_fp16, y = linear_142_cast_fp16)[name = string("input_827_cast_fp16")];
+            tensor<int32, [1]> x_367_axes_0 = const()[name = string("x_367_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(769538752)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(769540864)))];
+            tensor<fp16, [1, 188, 1024]> x_367_cast_fp16 = layer_norm(axes = x_367_axes_0, beta = encoder_module_layers_15_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_conv_weight_to_fp16, x = input_827_cast_fp16)[name = string("x_367_cast_fp16")];
+            tensor<int32, [3]> input_829_perm_0 = const()[name = string("input_829_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_831_pad_type_0 = const()[name = string("input_831_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_831_strides_0 = const()[name = string("input_831_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_831_pad_0 = const()[name = string("input_831_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_831_dilations_0 = const()[name = string("input_831_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_831_groups_0 = const()[name = string("input_831_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_15_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_15_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(769542976)))];
+            tensor<fp16, [2048]> encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(773737344)))];
+            tensor<fp16, [1, 1024, 188]> input_829_cast_fp16 = transpose(perm = input_829_perm_0, x = x_367_cast_fp16)[name = string("transpose_178")];
+            tensor<fp16, [1, 2048, 188]> input_831_cast_fp16 = conv(bias = encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_15_conv_pointwise_conv1_weight_to_fp16, x = input_829_cast_fp16)[name = string("input_831_cast_fp16")];
+            int32 x_369_split_num_splits_0 = const()[name = string("x_369_split_num_splits_0"), val = int32(2)];
+            int32 x_369_split_axis_0 = const()[name = string("x_369_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_369_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_369_split_cast_fp16_1 = split(axis = x_369_split_axis_0, num_splits = x_369_split_num_splits_0, x = input_831_cast_fp16)[name = string("x_369_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_369_split_1_sigmoid_cast_fp16 = sigmoid(x = x_369_split_cast_fp16_1)[name = string("x_369_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_369_cast_fp16 = mul(x = x_369_split_cast_fp16_0, y = x_369_split_1_sigmoid_cast_fp16)[name = string("x_369_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_833_cast_fp16 = select(a = var_164_to_fp16, b = x_369_cast_fp16, cond = var_608)[name = string("input_833_cast_fp16")];
+            tensor<int32, [6]> input_835_pad_0 = const()[name = string("input_835_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_835_mode_0 = const()[name = string("input_835_mode_0"), val = string("constant")];
+            fp16 const_241_to_fp16 = const()[name = string("const_241_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_835_cast_fp16 = pad(constant_val = const_241_to_fp16, mode = input_835_mode_0, pad = input_835_pad_0, x = input_833_cast_fp16)[name = string("input_835_cast_fp16")];
+            string input_837_pad_type_0 = const()[name = string("input_837_pad_type_0"), val = string("valid")];
+            int32 input_837_groups_0 = const()[name = string("input_837_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_837_strides_0 = const()[name = string("input_837_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_837_pad_0 = const()[name = string("input_837_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_837_dilations_0 = const()[name = string("input_837_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_352_to_fp16 = const()[name = string("const_352_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(773741504)))];
+            tensor<fp16, [1024]> const_353_to_fp16 = const()[name = string("const_353_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(773760000)))];
+            tensor<fp16, [1, 1024, 188]> input_839_cast_fp16 = conv(bias = const_353_to_fp16, dilations = input_837_dilations_0, groups = input_837_groups_0, pad = input_837_pad_0, pad_type = input_837_pad_type_0, strides = input_837_strides_0, weight = const_352_to_fp16, x = input_835_cast_fp16)[name = string("input_839_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_841_cast_fp16 = silu(x = input_839_cast_fp16)[name = string("input_841_cast_fp16")];
+            string x_371_pad_type_0 = const()[name = string("x_371_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_371_strides_0 = const()[name = string("x_371_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_371_pad_0 = const()[name = string("x_371_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_371_dilations_0 = const()[name = string("x_371_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_371_groups_0 = const()[name = string("x_371_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(773762112)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(775859328)))];
+            tensor<fp16, [1, 1024, 188]> x_371_cast_fp16 = conv(bias = encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16, dilations = x_371_dilations_0, groups = x_371_groups_0, pad = x_371_pad_0, pad_type = x_371_pad_type_0, strides = x_371_strides_0, weight = encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16, x = input_841_cast_fp16)[name = string("x_371_cast_fp16")];
+            tensor<int32, [3]> input_843_perm_0 = const()[name = string("input_843_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_843_cast_fp16 = transpose(perm = input_843_perm_0, x = x_371_cast_fp16)[name = string("transpose_177")];
+            tensor<fp16, [1, 188, 1024]> input_845_cast_fp16 = add(x = input_827_cast_fp16, y = input_843_cast_fp16)[name = string("input_845_cast_fp16")];
+            tensor<int32, [1]> input_847_axes_0 = const()[name = string("input_847_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(775861440)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(775863552)))];
+            tensor<fp16, [1, 188, 1024]> input_847_cast_fp16 = layer_norm(axes = input_847_axes_0, beta = encoder_module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_845_cast_fp16)[name = string("input_847_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(775865664)))];
+            tensor<fp16, [4096]> encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(784254336)))];
+            tensor<fp16, [1, 188, 4096]> linear_143_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16, x = input_847_cast_fp16)[name = string("linear_143_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_851_cast_fp16 = silu(x = linear_143_cast_fp16)[name = string("input_851_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(784262592)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(792651264)))];
+            tensor<fp16, [1, 188, 1024]> linear_144_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16, x = input_851_cast_fp16)[name = string("linear_144_cast_fp16")];
+            fp16 var_3260_to_fp16 = const()[name = string("op_3260_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3261_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_3260_to_fp16)[name = string("op_3261_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_857_cast_fp16 = add(x = input_845_cast_fp16, y = var_3261_cast_fp16)[name = string("input_857_cast_fp16")];
+            tensor<int32, [1]> input_859_axes_0 = const()[name = string("input_859_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(792653376)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(792655488)))];
+            tensor<fp16, [1, 188, 1024]> input_859_cast_fp16 = layer_norm(axes = input_859_axes_0, beta = encoder_module_layers_15_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_out_weight_to_fp16, x = input_857_cast_fp16)[name = string("input_859_cast_fp16")];
+            tensor<int32, [1]> input_861_axes_0 = const()[name = string("input_861_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(792657600)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(792659712)))];
+            tensor<fp16, [1, 188, 1024]> input_861_cast_fp16 = layer_norm(axes = input_861_axes_0, beta = encoder_module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_859_cast_fp16)[name = string("input_861_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(792661824)))];
+            tensor<fp16, [4096]> encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(801050496)))];
+            tensor<fp16, [1, 188, 4096]> linear_145_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16, x = input_861_cast_fp16)[name = string("linear_145_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_865_cast_fp16 = silu(x = linear_145_cast_fp16)[name = string("input_865_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(801058752)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(809447424)))];
+            tensor<fp16, [1, 188, 1024]> linear_146_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16, x = input_865_cast_fp16)[name = string("linear_146_cast_fp16")];
+            fp16 var_3291_to_fp16 = const()[name = string("op_3291_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3292_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3291_to_fp16)[name = string("op_3292_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_871_cast_fp16 = add(x = input_859_cast_fp16, y = var_3292_cast_fp16)[name = string("input_871_cast_fp16")];
+            tensor<int32, [1]> query_33_axes_0 = const()[name = string("query_33_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(809449536)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(809451648)))];
+            tensor<fp16, [1, 188, 1024]> query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = encoder_module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_self_att_weight_to_fp16, x = input_871_cast_fp16)[name = string("query_33_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(809453760)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(811550976)))];
+            tensor<fp16, [1, 188, 1024]> linear_147_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_q_weight_to_fp16, x = query_33_cast_fp16)[name = string("linear_147_cast_fp16")];
+            tensor<int32, [4]> var_3309 = const()[name = string("op_3309"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_97_cast_fp16 = reshape(shape = var_3309, x = linear_147_cast_fp16)[name = string("q_97_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(811553088)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813650304)))];
+            tensor<fp16, [1, 188, 1024]> linear_148_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_k_weight_to_fp16, x = query_33_cast_fp16)[name = string("linear_148_cast_fp16")];
+            tensor<int32, [4]> var_3314 = const()[name = string("op_3314"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_65_cast_fp16 = reshape(shape = var_3314, x = linear_148_cast_fp16)[name = string("k_65_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813652416)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(815749632)))];
+            tensor<fp16, [1, 188, 1024]> linear_149_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_v_weight_to_fp16, x = query_33_cast_fp16)[name = string("linear_149_cast_fp16")];
+            tensor<int32, [4]> var_3319 = const()[name = string("op_3319"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_33_cast_fp16 = reshape(shape = var_3319, x = linear_149_cast_fp16)[name = string("v_33_cast_fp16")];
+            tensor<int32, [4]> value_37_perm_0 = const()[name = string("value_37_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(815751744)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3331_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_module_layers_16_self_attn_pos_bias_u_to_fp16)[name = string("op_3331_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(815753856)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3333_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_module_layers_16_self_attn_pos_bias_v_to_fp16)[name = string("op_3333_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_33_perm_0 = const()[name = string("q_with_bias_v_33_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_379_transpose_x_0 = const()[name = string("x_379_transpose_x_0"), val = bool(false)];
+            bool x_379_transpose_y_0 = const()[name = string("x_379_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_3335_to_fp16 = const()[name = string("op_3335_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(815755968)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_3333_cast_fp16)[name = string("transpose_175")];
+            tensor<fp16, [1, 8, 188, 375]> x_379_cast_fp16 = matmul(transpose_x = x_379_transpose_x_0, transpose_y = x_379_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = var_3335_to_fp16)[name = string("x_379_cast_fp16")];
+            tensor<int32, [8]> x_381_pad_0 = const()[name = string("x_381_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_381_mode_0 = const()[name = string("x_381_mode_0"), val = string("constant")];
+            fp16 const_248_to_fp16 = const()[name = string("const_248_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_381_cast_fp16 = pad(constant_val = const_248_to_fp16, mode = x_381_mode_0, pad = x_381_pad_0, x = x_379_cast_fp16)[name = string("x_381_cast_fp16")];
+            tensor<int32, [4]> var_3343 = const()[name = string("op_3343"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_383_cast_fp16 = reshape(shape = var_3343, x = x_381_cast_fp16)[name = string("x_383_cast_fp16")];
+            tensor<int32, [4]> var_3347_begin_0 = const()[name = string("op_3347_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3347_end_0 = const()[name = string("op_3347_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3347_end_mask_0 = const()[name = string("op_3347_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3347_cast_fp16 = slice_by_index(begin = var_3347_begin_0, end = var_3347_end_0, end_mask = var_3347_end_mask_0, x = x_383_cast_fp16)[name = string("op_3347_cast_fp16")];
+            tensor<int32, [4]> var_3348 = const()[name = string("op_3348"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_65_cast_fp16 = reshape(shape = var_3348, x = var_3347_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<int32, [4]> transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_105 = transpose(perm = transpose_105_perm_0, x = k_65_cast_fp16)[name = string("transpose_173")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_104 = transpose(perm = transpose_104_perm_0, x = var_3331_cast_fp16)[name = string("transpose_174")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_33_cast_fp16 = matmul(transpose_x = matrix_ac_33_transpose_x_0, transpose_y = matrix_ac_33_transpose_y_0, x = transpose_104, y = transpose_105)[name = string("matrix_ac_33_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_67_begin_0 = const()[name = string("matrix_bd_67_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_67_end_0 = const()[name = string("matrix_bd_67_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_67_end_mask_0 = const()[name = string("matrix_bd_67_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3357_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = string("op_3357_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<fp16, [1, 8, 188, 188]> _inversed_scores_65_cast_fp16 = mul(x = var_3357_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = string("_inversed_scores_65_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_67_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_15)[name = string("scores_67_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3363_cast_fp16 = softmax(axis = var_152, x = scores_67_cast_fp16)[name = string("op_3363_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_873_cast_fp16 = select(a = var_164_to_fp16, b = var_3363_cast_fp16, cond = mask_15)[name = string("input_873_cast_fp16")];
+            bool x_385_transpose_x_0 = const()[name = string("x_385_transpose_x_0"), val = bool(false)];
+            bool x_385_transpose_y_0 = const()[name = string("x_385_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_33_cast_fp16)[name = string("transpose_176")];
+            tensor<fp16, [1, 8, 188, 128]> x_385_cast_fp16 = matmul(transpose_x = x_385_transpose_x_0, transpose_y = x_385_transpose_y_0, x = input_873_cast_fp16, y = value_37_cast_fp16)[name = string("x_385_cast_fp16")];
+            tensor<int32, [4]> var_3367_perm_0 = const()[name = string("op_3367_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3368 = const()[name = string("op_3368"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3367_cast_fp16 = transpose(perm = var_3367_perm_0, x = x_385_cast_fp16)[name = string("transpose_172")];
+            tensor<fp16, [1, 188, 1024]> input_875_cast_fp16 = reshape(shape = var_3368, x = var_3367_cast_fp16)[name = string("input_875_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(816524032)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(818621248)))];
+            tensor<fp16, [1, 188, 1024]> linear_151_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_out_weight_to_fp16, x = input_875_cast_fp16)[name = string("linear_151_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_879_cast_fp16 = add(x = input_871_cast_fp16, y = linear_151_cast_fp16)[name = string("input_879_cast_fp16")];
+            tensor<int32, [1]> x_389_axes_0 = const()[name = string("x_389_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(818623360)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(818625472)))];
+            tensor<fp16, [1, 188, 1024]> x_389_cast_fp16 = layer_norm(axes = x_389_axes_0, beta = encoder_module_layers_16_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_conv_weight_to_fp16, x = input_879_cast_fp16)[name = string("x_389_cast_fp16")];
+            tensor<int32, [3]> input_881_perm_0 = const()[name = string("input_881_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_883_pad_type_0 = const()[name = string("input_883_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_883_strides_0 = const()[name = string("input_883_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_883_pad_0 = const()[name = string("input_883_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_883_dilations_0 = const()[name = string("input_883_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_883_groups_0 = const()[name = string("input_883_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_16_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_16_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(818627584)))];
+            tensor<fp16, [2048]> encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(822821952)))];
+            tensor<fp16, [1, 1024, 188]> input_881_cast_fp16 = transpose(perm = input_881_perm_0, x = x_389_cast_fp16)[name = string("transpose_171")];
+            tensor<fp16, [1, 2048, 188]> input_883_cast_fp16 = conv(bias = encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_16_conv_pointwise_conv1_weight_to_fp16, x = input_881_cast_fp16)[name = string("input_883_cast_fp16")];
+            int32 x_391_split_num_splits_0 = const()[name = string("x_391_split_num_splits_0"), val = int32(2)];
+            int32 x_391_split_axis_0 = const()[name = string("x_391_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_391_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_391_split_cast_fp16_1 = split(axis = x_391_split_axis_0, num_splits = x_391_split_num_splits_0, x = input_883_cast_fp16)[name = string("x_391_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_391_split_1_sigmoid_cast_fp16 = sigmoid(x = x_391_split_cast_fp16_1)[name = string("x_391_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_391_cast_fp16 = mul(x = x_391_split_cast_fp16_0, y = x_391_split_1_sigmoid_cast_fp16)[name = string("x_391_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_885_cast_fp16 = select(a = var_164_to_fp16, b = x_391_cast_fp16, cond = var_608)[name = string("input_885_cast_fp16")];
+            tensor<int32, [6]> input_887_pad_0 = const()[name = string("input_887_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_887_mode_0 = const()[name = string("input_887_mode_0"), val = string("constant")];
+            fp16 const_251_to_fp16 = const()[name = string("const_251_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_887_cast_fp16 = pad(constant_val = const_251_to_fp16, mode = input_887_mode_0, pad = input_887_pad_0, x = input_885_cast_fp16)[name = string("input_887_cast_fp16")];
+            string input_889_pad_type_0 = const()[name = string("input_889_pad_type_0"), val = string("valid")];
+            int32 input_889_groups_0 = const()[name = string("input_889_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_889_strides_0 = const()[name = string("input_889_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_889_pad_0 = const()[name = string("input_889_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_889_dilations_0 = const()[name = string("input_889_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_354_to_fp16 = const()[name = string("const_354_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(822826112)))];
+            tensor<fp16, [1024]> const_355_to_fp16 = const()[name = string("const_355_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(822844608)))];
+            tensor<fp16, [1, 1024, 188]> input_891_cast_fp16 = conv(bias = const_355_to_fp16, dilations = input_889_dilations_0, groups = input_889_groups_0, pad = input_889_pad_0, pad_type = input_889_pad_type_0, strides = input_889_strides_0, weight = const_354_to_fp16, x = input_887_cast_fp16)[name = string("input_891_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_893_cast_fp16 = silu(x = input_891_cast_fp16)[name = string("input_893_cast_fp16")];
+            string x_393_pad_type_0 = const()[name = string("x_393_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_393_strides_0 = const()[name = string("x_393_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_393_pad_0 = const()[name = string("x_393_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_393_dilations_0 = const()[name = string("x_393_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_393_groups_0 = const()[name = string("x_393_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(822846720)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(824943936)))];
+            tensor<fp16, [1, 1024, 188]> x_393_cast_fp16 = conv(bias = encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16, dilations = x_393_dilations_0, groups = x_393_groups_0, pad = x_393_pad_0, pad_type = x_393_pad_type_0, strides = x_393_strides_0, weight = encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16, x = input_893_cast_fp16)[name = string("x_393_cast_fp16")];
+            tensor<int32, [3]> input_895_perm_0 = const()[name = string("input_895_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_895_cast_fp16 = transpose(perm = input_895_perm_0, x = x_393_cast_fp16)[name = string("transpose_170")];
+            tensor<fp16, [1, 188, 1024]> input_897_cast_fp16 = add(x = input_879_cast_fp16, y = input_895_cast_fp16)[name = string("input_897_cast_fp16")];
+            tensor<int32, [1]> input_899_axes_0 = const()[name = string("input_899_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(824946048)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(824948160)))];
+            tensor<fp16, [1, 188, 1024]> input_899_cast_fp16 = layer_norm(axes = input_899_axes_0, beta = encoder_module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_897_cast_fp16)[name = string("input_899_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(824950272)))];
+            tensor<fp16, [4096]> encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(833338944)))];
+            tensor<fp16, [1, 188, 4096]> linear_152_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16, x = input_899_cast_fp16)[name = string("linear_152_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_903_cast_fp16 = silu(x = linear_152_cast_fp16)[name = string("input_903_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(833347200)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(841735872)))];
+            tensor<fp16, [1, 188, 1024]> linear_153_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16, x = input_903_cast_fp16)[name = string("linear_153_cast_fp16")];
+            fp16 var_3434_to_fp16 = const()[name = string("op_3434_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3435_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_3434_to_fp16)[name = string("op_3435_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_909_cast_fp16 = add(x = input_897_cast_fp16, y = var_3435_cast_fp16)[name = string("input_909_cast_fp16")];
+            tensor<int32, [1]> input_911_axes_0 = const()[name = string("input_911_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(841737984)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(841740096)))];
+            tensor<fp16, [1, 188, 1024]> input_911_cast_fp16 = layer_norm(axes = input_911_axes_0, beta = encoder_module_layers_16_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_out_weight_to_fp16, x = input_909_cast_fp16)[name = string("input_911_cast_fp16")];
+            tensor<int32, [1]> input_913_axes_0 = const()[name = string("input_913_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(841742208)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(841744320)))];
+            tensor<fp16, [1, 188, 1024]> input_913_cast_fp16 = layer_norm(axes = input_913_axes_0, beta = encoder_module_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_feed_forward1_weight_to_fp16, x = input_911_cast_fp16)[name = string("input_913_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(841746432)))];
+            tensor<fp16, [4096]> encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(850135104)))];
+            tensor<fp16, [1, 188, 4096]> linear_154_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16, x = input_913_cast_fp16)[name = string("linear_154_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_917_cast_fp16 = silu(x = linear_154_cast_fp16)[name = string("input_917_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(850143360)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(858532032)))];
+            tensor<fp16, [1, 188, 1024]> linear_155_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16, x = input_917_cast_fp16)[name = string("linear_155_cast_fp16")];
+            fp16 var_3465_to_fp16 = const()[name = string("op_3465_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3466_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_3465_to_fp16)[name = string("op_3466_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_923_cast_fp16 = add(x = input_911_cast_fp16, y = var_3466_cast_fp16)[name = string("input_923_cast_fp16")];
+            tensor<int32, [1]> query_35_axes_0 = const()[name = string("query_35_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(858534144)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(858536256)))];
+            tensor<fp16, [1, 188, 1024]> query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = encoder_module_layers_17_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_self_att_weight_to_fp16, x = input_923_cast_fp16)[name = string("query_35_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(858538368)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860635584)))];
+            tensor<fp16, [1, 188, 1024]> linear_156_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_q_weight_to_fp16, x = query_35_cast_fp16)[name = string("linear_156_cast_fp16")];
+            tensor<int32, [4]> var_3483 = const()[name = string("op_3483"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_103_cast_fp16 = reshape(shape = var_3483, x = linear_156_cast_fp16)[name = string("q_103_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860637696)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(862734912)))];
+            tensor<fp16, [1, 188, 1024]> linear_157_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_k_weight_to_fp16, x = query_35_cast_fp16)[name = string("linear_157_cast_fp16")];
+            tensor<int32, [4]> var_3488 = const()[name = string("op_3488"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_69_cast_fp16 = reshape(shape = var_3488, x = linear_157_cast_fp16)[name = string("k_69_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(862737024)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864834240)))];
+            tensor<fp16, [1, 188, 1024]> linear_158_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_v_weight_to_fp16, x = query_35_cast_fp16)[name = string("linear_158_cast_fp16")];
+            tensor<int32, [4]> var_3493 = const()[name = string("op_3493"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_35_cast_fp16 = reshape(shape = var_3493, x = linear_158_cast_fp16)[name = string("v_35_cast_fp16")];
+            tensor<int32, [4]> value_39_perm_0 = const()[name = string("value_39_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864836352)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3505_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_module_layers_17_self_attn_pos_bias_u_to_fp16)[name = string("op_3505_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864838464)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3507_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_module_layers_17_self_attn_pos_bias_v_to_fp16)[name = string("op_3507_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_35_perm_0 = const()[name = string("q_with_bias_v_35_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_401_transpose_x_0 = const()[name = string("x_401_transpose_x_0"), val = bool(false)];
+            bool x_401_transpose_y_0 = const()[name = string("x_401_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_3509_to_fp16 = const()[name = string("op_3509_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864840576)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3507_cast_fp16)[name = string("transpose_168")];
+            tensor<fp16, [1, 8, 188, 375]> x_401_cast_fp16 = matmul(transpose_x = x_401_transpose_x_0, transpose_y = x_401_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = var_3509_to_fp16)[name = string("x_401_cast_fp16")];
+            tensor<int32, [8]> x_403_pad_0 = const()[name = string("x_403_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_403_mode_0 = const()[name = string("x_403_mode_0"), val = string("constant")];
+            fp16 const_258_to_fp16 = const()[name = string("const_258_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_403_cast_fp16 = pad(constant_val = const_258_to_fp16, mode = x_403_mode_0, pad = x_403_pad_0, x = x_401_cast_fp16)[name = string("x_403_cast_fp16")];
+            tensor<int32, [4]> var_3517 = const()[name = string("op_3517"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_405_cast_fp16 = reshape(shape = var_3517, x = x_403_cast_fp16)[name = string("x_405_cast_fp16")];
+            tensor<int32, [4]> var_3521_begin_0 = const()[name = string("op_3521_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3521_end_0 = const()[name = string("op_3521_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3521_end_mask_0 = const()[name = string("op_3521_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3521_cast_fp16 = slice_by_index(begin = var_3521_begin_0, end = var_3521_end_0, end_mask = var_3521_end_mask_0, x = x_405_cast_fp16)[name = string("op_3521_cast_fp16")];
+            tensor<int32, [4]> var_3522 = const()[name = string("op_3522"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_69_cast_fp16 = reshape(shape = var_3522, x = var_3521_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<int32, [4]> transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_107 = transpose(perm = transpose_107_perm_0, x = k_69_cast_fp16)[name = string("transpose_166")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_106 = transpose(perm = transpose_106_perm_0, x = var_3505_cast_fp16)[name = string("transpose_167")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_35_cast_fp16 = matmul(transpose_x = matrix_ac_35_transpose_x_0, transpose_y = matrix_ac_35_transpose_y_0, x = transpose_106, y = transpose_107)[name = string("matrix_ac_35_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_71_begin_0 = const()[name = string("matrix_bd_71_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_71_end_0 = const()[name = string("matrix_bd_71_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_71_end_mask_0 = const()[name = string("matrix_bd_71_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3531_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = string("op_3531_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<fp16, [1, 8, 188, 188]> _inversed_scores_69_cast_fp16 = mul(x = var_3531_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = string("_inversed_scores_69_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_71_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_15)[name = string("scores_71_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3537_cast_fp16 = softmax(axis = var_152, x = scores_71_cast_fp16)[name = string("op_3537_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_925_cast_fp16 = select(a = var_164_to_fp16, b = var_3537_cast_fp16, cond = mask_15)[name = string("input_925_cast_fp16")];
+            bool x_407_transpose_x_0 = const()[name = string("x_407_transpose_x_0"), val = bool(false)];
+            bool x_407_transpose_y_0 = const()[name = string("x_407_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_35_cast_fp16)[name = string("transpose_169")];
+            tensor<fp16, [1, 8, 188, 128]> x_407_cast_fp16 = matmul(transpose_x = x_407_transpose_x_0, transpose_y = x_407_transpose_y_0, x = input_925_cast_fp16, y = value_39_cast_fp16)[name = string("x_407_cast_fp16")];
+            tensor<int32, [4]> var_3541_perm_0 = const()[name = string("op_3541_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3542 = const()[name = string("op_3542"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3541_cast_fp16 = transpose(perm = var_3541_perm_0, x = x_407_cast_fp16)[name = string("transpose_165")];
+            tensor<fp16, [1, 188, 1024]> input_927_cast_fp16 = reshape(shape = var_3542, x = var_3541_cast_fp16)[name = string("input_927_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(865608640)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(867705856)))];
+            tensor<fp16, [1, 188, 1024]> linear_160_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_out_weight_to_fp16, x = input_927_cast_fp16)[name = string("linear_160_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_931_cast_fp16 = add(x = input_923_cast_fp16, y = linear_160_cast_fp16)[name = string("input_931_cast_fp16")];
+            tensor<int32, [1]> x_411_axes_0 = const()[name = string("x_411_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(867707968)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(867710080)))];
+            tensor<fp16, [1, 188, 1024]> x_411_cast_fp16 = layer_norm(axes = x_411_axes_0, beta = encoder_module_layers_17_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_conv_weight_to_fp16, x = input_931_cast_fp16)[name = string("x_411_cast_fp16")];
+            tensor<int32, [3]> input_933_perm_0 = const()[name = string("input_933_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_935_pad_type_0 = const()[name = string("input_935_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_935_strides_0 = const()[name = string("input_935_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_935_pad_0 = const()[name = string("input_935_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_935_dilations_0 = const()[name = string("input_935_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_935_groups_0 = const()[name = string("input_935_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_17_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_17_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(867712192)))];
+            tensor<fp16, [2048]> encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(871906560)))];
+            tensor<fp16, [1, 1024, 188]> input_933_cast_fp16 = transpose(perm = input_933_perm_0, x = x_411_cast_fp16)[name = string("transpose_164")];
+            tensor<fp16, [1, 2048, 188]> input_935_cast_fp16 = conv(bias = encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_17_conv_pointwise_conv1_weight_to_fp16, x = input_933_cast_fp16)[name = string("input_935_cast_fp16")];
+            int32 x_413_split_num_splits_0 = const()[name = string("x_413_split_num_splits_0"), val = int32(2)];
+            int32 x_413_split_axis_0 = const()[name = string("x_413_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_413_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_413_split_cast_fp16_1 = split(axis = x_413_split_axis_0, num_splits = x_413_split_num_splits_0, x = input_935_cast_fp16)[name = string("x_413_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_413_split_1_sigmoid_cast_fp16 = sigmoid(x = x_413_split_cast_fp16_1)[name = string("x_413_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_413_cast_fp16 = mul(x = x_413_split_cast_fp16_0, y = x_413_split_1_sigmoid_cast_fp16)[name = string("x_413_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_937_cast_fp16 = select(a = var_164_to_fp16, b = x_413_cast_fp16, cond = var_608)[name = string("input_937_cast_fp16")];
+            tensor<int32, [6]> input_939_pad_0 = const()[name = string("input_939_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_939_mode_0 = const()[name = string("input_939_mode_0"), val = string("constant")];
+            fp16 const_261_to_fp16 = const()[name = string("const_261_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_939_cast_fp16 = pad(constant_val = const_261_to_fp16, mode = input_939_mode_0, pad = input_939_pad_0, x = input_937_cast_fp16)[name = string("input_939_cast_fp16")];
+            string input_941_pad_type_0 = const()[name = string("input_941_pad_type_0"), val = string("valid")];
+            int32 input_941_groups_0 = const()[name = string("input_941_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_941_strides_0 = const()[name = string("input_941_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_941_pad_0 = const()[name = string("input_941_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_941_dilations_0 = const()[name = string("input_941_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_356_to_fp16 = const()[name = string("const_356_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(871910720)))];
+            tensor<fp16, [1024]> const_357_to_fp16 = const()[name = string("const_357_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(871929216)))];
+            tensor<fp16, [1, 1024, 188]> input_943_cast_fp16 = conv(bias = const_357_to_fp16, dilations = input_941_dilations_0, groups = input_941_groups_0, pad = input_941_pad_0, pad_type = input_941_pad_type_0, strides = input_941_strides_0, weight = const_356_to_fp16, x = input_939_cast_fp16)[name = string("input_943_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_945_cast_fp16 = silu(x = input_943_cast_fp16)[name = string("input_945_cast_fp16")];
+            string x_415_pad_type_0 = const()[name = string("x_415_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_415_strides_0 = const()[name = string("x_415_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_415_pad_0 = const()[name = string("x_415_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_415_dilations_0 = const()[name = string("x_415_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_415_groups_0 = const()[name = string("x_415_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_17_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_17_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(871931328)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(874028544)))];
+            tensor<fp16, [1, 1024, 188]> x_415_cast_fp16 = conv(bias = encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_17_conv_pointwise_conv2_weight_to_fp16, x = input_945_cast_fp16)[name = string("x_415_cast_fp16")];
+            tensor<int32, [3]> input_947_perm_0 = const()[name = string("input_947_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_947_cast_fp16 = transpose(perm = input_947_perm_0, x = x_415_cast_fp16)[name = string("transpose_163")];
+            tensor<fp16, [1, 188, 1024]> input_949_cast_fp16 = add(x = input_931_cast_fp16, y = input_947_cast_fp16)[name = string("input_949_cast_fp16")];
+            tensor<int32, [1]> input_951_axes_0 = const()[name = string("input_951_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(874030656)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(874032768)))];
+            tensor<fp16, [1, 188, 1024]> input_951_cast_fp16 = layer_norm(axes = input_951_axes_0, beta = encoder_module_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_feed_forward2_weight_to_fp16, x = input_949_cast_fp16)[name = string("input_951_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(874034880)))];
+            tensor<fp16, [4096]> encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(882423552)))];
+            tensor<fp16, [1, 188, 4096]> linear_161_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16, x = input_951_cast_fp16)[name = string("linear_161_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_955_cast_fp16 = silu(x = linear_161_cast_fp16)[name = string("input_955_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(882431808)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(890820480)))];
+            tensor<fp16, [1, 188, 1024]> linear_162_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16, x = input_955_cast_fp16)[name = string("linear_162_cast_fp16")];
+            fp16 var_3608_to_fp16 = const()[name = string("op_3608_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3609_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_3608_to_fp16)[name = string("op_3609_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_961_cast_fp16 = add(x = input_949_cast_fp16, y = var_3609_cast_fp16)[name = string("input_961_cast_fp16")];
+            tensor<int32, [1]> input_963_axes_0 = const()[name = string("input_963_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(890822592)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(890824704)))];
+            tensor<fp16, [1, 188, 1024]> input_963_cast_fp16 = layer_norm(axes = input_963_axes_0, beta = encoder_module_layers_17_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_out_weight_to_fp16, x = input_961_cast_fp16)[name = string("input_963_cast_fp16")];
+            tensor<int32, [1]> input_965_axes_0 = const()[name = string("input_965_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(890826816)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(890828928)))];
+            tensor<fp16, [1, 188, 1024]> input_965_cast_fp16 = layer_norm(axes = input_965_axes_0, beta = encoder_module_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_feed_forward1_weight_to_fp16, x = input_963_cast_fp16)[name = string("input_965_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(890831040)))];
+            tensor<fp16, [4096]> encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(899219712)))];
+            tensor<fp16, [1, 188, 4096]> linear_163_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16, x = input_965_cast_fp16)[name = string("linear_163_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_969_cast_fp16 = silu(x = linear_163_cast_fp16)[name = string("input_969_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(899227968)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907616640)))];
+            tensor<fp16, [1, 188, 1024]> linear_164_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16, x = input_969_cast_fp16)[name = string("linear_164_cast_fp16")];
+            fp16 var_3639_to_fp16 = const()[name = string("op_3639_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3640_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3639_to_fp16)[name = string("op_3640_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_975_cast_fp16 = add(x = input_963_cast_fp16, y = var_3640_cast_fp16)[name = string("input_975_cast_fp16")];
+            tensor<int32, [1]> query_37_axes_0 = const()[name = string("query_37_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907618752)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907620864)))];
+            tensor<fp16, [1, 188, 1024]> query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = encoder_module_layers_18_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_self_att_weight_to_fp16, x = input_975_cast_fp16)[name = string("query_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907622976)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(909720192)))];
+            tensor<fp16, [1, 188, 1024]> linear_165_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_q_weight_to_fp16, x = query_37_cast_fp16)[name = string("linear_165_cast_fp16")];
+            tensor<int32, [4]> var_3657 = const()[name = string("op_3657"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_109_cast_fp16 = reshape(shape = var_3657, x = linear_165_cast_fp16)[name = string("q_109_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(909722304)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911819520)))];
+            tensor<fp16, [1, 188, 1024]> linear_166_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_k_weight_to_fp16, x = query_37_cast_fp16)[name = string("linear_166_cast_fp16")];
+            tensor<int32, [4]> var_3662 = const()[name = string("op_3662"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_73_cast_fp16 = reshape(shape = var_3662, x = linear_166_cast_fp16)[name = string("k_73_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911821632)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(913918848)))];
+            tensor<fp16, [1, 188, 1024]> linear_167_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_v_weight_to_fp16, x = query_37_cast_fp16)[name = string("linear_167_cast_fp16")];
+            tensor<int32, [4]> var_3667 = const()[name = string("op_3667"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_37_cast_fp16 = reshape(shape = var_3667, x = linear_167_cast_fp16)[name = string("v_37_cast_fp16")];
+            tensor<int32, [4]> value_41_perm_0 = const()[name = string("value_41_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(913920960)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3679_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_module_layers_18_self_attn_pos_bias_u_to_fp16)[name = string("op_3679_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(913923072)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3681_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_module_layers_18_self_attn_pos_bias_v_to_fp16)[name = string("op_3681_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_37_perm_0 = const()[name = string("q_with_bias_v_37_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            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<fp16, [1, 8, 128, 375]> var_3683_to_fp16 = const()[name = string("op_3683_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(913925184)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3681_cast_fp16)[name = string("transpose_161")];
+            tensor<fp16, [1, 8, 188, 375]> x_423_cast_fp16 = matmul(transpose_x = x_423_transpose_x_0, transpose_y = x_423_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = var_3683_to_fp16)[name = string("x_423_cast_fp16")];
+            tensor<int32, [8]> x_425_pad_0 = const()[name = string("x_425_pad_0"), val = tensor<int32, [8]>([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_268_to_fp16 = const()[name = string("const_268_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_425_cast_fp16 = pad(constant_val = const_268_to_fp16, mode = x_425_mode_0, pad = x_425_pad_0, x = x_423_cast_fp16)[name = string("x_425_cast_fp16")];
+            tensor<int32, [4]> var_3691 = const()[name = string("op_3691"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_427_cast_fp16 = reshape(shape = var_3691, x = x_425_cast_fp16)[name = string("x_427_cast_fp16")];
+            tensor<int32, [4]> var_3695_begin_0 = const()[name = string("op_3695_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3695_end_0 = const()[name = string("op_3695_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3695_end_mask_0 = const()[name = string("op_3695_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3695_cast_fp16 = slice_by_index(begin = var_3695_begin_0, end = var_3695_end_0, end_mask = var_3695_end_mask_0, x = x_427_cast_fp16)[name = string("op_3695_cast_fp16")];
+            tensor<int32, [4]> var_3696 = const()[name = string("op_3696"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_73_cast_fp16 = reshape(shape = var_3696, x = var_3695_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<int32, [4]> transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_109 = transpose(perm = transpose_109_perm_0, x = k_73_cast_fp16)[name = string("transpose_159")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_108 = transpose(perm = transpose_108_perm_0, x = var_3679_cast_fp16)[name = string("transpose_160")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_37_cast_fp16 = matmul(transpose_x = matrix_ac_37_transpose_x_0, transpose_y = matrix_ac_37_transpose_y_0, x = transpose_108, y = transpose_109)[name = string("matrix_ac_37_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_75_begin_0 = const()[name = string("matrix_bd_75_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_75_end_0 = const()[name = string("matrix_bd_75_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_75_end_mask_0 = const()[name = string("matrix_bd_75_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3705_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = string("op_3705_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<fp16, [1, 8, 188, 188]> _inversed_scores_73_cast_fp16 = mul(x = var_3705_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = string("_inversed_scores_73_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_75_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_15)[name = string("scores_75_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3711_cast_fp16 = softmax(axis = var_152, x = scores_75_cast_fp16)[name = string("op_3711_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_977_cast_fp16 = select(a = var_164_to_fp16, b = var_3711_cast_fp16, cond = mask_15)[name = string("input_977_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<fp16, [1, 8, 188, 128]> value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_37_cast_fp16)[name = string("transpose_162")];
+            tensor<fp16, [1, 8, 188, 128]> x_429_cast_fp16 = matmul(transpose_x = x_429_transpose_x_0, transpose_y = x_429_transpose_y_0, x = input_977_cast_fp16, y = value_41_cast_fp16)[name = string("x_429_cast_fp16")];
+            tensor<int32, [4]> var_3715_perm_0 = const()[name = string("op_3715_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3716 = const()[name = string("op_3716"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3715_cast_fp16 = transpose(perm = var_3715_perm_0, x = x_429_cast_fp16)[name = string("transpose_158")];
+            tensor<fp16, [1, 188, 1024]> input_979_cast_fp16 = reshape(shape = var_3716, x = var_3715_cast_fp16)[name = string("input_979_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(914693248)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(916790464)))];
+            tensor<fp16, [1, 188, 1024]> linear_169_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_out_weight_to_fp16, x = input_979_cast_fp16)[name = string("linear_169_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_983_cast_fp16 = add(x = input_975_cast_fp16, y = linear_169_cast_fp16)[name = string("input_983_cast_fp16")];
+            tensor<int32, [1]> x_433_axes_0 = const()[name = string("x_433_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(916792576)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(916794688)))];
+            tensor<fp16, [1, 188, 1024]> x_433_cast_fp16 = layer_norm(axes = x_433_axes_0, beta = encoder_module_layers_18_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_conv_weight_to_fp16, x = input_983_cast_fp16)[name = string("x_433_cast_fp16")];
+            tensor<int32, [3]> input_985_perm_0 = const()[name = string("input_985_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_987_pad_type_0 = const()[name = string("input_987_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_987_strides_0 = const()[name = string("input_987_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_987_pad_0 = const()[name = string("input_987_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_987_dilations_0 = const()[name = string("input_987_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_987_groups_0 = const()[name = string("input_987_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_18_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_18_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(916796800)))];
+            tensor<fp16, [2048]> encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(920991168)))];
+            tensor<fp16, [1, 1024, 188]> input_985_cast_fp16 = transpose(perm = input_985_perm_0, x = x_433_cast_fp16)[name = string("transpose_157")];
+            tensor<fp16, [1, 2048, 188]> input_987_cast_fp16 = conv(bias = encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_18_conv_pointwise_conv1_weight_to_fp16, x = input_985_cast_fp16)[name = string("input_987_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<fp16, [1, 1024, 188]> x_435_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_435_split_cast_fp16_1 = split(axis = x_435_split_axis_0, num_splits = x_435_split_num_splits_0, x = input_987_cast_fp16)[name = string("x_435_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> input_989_cast_fp16 = select(a = var_164_to_fp16, b = x_435_cast_fp16, cond = var_608)[name = string("input_989_cast_fp16")];
+            tensor<int32, [6]> input_991_pad_0 = const()[name = string("input_991_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_991_mode_0 = const()[name = string("input_991_mode_0"), val = string("constant")];
+            fp16 const_271_to_fp16 = const()[name = string("const_271_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_991_cast_fp16 = pad(constant_val = const_271_to_fp16, mode = input_991_mode_0, pad = input_991_pad_0, x = input_989_cast_fp16)[name = string("input_991_cast_fp16")];
+            string input_993_pad_type_0 = const()[name = string("input_993_pad_type_0"), val = string("valid")];
+            int32 input_993_groups_0 = const()[name = string("input_993_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_993_strides_0 = const()[name = string("input_993_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_993_pad_0 = const()[name = string("input_993_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_993_dilations_0 = const()[name = string("input_993_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_358_to_fp16 = const()[name = string("const_358_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(920995328)))];
+            tensor<fp16, [1024]> const_359_to_fp16 = const()[name = string("const_359_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(921013824)))];
+            tensor<fp16, [1, 1024, 188]> input_995_cast_fp16 = conv(bias = const_359_to_fp16, dilations = input_993_dilations_0, groups = input_993_groups_0, pad = input_993_pad_0, pad_type = input_993_pad_type_0, strides = input_993_strides_0, weight = const_358_to_fp16, x = input_991_cast_fp16)[name = string("input_995_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_997_cast_fp16 = silu(x = input_995_cast_fp16)[name = string("input_997_cast_fp16")];
+            string x_437_pad_type_0 = const()[name = string("x_437_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_437_strides_0 = const()[name = string("x_437_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_437_pad_0 = const()[name = string("x_437_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_437_dilations_0 = const()[name = string("x_437_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_437_groups_0 = const()[name = string("x_437_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_18_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_18_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(921015936)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923113152)))];
+            tensor<fp16, [1, 1024, 188]> x_437_cast_fp16 = conv(bias = encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_18_conv_pointwise_conv2_weight_to_fp16, x = input_997_cast_fp16)[name = string("x_437_cast_fp16")];
+            tensor<int32, [3]> input_999_perm_0 = const()[name = string("input_999_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_999_cast_fp16 = transpose(perm = input_999_perm_0, x = x_437_cast_fp16)[name = string("transpose_156")];
+            tensor<fp16, [1, 188, 1024]> input_1001_cast_fp16 = add(x = input_983_cast_fp16, y = input_999_cast_fp16)[name = string("input_1001_cast_fp16")];
+            tensor<int32, [1]> input_1003_axes_0 = const()[name = string("input_1003_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923115264)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923117376)))];
+            tensor<fp16, [1, 188, 1024]> input_1003_cast_fp16 = layer_norm(axes = input_1003_axes_0, beta = encoder_module_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_feed_forward2_weight_to_fp16, x = input_1001_cast_fp16)[name = string("input_1003_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923119488)))];
+            tensor<fp16, [4096]> encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(931508160)))];
+            tensor<fp16, [1, 188, 4096]> linear_170_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16, x = input_1003_cast_fp16)[name = string("linear_170_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1007_cast_fp16 = silu(x = linear_170_cast_fp16)[name = string("input_1007_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(931516416)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(939905088)))];
+            tensor<fp16, [1, 188, 1024]> linear_171_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16, x = input_1007_cast_fp16)[name = string("linear_171_cast_fp16")];
+            fp16 var_3782_to_fp16 = const()[name = string("op_3782_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3783_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_3782_to_fp16)[name = string("op_3783_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1013_cast_fp16 = add(x = input_1001_cast_fp16, y = var_3783_cast_fp16)[name = string("input_1013_cast_fp16")];
+            tensor<int32, [1]> input_1015_axes_0 = const()[name = string("input_1015_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(939907200)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(939909312)))];
+            tensor<fp16, [1, 188, 1024]> input_1015_cast_fp16 = layer_norm(axes = input_1015_axes_0, beta = encoder_module_layers_18_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_out_weight_to_fp16, x = input_1013_cast_fp16)[name = string("input_1015_cast_fp16")];
+            tensor<int32, [1]> input_1017_axes_0 = const()[name = string("input_1017_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(939911424)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(939913536)))];
+            tensor<fp16, [1, 188, 1024]> input_1017_cast_fp16 = layer_norm(axes = input_1017_axes_0, beta = encoder_module_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1015_cast_fp16)[name = string("input_1017_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(939915648)))];
+            tensor<fp16, [4096]> encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(948304320)))];
+            tensor<fp16, [1, 188, 4096]> linear_172_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16, x = input_1017_cast_fp16)[name = string("linear_172_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1021_cast_fp16 = silu(x = linear_172_cast_fp16)[name = string("input_1021_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(948312576)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(956701248)))];
+            tensor<fp16, [1, 188, 1024]> linear_173_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16, x = input_1021_cast_fp16)[name = string("linear_173_cast_fp16")];
+            fp16 var_3813_to_fp16 = const()[name = string("op_3813_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3814_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3813_to_fp16)[name = string("op_3814_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1027_cast_fp16 = add(x = input_1015_cast_fp16, y = var_3814_cast_fp16)[name = string("input_1027_cast_fp16")];
+            tensor<int32, [1]> query_39_axes_0 = const()[name = string("query_39_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(956703360)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(956705472)))];
+            tensor<fp16, [1, 188, 1024]> query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = encoder_module_layers_19_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_self_att_weight_to_fp16, x = input_1027_cast_fp16)[name = string("query_39_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(956707584)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(958804800)))];
+            tensor<fp16, [1, 188, 1024]> linear_174_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_q_weight_to_fp16, x = query_39_cast_fp16)[name = string("linear_174_cast_fp16")];
+            tensor<int32, [4]> var_3831 = const()[name = string("op_3831"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_115_cast_fp16 = reshape(shape = var_3831, x = linear_174_cast_fp16)[name = string("q_115_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(958806912)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(960904128)))];
+            tensor<fp16, [1, 188, 1024]> linear_175_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_k_weight_to_fp16, x = query_39_cast_fp16)[name = string("linear_175_cast_fp16")];
+            tensor<int32, [4]> var_3836 = const()[name = string("op_3836"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_77_cast_fp16 = reshape(shape = var_3836, x = linear_175_cast_fp16)[name = string("k_77_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(960906240)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(963003456)))];
+            tensor<fp16, [1, 188, 1024]> linear_176_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_v_weight_to_fp16, x = query_39_cast_fp16)[name = string("linear_176_cast_fp16")];
+            tensor<int32, [4]> var_3841 = const()[name = string("op_3841"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_39_cast_fp16 = reshape(shape = var_3841, x = linear_176_cast_fp16)[name = string("v_39_cast_fp16")];
+            tensor<int32, [4]> value_43_perm_0 = const()[name = string("value_43_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(963005568)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3853_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_module_layers_19_self_attn_pos_bias_u_to_fp16)[name = string("op_3853_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(963007680)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3855_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_module_layers_19_self_attn_pos_bias_v_to_fp16)[name = string("op_3855_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_39_perm_0 = const()[name = string("q_with_bias_v_39_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_445_transpose_x_0 = const()[name = string("x_445_transpose_x_0"), val = bool(false)];
+            bool x_445_transpose_y_0 = const()[name = string("x_445_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_3857_to_fp16 = const()[name = string("op_3857_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(963009792)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3855_cast_fp16)[name = string("transpose_154")];
+            tensor<fp16, [1, 8, 188, 375]> x_445_cast_fp16 = matmul(transpose_x = x_445_transpose_x_0, transpose_y = x_445_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = var_3857_to_fp16)[name = string("x_445_cast_fp16")];
+            tensor<int32, [8]> x_447_pad_0 = const()[name = string("x_447_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_447_mode_0 = const()[name = string("x_447_mode_0"), val = string("constant")];
+            fp16 const_278_to_fp16 = const()[name = string("const_278_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_447_cast_fp16 = pad(constant_val = const_278_to_fp16, mode = x_447_mode_0, pad = x_447_pad_0, x = x_445_cast_fp16)[name = string("x_447_cast_fp16")];
+            tensor<int32, [4]> var_3865 = const()[name = string("op_3865"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_449_cast_fp16 = reshape(shape = var_3865, x = x_447_cast_fp16)[name = string("x_449_cast_fp16")];
+            tensor<int32, [4]> var_3869_begin_0 = const()[name = string("op_3869_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3869_end_0 = const()[name = string("op_3869_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3869_end_mask_0 = const()[name = string("op_3869_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3869_cast_fp16 = slice_by_index(begin = var_3869_begin_0, end = var_3869_end_0, end_mask = var_3869_end_mask_0, x = x_449_cast_fp16)[name = string("op_3869_cast_fp16")];
+            tensor<int32, [4]> var_3870 = const()[name = string("op_3870"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_77_cast_fp16 = reshape(shape = var_3870, x = var_3869_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<int32, [4]> transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_111 = transpose(perm = transpose_111_perm_0, x = k_77_cast_fp16)[name = string("transpose_152")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_110 = transpose(perm = transpose_110_perm_0, x = var_3853_cast_fp16)[name = string("transpose_153")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_39_cast_fp16 = matmul(transpose_x = matrix_ac_39_transpose_x_0, transpose_y = matrix_ac_39_transpose_y_0, x = transpose_110, y = transpose_111)[name = string("matrix_ac_39_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_79_begin_0 = const()[name = string("matrix_bd_79_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_79_end_0 = const()[name = string("matrix_bd_79_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_79_end_mask_0 = const()[name = string("matrix_bd_79_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3879_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = string("op_3879_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<fp16, [1, 8, 188, 188]> _inversed_scores_77_cast_fp16 = mul(x = var_3879_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = string("_inversed_scores_77_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_79_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_15)[name = string("scores_79_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3885_cast_fp16 = softmax(axis = var_152, x = scores_79_cast_fp16)[name = string("op_3885_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1029_cast_fp16 = select(a = var_164_to_fp16, b = var_3885_cast_fp16, cond = mask_15)[name = string("input_1029_cast_fp16")];
+            bool x_451_transpose_x_0 = const()[name = string("x_451_transpose_x_0"), val = bool(false)];
+            bool x_451_transpose_y_0 = const()[name = string("x_451_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_39_cast_fp16)[name = string("transpose_155")];
+            tensor<fp16, [1, 8, 188, 128]> x_451_cast_fp16 = matmul(transpose_x = x_451_transpose_x_0, transpose_y = x_451_transpose_y_0, x = input_1029_cast_fp16, y = value_43_cast_fp16)[name = string("x_451_cast_fp16")];
+            tensor<int32, [4]> var_3889_perm_0 = const()[name = string("op_3889_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3890 = const()[name = string("op_3890"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3889_cast_fp16 = transpose(perm = var_3889_perm_0, x = x_451_cast_fp16)[name = string("transpose_151")];
+            tensor<fp16, [1, 188, 1024]> input_1031_cast_fp16 = reshape(shape = var_3890, x = var_3889_cast_fp16)[name = string("input_1031_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(963777856)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(965875072)))];
+            tensor<fp16, [1, 188, 1024]> linear_178_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_out_weight_to_fp16, x = input_1031_cast_fp16)[name = string("linear_178_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1035_cast_fp16 = add(x = input_1027_cast_fp16, y = linear_178_cast_fp16)[name = string("input_1035_cast_fp16")];
+            tensor<int32, [1]> x_455_axes_0 = const()[name = string("x_455_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(965877184)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(965879296)))];
+            tensor<fp16, [1, 188, 1024]> x_455_cast_fp16 = layer_norm(axes = x_455_axes_0, beta = encoder_module_layers_19_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_conv_weight_to_fp16, x = input_1035_cast_fp16)[name = string("x_455_cast_fp16")];
+            tensor<int32, [3]> input_1037_perm_0 = const()[name = string("input_1037_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1039_pad_type_0 = const()[name = string("input_1039_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1039_strides_0 = const()[name = string("input_1039_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1039_pad_0 = const()[name = string("input_1039_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1039_dilations_0 = const()[name = string("input_1039_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1039_groups_0 = const()[name = string("input_1039_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_19_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_19_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(965881408)))];
+            tensor<fp16, [2048]> encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(970075776)))];
+            tensor<fp16, [1, 1024, 188]> input_1037_cast_fp16 = transpose(perm = input_1037_perm_0, x = x_455_cast_fp16)[name = string("transpose_150")];
+            tensor<fp16, [1, 2048, 188]> input_1039_cast_fp16 = conv(bias = encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_19_conv_pointwise_conv1_weight_to_fp16, x = input_1037_cast_fp16)[name = string("input_1039_cast_fp16")];
+            int32 x_457_split_num_splits_0 = const()[name = string("x_457_split_num_splits_0"), val = int32(2)];
+            int32 x_457_split_axis_0 = const()[name = string("x_457_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_457_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_457_split_cast_fp16_1 = split(axis = x_457_split_axis_0, num_splits = x_457_split_num_splits_0, x = input_1039_cast_fp16)[name = string("x_457_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_457_split_1_sigmoid_cast_fp16 = sigmoid(x = x_457_split_cast_fp16_1)[name = string("x_457_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_457_cast_fp16 = mul(x = x_457_split_cast_fp16_0, y = x_457_split_1_sigmoid_cast_fp16)[name = string("x_457_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1041_cast_fp16 = select(a = var_164_to_fp16, b = x_457_cast_fp16, cond = var_608)[name = string("input_1041_cast_fp16")];
+            tensor<int32, [6]> input_1043_pad_0 = const()[name = string("input_1043_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1043_mode_0 = const()[name = string("input_1043_mode_0"), val = string("constant")];
+            fp16 const_281_to_fp16 = const()[name = string("const_281_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1043_cast_fp16 = pad(constant_val = const_281_to_fp16, mode = input_1043_mode_0, pad = input_1043_pad_0, x = input_1041_cast_fp16)[name = string("input_1043_cast_fp16")];
+            string input_1045_pad_type_0 = const()[name = string("input_1045_pad_type_0"), val = string("valid")];
+            int32 input_1045_groups_0 = const()[name = string("input_1045_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1045_strides_0 = const()[name = string("input_1045_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1045_pad_0 = const()[name = string("input_1045_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1045_dilations_0 = const()[name = string("input_1045_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_360_to_fp16 = const()[name = string("const_360_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(970079936)))];
+            tensor<fp16, [1024]> const_361_to_fp16 = const()[name = string("const_361_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(970098432)))];
+            tensor<fp16, [1, 1024, 188]> input_1047_cast_fp16 = conv(bias = const_361_to_fp16, dilations = input_1045_dilations_0, groups = input_1045_groups_0, pad = input_1045_pad_0, pad_type = input_1045_pad_type_0, strides = input_1045_strides_0, weight = const_360_to_fp16, x = input_1043_cast_fp16)[name = string("input_1047_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1049_cast_fp16 = silu(x = input_1047_cast_fp16)[name = string("input_1049_cast_fp16")];
+            string x_459_pad_type_0 = const()[name = string("x_459_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_459_strides_0 = const()[name = string("x_459_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_459_pad_0 = const()[name = string("x_459_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_459_dilations_0 = const()[name = string("x_459_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_459_groups_0 = const()[name = string("x_459_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(970100544)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(972197760)))];
+            tensor<fp16, [1, 1024, 188]> x_459_cast_fp16 = conv(bias = encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16, dilations = x_459_dilations_0, groups = x_459_groups_0, pad = x_459_pad_0, pad_type = x_459_pad_type_0, strides = x_459_strides_0, weight = encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16, x = input_1049_cast_fp16)[name = string("x_459_cast_fp16")];
+            tensor<int32, [3]> input_1051_perm_0 = const()[name = string("input_1051_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1051_cast_fp16 = transpose(perm = input_1051_perm_0, x = x_459_cast_fp16)[name = string("transpose_149")];
+            tensor<fp16, [1, 188, 1024]> input_1053_cast_fp16 = add(x = input_1035_cast_fp16, y = input_1051_cast_fp16)[name = string("input_1053_cast_fp16")];
+            tensor<int32, [1]> input_1055_axes_0 = const()[name = string("input_1055_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(972199872)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(972201984)))];
+            tensor<fp16, [1, 188, 1024]> input_1055_cast_fp16 = layer_norm(axes = input_1055_axes_0, beta = encoder_module_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1053_cast_fp16)[name = string("input_1055_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(972204096)))];
+            tensor<fp16, [4096]> encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(980592768)))];
+            tensor<fp16, [1, 188, 4096]> linear_179_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16, x = input_1055_cast_fp16)[name = string("linear_179_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1059_cast_fp16 = silu(x = linear_179_cast_fp16)[name = string("input_1059_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(980601024)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(988989696)))];
+            tensor<fp16, [1, 188, 1024]> linear_180_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16, x = input_1059_cast_fp16)[name = string("linear_180_cast_fp16")];
+            fp16 var_3956_to_fp16 = const()[name = string("op_3956_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3957_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_3956_to_fp16)[name = string("op_3957_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1065_cast_fp16 = add(x = input_1053_cast_fp16, y = var_3957_cast_fp16)[name = string("input_1065_cast_fp16")];
+            tensor<int32, [1]> input_1067_axes_0 = const()[name = string("input_1067_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(988991808)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(988993920)))];
+            tensor<fp16, [1, 188, 1024]> input_1067_cast_fp16 = layer_norm(axes = input_1067_axes_0, beta = encoder_module_layers_19_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_out_weight_to_fp16, x = input_1065_cast_fp16)[name = string("input_1067_cast_fp16")];
+            tensor<int32, [1]> input_1069_axes_0 = const()[name = string("input_1069_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(988996032)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(988998144)))];
+            tensor<fp16, [1, 188, 1024]> input_1069_cast_fp16 = layer_norm(axes = input_1069_axes_0, beta = encoder_module_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1067_cast_fp16)[name = string("input_1069_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(989000256)))];
+            tensor<fp16, [4096]> encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(997388928)))];
+            tensor<fp16, [1, 188, 4096]> linear_181_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16, x = input_1069_cast_fp16)[name = string("linear_181_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1073_cast_fp16 = silu(x = linear_181_cast_fp16)[name = string("input_1073_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(997397184)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1005785856)))];
+            tensor<fp16, [1, 188, 1024]> linear_182_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16, x = input_1073_cast_fp16)[name = string("linear_182_cast_fp16")];
+            fp16 var_3987_to_fp16 = const()[name = string("op_3987_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3988_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3987_to_fp16)[name = string("op_3988_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1079_cast_fp16 = add(x = input_1067_cast_fp16, y = var_3988_cast_fp16)[name = string("input_1079_cast_fp16")];
+            tensor<int32, [1]> query_41_axes_0 = const()[name = string("query_41_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1005787968)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1005790080)))];
+            tensor<fp16, [1, 188, 1024]> query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = encoder_module_layers_20_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_self_att_weight_to_fp16, x = input_1079_cast_fp16)[name = string("query_41_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1005792192)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1007889408)))];
+            tensor<fp16, [1, 188, 1024]> linear_183_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_q_weight_to_fp16, x = query_41_cast_fp16)[name = string("linear_183_cast_fp16")];
+            tensor<int32, [4]> var_4005 = const()[name = string("op_4005"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_121_cast_fp16 = reshape(shape = var_4005, x = linear_183_cast_fp16)[name = string("q_121_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1007891520)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1009988736)))];
+            tensor<fp16, [1, 188, 1024]> linear_184_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_k_weight_to_fp16, x = query_41_cast_fp16)[name = string("linear_184_cast_fp16")];
+            tensor<int32, [4]> var_4010 = const()[name = string("op_4010"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_81_cast_fp16 = reshape(shape = var_4010, x = linear_184_cast_fp16)[name = string("k_81_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1009990848)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1012088064)))];
+            tensor<fp16, [1, 188, 1024]> linear_185_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_v_weight_to_fp16, x = query_41_cast_fp16)[name = string("linear_185_cast_fp16")];
+            tensor<int32, [4]> var_4015 = const()[name = string("op_4015"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_41_cast_fp16 = reshape(shape = var_4015, x = linear_185_cast_fp16)[name = string("v_41_cast_fp16")];
+            tensor<int32, [4]> value_45_perm_0 = const()[name = string("value_45_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1012090176)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4027_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_module_layers_20_self_attn_pos_bias_u_to_fp16)[name = string("op_4027_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1012092288)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4029_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_module_layers_20_self_attn_pos_bias_v_to_fp16)[name = string("op_4029_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_41_perm_0 = const()[name = string("q_with_bias_v_41_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_467_transpose_x_0 = const()[name = string("x_467_transpose_x_0"), val = bool(false)];
+            bool x_467_transpose_y_0 = const()[name = string("x_467_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_4031_to_fp16 = const()[name = string("op_4031_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1012094400)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_4029_cast_fp16)[name = string("transpose_147")];
+            tensor<fp16, [1, 8, 188, 375]> x_467_cast_fp16 = matmul(transpose_x = x_467_transpose_x_0, transpose_y = x_467_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = var_4031_to_fp16)[name = string("x_467_cast_fp16")];
+            tensor<int32, [8]> x_469_pad_0 = const()[name = string("x_469_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_469_mode_0 = const()[name = string("x_469_mode_0"), val = string("constant")];
+            fp16 const_288_to_fp16 = const()[name = string("const_288_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_469_cast_fp16 = pad(constant_val = const_288_to_fp16, mode = x_469_mode_0, pad = x_469_pad_0, x = x_467_cast_fp16)[name = string("x_469_cast_fp16")];
+            tensor<int32, [4]> var_4039 = const()[name = string("op_4039"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_471_cast_fp16 = reshape(shape = var_4039, x = x_469_cast_fp16)[name = string("x_471_cast_fp16")];
+            tensor<int32, [4]> var_4043_begin_0 = const()[name = string("op_4043_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4043_end_0 = const()[name = string("op_4043_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4043_end_mask_0 = const()[name = string("op_4043_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4043_cast_fp16 = slice_by_index(begin = var_4043_begin_0, end = var_4043_end_0, end_mask = var_4043_end_mask_0, x = x_471_cast_fp16)[name = string("op_4043_cast_fp16")];
+            tensor<int32, [4]> var_4044 = const()[name = string("op_4044"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_81_cast_fp16 = reshape(shape = var_4044, x = var_4043_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<int32, [4]> transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_113 = transpose(perm = transpose_113_perm_0, x = k_81_cast_fp16)[name = string("transpose_145")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_112 = transpose(perm = transpose_112_perm_0, x = var_4027_cast_fp16)[name = string("transpose_146")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_41_cast_fp16 = matmul(transpose_x = matrix_ac_41_transpose_x_0, transpose_y = matrix_ac_41_transpose_y_0, x = transpose_112, y = transpose_113)[name = string("matrix_ac_41_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_83_begin_0 = const()[name = string("matrix_bd_83_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_83_end_0 = const()[name = string("matrix_bd_83_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_83_end_mask_0 = const()[name = string("matrix_bd_83_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4053_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = string("op_4053_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<fp16, [1, 8, 188, 188]> _inversed_scores_81_cast_fp16 = mul(x = var_4053_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = string("_inversed_scores_81_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_83_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_15)[name = string("scores_83_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4059_cast_fp16 = softmax(axis = var_152, x = scores_83_cast_fp16)[name = string("op_4059_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1081_cast_fp16 = select(a = var_164_to_fp16, b = var_4059_cast_fp16, cond = mask_15)[name = string("input_1081_cast_fp16")];
+            bool x_473_transpose_x_0 = const()[name = string("x_473_transpose_x_0"), val = bool(false)];
+            bool x_473_transpose_y_0 = const()[name = string("x_473_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_41_cast_fp16)[name = string("transpose_148")];
+            tensor<fp16, [1, 8, 188, 128]> x_473_cast_fp16 = matmul(transpose_x = x_473_transpose_x_0, transpose_y = x_473_transpose_y_0, x = input_1081_cast_fp16, y = value_45_cast_fp16)[name = string("x_473_cast_fp16")];
+            tensor<int32, [4]> var_4063_perm_0 = const()[name = string("op_4063_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4064 = const()[name = string("op_4064"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4063_cast_fp16 = transpose(perm = var_4063_perm_0, x = x_473_cast_fp16)[name = string("transpose_144")];
+            tensor<fp16, [1, 188, 1024]> input_1083_cast_fp16 = reshape(shape = var_4064, x = var_4063_cast_fp16)[name = string("input_1083_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1012862464)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1014959680)))];
+            tensor<fp16, [1, 188, 1024]> linear_187_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_out_weight_to_fp16, x = input_1083_cast_fp16)[name = string("linear_187_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1087_cast_fp16 = add(x = input_1079_cast_fp16, y = linear_187_cast_fp16)[name = string("input_1087_cast_fp16")];
+            tensor<int32, [1]> x_477_axes_0 = const()[name = string("x_477_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1014961792)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1014963904)))];
+            tensor<fp16, [1, 188, 1024]> x_477_cast_fp16 = layer_norm(axes = x_477_axes_0, beta = encoder_module_layers_20_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_conv_weight_to_fp16, x = input_1087_cast_fp16)[name = string("x_477_cast_fp16")];
+            tensor<int32, [3]> input_1089_perm_0 = const()[name = string("input_1089_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1091_pad_type_0 = const()[name = string("input_1091_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1091_strides_0 = const()[name = string("input_1091_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1091_pad_0 = const()[name = string("input_1091_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1091_dilations_0 = const()[name = string("input_1091_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1091_groups_0 = const()[name = string("input_1091_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_20_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_20_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1014966016)))];
+            tensor<fp16, [2048]> encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1019160384)))];
+            tensor<fp16, [1, 1024, 188]> input_1089_cast_fp16 = transpose(perm = input_1089_perm_0, x = x_477_cast_fp16)[name = string("transpose_143")];
+            tensor<fp16, [1, 2048, 188]> input_1091_cast_fp16 = conv(bias = encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_20_conv_pointwise_conv1_weight_to_fp16, x = input_1089_cast_fp16)[name = string("input_1091_cast_fp16")];
+            int32 x_479_split_num_splits_0 = const()[name = string("x_479_split_num_splits_0"), val = int32(2)];
+            int32 x_479_split_axis_0 = const()[name = string("x_479_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_479_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_479_split_cast_fp16_1 = split(axis = x_479_split_axis_0, num_splits = x_479_split_num_splits_0, x = input_1091_cast_fp16)[name = string("x_479_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_479_split_1_sigmoid_cast_fp16 = sigmoid(x = x_479_split_cast_fp16_1)[name = string("x_479_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_479_cast_fp16 = mul(x = x_479_split_cast_fp16_0, y = x_479_split_1_sigmoid_cast_fp16)[name = string("x_479_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1093_cast_fp16 = select(a = var_164_to_fp16, b = x_479_cast_fp16, cond = var_608)[name = string("input_1093_cast_fp16")];
+            tensor<int32, [6]> input_1095_pad_0 = const()[name = string("input_1095_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1095_mode_0 = const()[name = string("input_1095_mode_0"), val = string("constant")];
+            fp16 const_291_to_fp16 = const()[name = string("const_291_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1095_cast_fp16 = pad(constant_val = const_291_to_fp16, mode = input_1095_mode_0, pad = input_1095_pad_0, x = input_1093_cast_fp16)[name = string("input_1095_cast_fp16")];
+            string input_1097_pad_type_0 = const()[name = string("input_1097_pad_type_0"), val = string("valid")];
+            int32 input_1097_groups_0 = const()[name = string("input_1097_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1097_strides_0 = const()[name = string("input_1097_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1097_pad_0 = const()[name = string("input_1097_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1097_dilations_0 = const()[name = string("input_1097_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_362_to_fp16 = const()[name = string("const_362_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1019164544)))];
+            tensor<fp16, [1024]> const_363_to_fp16 = const()[name = string("const_363_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1019183040)))];
+            tensor<fp16, [1, 1024, 188]> input_1099_cast_fp16 = conv(bias = const_363_to_fp16, dilations = input_1097_dilations_0, groups = input_1097_groups_0, pad = input_1097_pad_0, pad_type = input_1097_pad_type_0, strides = input_1097_strides_0, weight = const_362_to_fp16, x = input_1095_cast_fp16)[name = string("input_1099_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1101_cast_fp16 = silu(x = input_1099_cast_fp16)[name = string("input_1101_cast_fp16")];
+            string x_481_pad_type_0 = const()[name = string("x_481_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_481_strides_0 = const()[name = string("x_481_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_481_pad_0 = const()[name = string("x_481_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_481_dilations_0 = const()[name = string("x_481_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_481_groups_0 = const()[name = string("x_481_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1019185152)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1021282368)))];
+            tensor<fp16, [1, 1024, 188]> x_481_cast_fp16 = conv(bias = encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16, dilations = x_481_dilations_0, groups = x_481_groups_0, pad = x_481_pad_0, pad_type = x_481_pad_type_0, strides = x_481_strides_0, weight = encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16, x = input_1101_cast_fp16)[name = string("x_481_cast_fp16")];
+            tensor<int32, [3]> input_1103_perm_0 = const()[name = string("input_1103_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1103_cast_fp16 = transpose(perm = input_1103_perm_0, x = x_481_cast_fp16)[name = string("transpose_142")];
+            tensor<fp16, [1, 188, 1024]> input_1105_cast_fp16 = add(x = input_1087_cast_fp16, y = input_1103_cast_fp16)[name = string("input_1105_cast_fp16")];
+            tensor<int32, [1]> input_1107_axes_0 = const()[name = string("input_1107_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1021284480)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1021286592)))];
+            tensor<fp16, [1, 188, 1024]> input_1107_cast_fp16 = layer_norm(axes = input_1107_axes_0, beta = encoder_module_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1105_cast_fp16)[name = string("input_1107_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1021288704)))];
+            tensor<fp16, [4096]> encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1029677376)))];
+            tensor<fp16, [1, 188, 4096]> linear_188_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16, x = input_1107_cast_fp16)[name = string("linear_188_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1111_cast_fp16 = silu(x = linear_188_cast_fp16)[name = string("input_1111_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1029685632)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1038074304)))];
+            tensor<fp16, [1, 188, 1024]> linear_189_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16, x = input_1111_cast_fp16)[name = string("linear_189_cast_fp16")];
+            fp16 var_4130_to_fp16 = const()[name = string("op_4130_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4131_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_4130_to_fp16)[name = string("op_4131_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1117_cast_fp16 = add(x = input_1105_cast_fp16, y = var_4131_cast_fp16)[name = string("input_1117_cast_fp16")];
+            tensor<int32, [1]> input_1119_axes_0 = const()[name = string("input_1119_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1038076416)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1038078528)))];
+            tensor<fp16, [1, 188, 1024]> input_1119_cast_fp16 = layer_norm(axes = input_1119_axes_0, beta = encoder_module_layers_20_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_out_weight_to_fp16, x = input_1117_cast_fp16)[name = string("input_1119_cast_fp16")];
+            tensor<int32, [1]> input_1121_axes_0 = const()[name = string("input_1121_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1038080640)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1038082752)))];
+            tensor<fp16, [1, 188, 1024]> input_1121_cast_fp16 = layer_norm(axes = input_1121_axes_0, beta = encoder_module_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1119_cast_fp16)[name = string("input_1121_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1038084864)))];
+            tensor<fp16, [4096]> encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1046473536)))];
+            tensor<fp16, [1, 188, 4096]> linear_190_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16, x = input_1121_cast_fp16)[name = string("linear_190_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1125_cast_fp16 = silu(x = linear_190_cast_fp16)[name = string("input_1125_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1046481792)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1054870464)))];
+            tensor<fp16, [1, 188, 1024]> linear_191_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16, x = input_1125_cast_fp16)[name = string("linear_191_cast_fp16")];
+            fp16 var_4161_to_fp16 = const()[name = string("op_4161_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4162_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_4161_to_fp16)[name = string("op_4162_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1131_cast_fp16 = add(x = input_1119_cast_fp16, y = var_4162_cast_fp16)[name = string("input_1131_cast_fp16")];
+            tensor<int32, [1]> query_43_axes_0 = const()[name = string("query_43_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1054872576)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1054874688)))];
+            tensor<fp16, [1, 188, 1024]> query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = encoder_module_layers_21_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_self_att_weight_to_fp16, x = input_1131_cast_fp16)[name = string("query_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1054876800)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1056974016)))];
+            tensor<fp16, [1, 188, 1024]> linear_192_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_q_weight_to_fp16, x = query_43_cast_fp16)[name = string("linear_192_cast_fp16")];
+            tensor<int32, [4]> var_4179 = const()[name = string("op_4179"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_127_cast_fp16 = reshape(shape = var_4179, x = linear_192_cast_fp16)[name = string("q_127_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1056976128)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1059073344)))];
+            tensor<fp16, [1, 188, 1024]> linear_193_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_k_weight_to_fp16, x = query_43_cast_fp16)[name = string("linear_193_cast_fp16")];
+            tensor<int32, [4]> var_4184 = const()[name = string("op_4184"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_85_cast_fp16 = reshape(shape = var_4184, x = linear_193_cast_fp16)[name = string("k_85_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1059075456)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1061172672)))];
+            tensor<fp16, [1, 188, 1024]> linear_194_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_v_weight_to_fp16, x = query_43_cast_fp16)[name = string("linear_194_cast_fp16")];
+            tensor<int32, [4]> var_4189 = const()[name = string("op_4189"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_43_cast_fp16 = reshape(shape = var_4189, x = linear_194_cast_fp16)[name = string("v_43_cast_fp16")];
+            tensor<int32, [4]> value_47_perm_0 = const()[name = string("value_47_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1061174784)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4201_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_module_layers_21_self_attn_pos_bias_u_to_fp16)[name = string("op_4201_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1061176896)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4203_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_module_layers_21_self_attn_pos_bias_v_to_fp16)[name = string("op_4203_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_43_perm_0 = const()[name = string("q_with_bias_v_43_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_489_transpose_x_0 = const()[name = string("x_489_transpose_x_0"), val = bool(false)];
+            bool x_489_transpose_y_0 = const()[name = string("x_489_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_4205_to_fp16 = const()[name = string("op_4205_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1061179008)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_4203_cast_fp16)[name = string("transpose_140")];
+            tensor<fp16, [1, 8, 188, 375]> x_489_cast_fp16 = matmul(transpose_x = x_489_transpose_x_0, transpose_y = x_489_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = var_4205_to_fp16)[name = string("x_489_cast_fp16")];
+            tensor<int32, [8]> x_491_pad_0 = const()[name = string("x_491_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_491_mode_0 = const()[name = string("x_491_mode_0"), val = string("constant")];
+            fp16 const_298_to_fp16 = const()[name = string("const_298_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_491_cast_fp16 = pad(constant_val = const_298_to_fp16, mode = x_491_mode_0, pad = x_491_pad_0, x = x_489_cast_fp16)[name = string("x_491_cast_fp16")];
+            tensor<int32, [4]> var_4213 = const()[name = string("op_4213"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_493_cast_fp16 = reshape(shape = var_4213, x = x_491_cast_fp16)[name = string("x_493_cast_fp16")];
+            tensor<int32, [4]> var_4217_begin_0 = const()[name = string("op_4217_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4217_end_0 = const()[name = string("op_4217_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4217_end_mask_0 = const()[name = string("op_4217_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4217_cast_fp16 = slice_by_index(begin = var_4217_begin_0, end = var_4217_end_0, end_mask = var_4217_end_mask_0, x = x_493_cast_fp16)[name = string("op_4217_cast_fp16")];
+            tensor<int32, [4]> var_4218 = const()[name = string("op_4218"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_85_cast_fp16 = reshape(shape = var_4218, x = var_4217_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<int32, [4]> transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_115 = transpose(perm = transpose_115_perm_0, x = k_85_cast_fp16)[name = string("transpose_138")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_114 = transpose(perm = transpose_114_perm_0, x = var_4201_cast_fp16)[name = string("transpose_139")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_43_cast_fp16 = matmul(transpose_x = matrix_ac_43_transpose_x_0, transpose_y = matrix_ac_43_transpose_y_0, x = transpose_114, y = transpose_115)[name = string("matrix_ac_43_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_87_begin_0 = const()[name = string("matrix_bd_87_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_87_end_0 = const()[name = string("matrix_bd_87_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_87_end_mask_0 = const()[name = string("matrix_bd_87_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4227_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = string("op_4227_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<fp16, [1, 8, 188, 188]> _inversed_scores_85_cast_fp16 = mul(x = var_4227_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = string("_inversed_scores_85_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_87_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_15)[name = string("scores_87_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4233_cast_fp16 = softmax(axis = var_152, x = scores_87_cast_fp16)[name = string("op_4233_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1133_cast_fp16 = select(a = var_164_to_fp16, b = var_4233_cast_fp16, cond = mask_15)[name = string("input_1133_cast_fp16")];
+            bool x_495_transpose_x_0 = const()[name = string("x_495_transpose_x_0"), val = bool(false)];
+            bool x_495_transpose_y_0 = const()[name = string("x_495_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_43_cast_fp16)[name = string("transpose_141")];
+            tensor<fp16, [1, 8, 188, 128]> x_495_cast_fp16 = matmul(transpose_x = x_495_transpose_x_0, transpose_y = x_495_transpose_y_0, x = input_1133_cast_fp16, y = value_47_cast_fp16)[name = string("x_495_cast_fp16")];
+            tensor<int32, [4]> var_4237_perm_0 = const()[name = string("op_4237_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4238 = const()[name = string("op_4238"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4237_cast_fp16 = transpose(perm = var_4237_perm_0, x = x_495_cast_fp16)[name = string("transpose_137")];
+            tensor<fp16, [1, 188, 1024]> input_1135_cast_fp16 = reshape(shape = var_4238, x = var_4237_cast_fp16)[name = string("input_1135_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1061947072)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1064044288)))];
+            tensor<fp16, [1, 188, 1024]> linear_196_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_out_weight_to_fp16, x = input_1135_cast_fp16)[name = string("linear_196_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1139_cast_fp16 = add(x = input_1131_cast_fp16, y = linear_196_cast_fp16)[name = string("input_1139_cast_fp16")];
+            tensor<int32, [1]> x_499_axes_0 = const()[name = string("x_499_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1064046400)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1064048512)))];
+            tensor<fp16, [1, 188, 1024]> x_499_cast_fp16 = layer_norm(axes = x_499_axes_0, beta = encoder_module_layers_21_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_conv_weight_to_fp16, x = input_1139_cast_fp16)[name = string("x_499_cast_fp16")];
+            tensor<int32, [3]> input_1141_perm_0 = const()[name = string("input_1141_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1143_pad_type_0 = const()[name = string("input_1143_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1143_strides_0 = const()[name = string("input_1143_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1143_pad_0 = const()[name = string("input_1143_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1143_dilations_0 = const()[name = string("input_1143_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1143_groups_0 = const()[name = string("input_1143_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_21_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_21_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1064050624)))];
+            tensor<fp16, [2048]> encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1068244992)))];
+            tensor<fp16, [1, 1024, 188]> input_1141_cast_fp16 = transpose(perm = input_1141_perm_0, x = x_499_cast_fp16)[name = string("transpose_136")];
+            tensor<fp16, [1, 2048, 188]> input_1143_cast_fp16 = conv(bias = encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_21_conv_pointwise_conv1_weight_to_fp16, x = input_1141_cast_fp16)[name = string("input_1143_cast_fp16")];
+            int32 x_501_split_num_splits_0 = const()[name = string("x_501_split_num_splits_0"), val = int32(2)];
+            int32 x_501_split_axis_0 = const()[name = string("x_501_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_501_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_501_split_cast_fp16_1 = split(axis = x_501_split_axis_0, num_splits = x_501_split_num_splits_0, x = input_1143_cast_fp16)[name = string("x_501_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_501_split_1_sigmoid_cast_fp16 = sigmoid(x = x_501_split_cast_fp16_1)[name = string("x_501_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_501_cast_fp16 = mul(x = x_501_split_cast_fp16_0, y = x_501_split_1_sigmoid_cast_fp16)[name = string("x_501_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1145_cast_fp16 = select(a = var_164_to_fp16, b = x_501_cast_fp16, cond = var_608)[name = string("input_1145_cast_fp16")];
+            tensor<int32, [6]> input_1147_pad_0 = const()[name = string("input_1147_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1147_mode_0 = const()[name = string("input_1147_mode_0"), val = string("constant")];
+            fp16 const_301_to_fp16 = const()[name = string("const_301_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1147_cast_fp16 = pad(constant_val = const_301_to_fp16, mode = input_1147_mode_0, pad = input_1147_pad_0, x = input_1145_cast_fp16)[name = string("input_1147_cast_fp16")];
+            string input_1149_pad_type_0 = const()[name = string("input_1149_pad_type_0"), val = string("valid")];
+            int32 input_1149_groups_0 = const()[name = string("input_1149_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1149_strides_0 = const()[name = string("input_1149_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1149_pad_0 = const()[name = string("input_1149_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1149_dilations_0 = const()[name = string("input_1149_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_364_to_fp16 = const()[name = string("const_364_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1068249152)))];
+            tensor<fp16, [1024]> const_365_to_fp16 = const()[name = string("const_365_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1068267648)))];
+            tensor<fp16, [1, 1024, 188]> input_1151_cast_fp16 = conv(bias = const_365_to_fp16, dilations = input_1149_dilations_0, groups = input_1149_groups_0, pad = input_1149_pad_0, pad_type = input_1149_pad_type_0, strides = input_1149_strides_0, weight = const_364_to_fp16, x = input_1147_cast_fp16)[name = string("input_1151_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1153_cast_fp16 = silu(x = input_1151_cast_fp16)[name = string("input_1153_cast_fp16")];
+            string x_503_pad_type_0 = const()[name = string("x_503_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_503_strides_0 = const()[name = string("x_503_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_503_pad_0 = const()[name = string("x_503_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_503_dilations_0 = const()[name = string("x_503_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_503_groups_0 = const()[name = string("x_503_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1068269760)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1070366976)))];
+            tensor<fp16, [1, 1024, 188]> x_503_cast_fp16 = conv(bias = encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16, dilations = x_503_dilations_0, groups = x_503_groups_0, pad = x_503_pad_0, pad_type = x_503_pad_type_0, strides = x_503_strides_0, weight = encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16, x = input_1153_cast_fp16)[name = string("x_503_cast_fp16")];
+            tensor<int32, [3]> input_1155_perm_0 = const()[name = string("input_1155_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1155_cast_fp16 = transpose(perm = input_1155_perm_0, x = x_503_cast_fp16)[name = string("transpose_135")];
+            tensor<fp16, [1, 188, 1024]> input_1157_cast_fp16 = add(x = input_1139_cast_fp16, y = input_1155_cast_fp16)[name = string("input_1157_cast_fp16")];
+            tensor<int32, [1]> input_1159_axes_0 = const()[name = string("input_1159_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1070369088)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1070371200)))];
+            tensor<fp16, [1, 188, 1024]> input_1159_cast_fp16 = layer_norm(axes = input_1159_axes_0, beta = encoder_module_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1157_cast_fp16)[name = string("input_1159_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1070373312)))];
+            tensor<fp16, [4096]> encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1078761984)))];
+            tensor<fp16, [1, 188, 4096]> linear_197_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16, x = input_1159_cast_fp16)[name = string("linear_197_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1163_cast_fp16 = silu(x = linear_197_cast_fp16)[name = string("input_1163_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1078770240)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087158912)))];
+            tensor<fp16, [1, 188, 1024]> linear_198_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16, x = input_1163_cast_fp16)[name = string("linear_198_cast_fp16")];
+            fp16 var_4304_to_fp16 = const()[name = string("op_4304_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4305_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_4304_to_fp16)[name = string("op_4305_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1169_cast_fp16 = add(x = input_1157_cast_fp16, y = var_4305_cast_fp16)[name = string("input_1169_cast_fp16")];
+            tensor<int32, [1]> input_1171_axes_0 = const()[name = string("input_1171_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087161024)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087163136)))];
+            tensor<fp16, [1, 188, 1024]> input_1171_cast_fp16 = layer_norm(axes = input_1171_axes_0, beta = encoder_module_layers_21_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_out_weight_to_fp16, x = input_1169_cast_fp16)[name = string("input_1171_cast_fp16")];
+            tensor<int32, [1]> input_1173_axes_0 = const()[name = string("input_1173_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087165248)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087167360)))];
+            tensor<fp16, [1, 188, 1024]> input_1173_cast_fp16 = layer_norm(axes = input_1173_axes_0, beta = encoder_module_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1171_cast_fp16)[name = string("input_1173_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087169472)))];
+            tensor<fp16, [4096]> encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1095558144)))];
+            tensor<fp16, [1, 188, 4096]> linear_199_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16, x = input_1173_cast_fp16)[name = string("linear_199_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1177_cast_fp16 = silu(x = linear_199_cast_fp16)[name = string("input_1177_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1095566400)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1103955072)))];
+            tensor<fp16, [1, 188, 1024]> linear_200_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16, x = input_1177_cast_fp16)[name = string("linear_200_cast_fp16")];
+            fp16 var_4335_to_fp16 = const()[name = string("op_4335_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4336_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_4335_to_fp16)[name = string("op_4336_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1183_cast_fp16 = add(x = input_1171_cast_fp16, y = var_4336_cast_fp16)[name = string("input_1183_cast_fp16")];
+            tensor<int32, [1]> query_45_axes_0 = const()[name = string("query_45_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1103957184)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1103959296)))];
+            tensor<fp16, [1, 188, 1024]> query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = encoder_module_layers_22_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_self_att_weight_to_fp16, x = input_1183_cast_fp16)[name = string("query_45_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1103961408)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1106058624)))];
+            tensor<fp16, [1, 188, 1024]> linear_201_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_q_weight_to_fp16, x = query_45_cast_fp16)[name = string("linear_201_cast_fp16")];
+            tensor<int32, [4]> var_4353 = const()[name = string("op_4353"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_133_cast_fp16 = reshape(shape = var_4353, x = linear_201_cast_fp16)[name = string("q_133_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1106060736)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1108157952)))];
+            tensor<fp16, [1, 188, 1024]> linear_202_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_k_weight_to_fp16, x = query_45_cast_fp16)[name = string("linear_202_cast_fp16")];
+            tensor<int32, [4]> var_4358 = const()[name = string("op_4358"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_89_cast_fp16 = reshape(shape = var_4358, x = linear_202_cast_fp16)[name = string("k_89_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1108160064)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1110257280)))];
+            tensor<fp16, [1, 188, 1024]> linear_203_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_v_weight_to_fp16, x = query_45_cast_fp16)[name = string("linear_203_cast_fp16")];
+            tensor<int32, [4]> var_4363 = const()[name = string("op_4363"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_45_cast_fp16 = reshape(shape = var_4363, x = linear_203_cast_fp16)[name = string("v_45_cast_fp16")];
+            tensor<int32, [4]> value_49_perm_0 = const()[name = string("value_49_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1110259392)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4375_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_module_layers_22_self_attn_pos_bias_u_to_fp16)[name = string("op_4375_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1110261504)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4377_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_module_layers_22_self_attn_pos_bias_v_to_fp16)[name = string("op_4377_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_45_perm_0 = const()[name = string("q_with_bias_v_45_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            bool x_511_transpose_x_0 = const()[name = string("x_511_transpose_x_0"), val = bool(false)];
+            bool x_511_transpose_y_0 = const()[name = string("x_511_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> var_4379_to_fp16 = const()[name = string("op_4379_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1110263616)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_4377_cast_fp16)[name = string("transpose_133")];
+            tensor<fp16, [1, 8, 188, 375]> x_511_cast_fp16 = matmul(transpose_x = x_511_transpose_x_0, transpose_y = x_511_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = var_4379_to_fp16)[name = string("x_511_cast_fp16")];
+            tensor<int32, [8]> x_513_pad_0 = const()[name = string("x_513_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_513_mode_0 = const()[name = string("x_513_mode_0"), val = string("constant")];
+            fp16 const_308_to_fp16 = const()[name = string("const_308_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_513_cast_fp16 = pad(constant_val = const_308_to_fp16, mode = x_513_mode_0, pad = x_513_pad_0, x = x_511_cast_fp16)[name = string("x_513_cast_fp16")];
+            tensor<int32, [4]> var_4387 = const()[name = string("op_4387"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_515_cast_fp16 = reshape(shape = var_4387, x = x_513_cast_fp16)[name = string("x_515_cast_fp16")];
+            tensor<int32, [4]> var_4391_begin_0 = const()[name = string("op_4391_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4391_end_0 = const()[name = string("op_4391_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4391_end_mask_0 = const()[name = string("op_4391_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4391_cast_fp16 = slice_by_index(begin = var_4391_begin_0, end = var_4391_end_0, end_mask = var_4391_end_mask_0, x = x_515_cast_fp16)[name = string("op_4391_cast_fp16")];
+            tensor<int32, [4]> var_4392 = const()[name = string("op_4392"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_89_cast_fp16 = reshape(shape = var_4392, x = var_4391_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<int32, [4]> transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_117 = transpose(perm = transpose_117_perm_0, x = k_89_cast_fp16)[name = string("transpose_131")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_116 = transpose(perm = transpose_116_perm_0, x = var_4375_cast_fp16)[name = string("transpose_132")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_45_cast_fp16 = matmul(transpose_x = matrix_ac_45_transpose_x_0, transpose_y = matrix_ac_45_transpose_y_0, x = transpose_116, y = transpose_117)[name = string("matrix_ac_45_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_91_begin_0 = const()[name = string("matrix_bd_91_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_91_end_0 = const()[name = string("matrix_bd_91_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_91_end_mask_0 = const()[name = string("matrix_bd_91_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4401_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = string("op_4401_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<fp16, [1, 8, 188, 188]> _inversed_scores_89_cast_fp16 = mul(x = var_4401_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = string("_inversed_scores_89_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_91_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_15)[name = string("scores_91_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4407_cast_fp16 = softmax(axis = var_152, x = scores_91_cast_fp16)[name = string("op_4407_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1185_cast_fp16 = select(a = var_164_to_fp16, b = var_4407_cast_fp16, cond = mask_15)[name = string("input_1185_cast_fp16")];
+            bool x_517_transpose_x_0 = const()[name = string("x_517_transpose_x_0"), val = bool(false)];
+            bool x_517_transpose_y_0 = const()[name = string("x_517_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_49_cast_fp16 = transpose(perm = value_49_perm_0, x = v_45_cast_fp16)[name = string("transpose_134")];
+            tensor<fp16, [1, 8, 188, 128]> x_517_cast_fp16 = matmul(transpose_x = x_517_transpose_x_0, transpose_y = x_517_transpose_y_0, x = input_1185_cast_fp16, y = value_49_cast_fp16)[name = string("x_517_cast_fp16")];
+            tensor<int32, [4]> var_4411_perm_0 = const()[name = string("op_4411_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4412 = const()[name = string("op_4412"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4411_cast_fp16 = transpose(perm = var_4411_perm_0, x = x_517_cast_fp16)[name = string("transpose_130")];
+            tensor<fp16, [1, 188, 1024]> input_1187_cast_fp16 = reshape(shape = var_4412, x = var_4411_cast_fp16)[name = string("input_1187_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1111031680)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1113128896)))];
+            tensor<fp16, [1, 188, 1024]> linear_205_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_out_weight_to_fp16, x = input_1187_cast_fp16)[name = string("linear_205_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1191_cast_fp16 = add(x = input_1183_cast_fp16, y = linear_205_cast_fp16)[name = string("input_1191_cast_fp16")];
+            tensor<int32, [1]> x_521_axes_0 = const()[name = string("x_521_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1113131008)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1113133120)))];
+            tensor<fp16, [1, 188, 1024]> x_521_cast_fp16 = layer_norm(axes = x_521_axes_0, beta = encoder_module_layers_22_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_conv_weight_to_fp16, x = input_1191_cast_fp16)[name = string("x_521_cast_fp16")];
+            tensor<int32, [3]> input_1193_perm_0 = const()[name = string("input_1193_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1195_pad_type_0 = const()[name = string("input_1195_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1195_strides_0 = const()[name = string("input_1195_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1195_pad_0 = const()[name = string("input_1195_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1195_dilations_0 = const()[name = string("input_1195_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1195_groups_0 = const()[name = string("input_1195_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_22_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_22_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1113135232)))];
+            tensor<fp16, [2048]> encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1117329600)))];
+            tensor<fp16, [1, 1024, 188]> input_1193_cast_fp16 = transpose(perm = input_1193_perm_0, x = x_521_cast_fp16)[name = string("transpose_129")];
+            tensor<fp16, [1, 2048, 188]> input_1195_cast_fp16 = conv(bias = encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_22_conv_pointwise_conv1_weight_to_fp16, x = input_1193_cast_fp16)[name = string("input_1195_cast_fp16")];
+            int32 x_523_split_num_splits_0 = const()[name = string("x_523_split_num_splits_0"), val = int32(2)];
+            int32 x_523_split_axis_0 = const()[name = string("x_523_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_523_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_523_split_cast_fp16_1 = split(axis = x_523_split_axis_0, num_splits = x_523_split_num_splits_0, x = input_1195_cast_fp16)[name = string("x_523_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_523_split_1_sigmoid_cast_fp16 = sigmoid(x = x_523_split_cast_fp16_1)[name = string("x_523_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_523_cast_fp16 = mul(x = x_523_split_cast_fp16_0, y = x_523_split_1_sigmoid_cast_fp16)[name = string("x_523_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1197_cast_fp16 = select(a = var_164_to_fp16, b = x_523_cast_fp16, cond = var_608)[name = string("input_1197_cast_fp16")];
+            tensor<int32, [6]> input_1199_pad_0 = const()[name = string("input_1199_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1199_mode_0 = const()[name = string("input_1199_mode_0"), val = string("constant")];
+            fp16 const_311_to_fp16 = const()[name = string("const_311_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1199_cast_fp16 = pad(constant_val = const_311_to_fp16, mode = input_1199_mode_0, pad = input_1199_pad_0, x = input_1197_cast_fp16)[name = string("input_1199_cast_fp16")];
+            string input_1201_pad_type_0 = const()[name = string("input_1201_pad_type_0"), val = string("valid")];
+            int32 input_1201_groups_0 = const()[name = string("input_1201_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1201_strides_0 = const()[name = string("input_1201_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1201_pad_0 = const()[name = string("input_1201_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1201_dilations_0 = const()[name = string("input_1201_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_366_to_fp16 = const()[name = string("const_366_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1117333760)))];
+            tensor<fp16, [1024]> const_367_to_fp16 = const()[name = string("const_367_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1117352256)))];
+            tensor<fp16, [1, 1024, 188]> input_1203_cast_fp16 = conv(bias = const_367_to_fp16, dilations = input_1201_dilations_0, groups = input_1201_groups_0, pad = input_1201_pad_0, pad_type = input_1201_pad_type_0, strides = input_1201_strides_0, weight = const_366_to_fp16, x = input_1199_cast_fp16)[name = string("input_1203_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1205_cast_fp16 = silu(x = input_1203_cast_fp16)[name = string("input_1205_cast_fp16")];
+            string x_525_pad_type_0 = const()[name = string("x_525_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_525_strides_0 = const()[name = string("x_525_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_525_pad_0 = const()[name = string("x_525_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_525_dilations_0 = const()[name = string("x_525_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_525_groups_0 = const()[name = string("x_525_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1117354368)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1119451584)))];
+            tensor<fp16, [1, 1024, 188]> x_525_cast_fp16 = conv(bias = encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16, dilations = x_525_dilations_0, groups = x_525_groups_0, pad = x_525_pad_0, pad_type = x_525_pad_type_0, strides = x_525_strides_0, weight = encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16, x = input_1205_cast_fp16)[name = string("x_525_cast_fp16")];
+            tensor<int32, [3]> input_1207_perm_0 = const()[name = string("input_1207_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1207_cast_fp16 = transpose(perm = input_1207_perm_0, x = x_525_cast_fp16)[name = string("transpose_128")];
+            tensor<fp16, [1, 188, 1024]> input_1209_cast_fp16 = add(x = input_1191_cast_fp16, y = input_1207_cast_fp16)[name = string("input_1209_cast_fp16")];
+            tensor<int32, [1]> input_1211_axes_0 = const()[name = string("input_1211_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1119453696)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1119455808)))];
+            tensor<fp16, [1, 188, 1024]> input_1211_cast_fp16 = layer_norm(axes = input_1211_axes_0, beta = encoder_module_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1209_cast_fp16)[name = string("input_1211_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1119457920)))];
+            tensor<fp16, [4096]> encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1127846592)))];
+            tensor<fp16, [1, 188, 4096]> linear_206_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16, x = input_1211_cast_fp16)[name = string("linear_206_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1215_cast_fp16 = silu(x = linear_206_cast_fp16)[name = string("input_1215_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1127854848)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136243520)))];
+            tensor<fp16, [1, 188, 1024]> linear_207_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16, x = input_1215_cast_fp16)[name = string("linear_207_cast_fp16")];
+            fp16 var_4478_to_fp16 = const()[name = string("op_4478_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4479_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_4478_to_fp16)[name = string("op_4479_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1221_cast_fp16 = add(x = input_1209_cast_fp16, y = var_4479_cast_fp16)[name = string("input_1221_cast_fp16")];
+            tensor<int32, [1]> input_1223_axes_0 = const()[name = string("input_1223_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136245632)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136247744)))];
+            tensor<fp16, [1, 188, 1024]> input_1223_cast_fp16 = layer_norm(axes = input_1223_axes_0, beta = encoder_module_layers_22_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_out_weight_to_fp16, x = input_1221_cast_fp16)[name = string("input_1223_cast_fp16")];
+            tensor<int32, [1]> input_1225_axes_0 = const()[name = string("input_1225_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136249856)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136251968)))];
+            tensor<fp16, [1, 188, 1024]> input_1225_cast_fp16 = layer_norm(axes = input_1225_axes_0, beta = encoder_module_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1223_cast_fp16)[name = string("input_1225_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136254080)))];
+            tensor<fp16, [4096]> encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1144642752)))];
+            tensor<fp16, [1, 188, 4096]> linear_208_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16, x = input_1225_cast_fp16)[name = string("linear_208_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1229_cast_fp16 = silu(x = linear_208_cast_fp16)[name = string("input_1229_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1144651008)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1153039680)))];
+            tensor<fp16, [1, 188, 1024]> linear_209_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16, x = input_1229_cast_fp16)[name = string("linear_209_cast_fp16")];
+            fp16 var_4509_to_fp16 = const()[name = string("op_4509_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4510_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_4509_to_fp16)[name = string("op_4510_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1235_cast_fp16 = add(x = input_1223_cast_fp16, y = var_4510_cast_fp16)[name = string("input_1235_cast_fp16")];
+            tensor<int32, [1]> query_axes_0 = const()[name = string("query_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1153041792)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1153043904)))];
+            tensor<fp16, [1, 188, 1024]> query_cast_fp16 = layer_norm(axes = query_axes_0, beta = encoder_module_layers_23_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_self_att_weight_to_fp16, x = input_1235_cast_fp16)[name = string("query_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_q_weight_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_q_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1153046016)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155143232)))];
+            tensor<fp16, [1, 188, 1024]> linear_210_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_q_weight_to_fp16, x = query_cast_fp16)[name = string("linear_210_cast_fp16")];
+            tensor<int32, [4]> var_4527 = const()[name = string("op_4527"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_139_cast_fp16 = reshape(shape = var_4527, x = linear_210_cast_fp16)[name = string("q_139_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_k_weight_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_k_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155145344)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1157242560)))];
+            tensor<fp16, [1, 188, 1024]> linear_211_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_k_weight_to_fp16, x = query_cast_fp16)[name = string("linear_211_cast_fp16")];
+            tensor<int32, [4]> var_4532 = const()[name = string("op_4532"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_93_cast_fp16 = reshape(shape = var_4532, x = linear_211_cast_fp16)[name = string("k_93_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_v_weight_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_v_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1157244672)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1159341888)))];
+            tensor<fp16, [1, 188, 1024]> linear_212_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_v_weight_to_fp16, x = query_cast_fp16)[name = string("linear_212_cast_fp16")];
+            tensor<int32, [4]> var_4537 = const()[name = string("op_4537"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_cast_fp16 = reshape(shape = var_4537, x = linear_212_cast_fp16)[name = string("v_cast_fp16")];
+            tensor<int32, [4]> value_perm_0 = const()[name = string("value_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [8, 128]> encoder_module_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1159344000)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4549_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_module_layers_23_self_attn_pos_bias_u_to_fp16)[name = string("op_4549_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1159346112)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4551_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_module_layers_23_self_attn_pos_bias_v_to_fp16)[name = string("op_4551_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_perm_0 = const()[name = string("q_with_bias_v_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            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<fp16, [1, 8, 128, 375]> var_4553_to_fp16 = const()[name = string("op_4553_to_fp16"), val = tensor<fp16, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1159348224)))];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4551_cast_fp16)[name = string("transpose_126")];
+            tensor<fp16, [1, 8, 188, 375]> x_533_cast_fp16 = matmul(transpose_x = x_533_transpose_x_0, transpose_y = x_533_transpose_y_0, x = q_with_bias_v_cast_fp16, y = var_4553_to_fp16)[name = string("x_533_cast_fp16")];
+            tensor<int32, [8]> x_535_pad_0 = const()[name = string("x_535_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_535_mode_0 = const()[name = string("x_535_mode_0"), val = string("constant")];
+            fp16 const_318_to_fp16 = const()[name = string("const_318_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_535_cast_fp16 = pad(constant_val = const_318_to_fp16, mode = x_535_mode_0, pad = x_535_pad_0, x = x_533_cast_fp16)[name = string("x_535_cast_fp16")];
+            tensor<int32, [4]> var_4561 = const()[name = string("op_4561"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_537_cast_fp16 = reshape(shape = var_4561, x = x_535_cast_fp16)[name = string("x_537_cast_fp16")];
+            tensor<int32, [4]> var_4565_begin_0 = const()[name = string("op_4565_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4565_end_0 = const()[name = string("op_4565_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4565_end_mask_0 = const()[name = string("op_4565_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4565_cast_fp16 = slice_by_index(begin = var_4565_begin_0, end = var_4565_end_0, end_mask = var_4565_end_mask_0, x = x_537_cast_fp16)[name = string("op_4565_cast_fp16")];
+            tensor<int32, [4]> var_4566 = const()[name = string("op_4566"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_93_cast_fp16 = reshape(shape = var_4566, x = var_4565_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<int32, [4]> transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_119 = transpose(perm = transpose_119_perm_0, x = k_93_cast_fp16)[name = string("transpose_124")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_118 = transpose(perm = transpose_118_perm_0, x = var_4549_cast_fp16)[name = string("transpose_125")];
+            tensor<fp16, [1, 8, 188, 188]> matrix_ac_cast_fp16 = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_118, y = transpose_119)[name = string("matrix_ac_cast_fp16")];
+            tensor<int32, [4]> matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_end_0 = const()[name = string("matrix_bd_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4575_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = string("op_4575_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<fp16, [1, 8, 188, 188]> _inversed_scores_93_cast_fp16 = mul(x = var_4575_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = string("_inversed_scores_93_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_15)[name = string("scores_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4581_cast_fp16 = softmax(axis = var_152, x = scores_cast_fp16)[name = string("op_4581_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1237_cast_fp16 = select(a = var_164_to_fp16, b = var_4581_cast_fp16, cond = mask_15)[name = string("input_1237_cast_fp16")];
+            bool x_539_transpose_x_0 = const()[name = string("x_539_transpose_x_0"), val = bool(false)];
+            bool x_539_transpose_y_0 = const()[name = string("x_539_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = string("transpose_127")];
+            tensor<fp16, [1, 8, 188, 128]> x_539_cast_fp16 = matmul(transpose_x = x_539_transpose_x_0, transpose_y = x_539_transpose_y_0, x = input_1237_cast_fp16, y = value_cast_fp16)[name = string("x_539_cast_fp16")];
+            tensor<int32, [4]> var_4585_perm_0 = const()[name = string("op_4585_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4586 = const()[name = string("op_4586"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4585_cast_fp16 = transpose(perm = var_4585_perm_0, x = x_539_cast_fp16)[name = string("transpose_123")];
+            tensor<fp16, [1, 188, 1024]> input_1239_cast_fp16 = reshape(shape = var_4586, x = var_4585_cast_fp16)[name = string("input_1239_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_out_weight_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_out_weight_to_fp16"), val = tensor<fp16, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1160116288)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1162213504)))];
+            tensor<fp16, [1, 188, 1024]> linear_214_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_out_weight_to_fp16, x = input_1239_cast_fp16)[name = string("linear_214_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1243_cast_fp16 = add(x = input_1235_cast_fp16, y = linear_214_cast_fp16)[name = string("input_1243_cast_fp16")];
+            tensor<int32, [1]> x_543_axes_0 = const()[name = string("x_543_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1162215616)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1162217728)))];
+            tensor<fp16, [1, 188, 1024]> x_543_cast_fp16 = layer_norm(axes = x_543_axes_0, beta = encoder_module_layers_23_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_conv_weight_to_fp16, x = input_1243_cast_fp16)[name = string("x_543_cast_fp16")];
+            tensor<int32, [3]> input_1245_perm_0 = const()[name = string("input_1245_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1247_pad_type_0 = const()[name = string("input_1247_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1247_strides_0 = const()[name = string("input_1247_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1247_pad_0 = const()[name = string("input_1247_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1247_dilations_0 = const()[name = string("input_1247_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1247_groups_0 = const()[name = string("input_1247_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_23_conv_pointwise_conv1_weight_to_fp16 = const()[name = string("encoder_module_layers_23_conv_pointwise_conv1_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1162219840)))];
+            tensor<fp16, [2048]> encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1166414208)))];
+            tensor<fp16, [1, 1024, 188]> input_1245_cast_fp16 = transpose(perm = input_1245_perm_0, x = x_543_cast_fp16)[name = string("transpose_122")];
+            tensor<fp16, [1, 2048, 188]> input_1247_cast_fp16 = conv(bias = encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_23_conv_pointwise_conv1_weight_to_fp16, x = input_1245_cast_fp16)[name = string("input_1247_cast_fp16")];
+            int32 x_545_split_num_splits_0 = const()[name = string("x_545_split_num_splits_0"), val = int32(2)];
+            int32 x_545_split_axis_0 = const()[name = string("x_545_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_545_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_545_split_cast_fp16_1 = split(axis = x_545_split_axis_0, num_splits = x_545_split_num_splits_0, x = input_1247_cast_fp16)[name = string("x_545_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_545_split_1_sigmoid_cast_fp16 = sigmoid(x = x_545_split_cast_fp16_1)[name = string("x_545_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_545_cast_fp16 = mul(x = x_545_split_cast_fp16_0, y = x_545_split_1_sigmoid_cast_fp16)[name = string("x_545_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1249_cast_fp16 = select(a = var_164_to_fp16, b = x_545_cast_fp16, cond = var_608)[name = string("input_1249_cast_fp16")];
+            tensor<int32, [6]> input_1251_pad_0 = const()[name = string("input_1251_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1251_mode_0 = const()[name = string("input_1251_mode_0"), val = string("constant")];
+            fp16 const_321_to_fp16 = const()[name = string("const_321_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1251_cast_fp16 = pad(constant_val = const_321_to_fp16, mode = input_1251_mode_0, pad = input_1251_pad_0, x = input_1249_cast_fp16)[name = string("input_1251_cast_fp16")];
+            string input_1253_pad_type_0 = const()[name = string("input_1253_pad_type_0"), val = string("valid")];
+            int32 input_1253_groups_0 = const()[name = string("input_1253_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1253_strides_0 = const()[name = string("input_1253_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1253_pad_0 = const()[name = string("input_1253_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1253_dilations_0 = const()[name = string("input_1253_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_368_to_fp16 = const()[name = string("const_368_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1166418368)))];
+            tensor<fp16, [1024]> const_369_to_fp16 = const()[name = string("const_369_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1166436864)))];
+            tensor<fp16, [1, 1024, 188]> input_1255_cast_fp16 = conv(bias = const_369_to_fp16, dilations = input_1253_dilations_0, groups = input_1253_groups_0, pad = input_1253_pad_0, pad_type = input_1253_pad_type_0, strides = input_1253_strides_0, weight = const_368_to_fp16, x = input_1251_cast_fp16)[name = string("input_1255_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1257_cast_fp16 = silu(x = input_1255_cast_fp16)[name = string("input_1257_cast_fp16")];
+            string x_547_pad_type_0 = const()[name = string("x_547_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_547_strides_0 = const()[name = string("x_547_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_547_pad_0 = const()[name = string("x_547_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_547_dilations_0 = const()[name = string("x_547_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_547_groups_0 = const()[name = string("x_547_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16 = const()[name = string("encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1166438976)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1168536192)))];
+            tensor<fp16, [1, 1024, 188]> x_547_cast_fp16 = conv(bias = encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16, dilations = x_547_dilations_0, groups = x_547_groups_0, pad = x_547_pad_0, pad_type = x_547_pad_type_0, strides = x_547_strides_0, weight = encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16, x = input_1257_cast_fp16)[name = string("x_547_cast_fp16")];
+            tensor<int32, [3]> input_1259_perm_0 = const()[name = string("input_1259_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1259_cast_fp16 = transpose(perm = input_1259_perm_0, x = x_547_cast_fp16)[name = string("transpose_121")];
+            tensor<fp16, [1, 188, 1024]> input_1261_cast_fp16 = add(x = input_1243_cast_fp16, y = input_1259_cast_fp16)[name = string("input_1261_cast_fp16")];
+            tensor<int32, [1]> input_1263_axes_0 = const()[name = string("input_1263_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1168538304)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1168540416)))];
+            tensor<fp16, [1, 188, 1024]> input_1263_cast_fp16 = layer_norm(axes = input_1263_axes_0, beta = encoder_module_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1261_cast_fp16)[name = string("input_1263_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16"), val = tensor<fp16, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1168542528)))];
+            tensor<fp16, [4096]> encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1176931200)))];
+            tensor<fp16, [1, 188, 4096]> linear_215_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16, x = input_1263_cast_fp16)[name = string("linear_215_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1267_cast_fp16 = silu(x = linear_215_cast_fp16)[name = string("input_1267_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16"), val = tensor<fp16, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1176939456)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1185328128)))];
+            tensor<fp16, [1, 188, 1024]> linear_216_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16, x = input_1267_cast_fp16)[name = string("linear_216_cast_fp16")];
+            fp16 var_4652_to_fp16 = const()[name = string("op_4652_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4653_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_4652_to_fp16)[name = string("op_4653_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_cast_fp16 = add(x = input_1261_cast_fp16, y = var_4653_cast_fp16)[name = string("input_cast_fp16")];
+            tensor<int32, [1]> audio_signal_axes_0 = const()[name = string("audio_signal_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1185330240)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1185332352)))];
+            tensor<fp16, [1, 188, 1024]> audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = encoder_module_layers_23_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = string("audio_signal_cast_fp16")];
+            tensor<int32, [3]> obj_3_perm_0 = const()[name = string("obj_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp16, [1, 1024, 188]> obj_3_cast_fp16 = transpose(perm = obj_3_perm_0, x = audio_signal_cast_fp16)[name = string("transpose_120")];
+            tensor<fp32, [1, 1024, 188]> encoder = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_231")];
+        } -> (encoder, encoder_length);
+}
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/weights/weight.bin b/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..ccc780a60d16fca9d0a52506c11da76f57ae725b
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:94f8809383b04af30f1f248d47f4cb159fcf58dd6dccb93158c8600fad16f158
+size 1185334464
diff --git a/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/analytics/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/analytics/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..02847b7d5d602ec39926074bac91a5b51524768c
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+++ b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..dc7d752357ad13592893f618318f4b0b73c18493
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8bb8115c6d918526b13db13e228e22dd8010269a545d922d83d6a10ab0dedc42
+size 463
diff --git a/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/metadata.json b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..c6f794f8bbeab88377398233532f2283a442641d
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/metadata.json
@@ -0,0 +1,70 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "shortDescription" : "Parakeet CTC decoder head (encoder -> log_probs)",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "log_probs",
+        "type" : "MultiArray"
+      }
+    ],
+    "storagePrecision" : "Mixed (Float16, Palettized (4 bits), UInt4)",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.transpose" : 1,
+      "Ios18.constexprLutToDense" : 1,
+      "Ios18.cast" : 2,
+      "Ios18.conv" : 1,
+      "Ios18.softmax" : 1,
+      "Ios18.log" : 1
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 1024 × 1...188",
+        "shapeRange" : "[[1, 1], [1024, 1024], [1, 188]]",
+        "formattedType" : "MultiArray (Float32 1 × 1024 × 1)",
+        "type" : "MultiArray",
+        "shape" : "[1, 1024, 1]",
+        "name" : "encoder",
+        "shortDescription" : ""
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-02-27",
+      "com.github.apple.coremltools.source" : "torch==2.10.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "parakeet_ctc_decoder",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/model.mil b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..30cab9b431f64df91c5ca19c24994415c9842c3e
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/model.mil
@@ -0,0 +1,24 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 1024, ?]> encoder) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"encoder", [1, 1024, 1]}}), ("RangeDims", {{"encoder", [[1, 1], [1024, 1024], [1, 188]]}})))] {
+            int32 var_4 = const()[name = string("op_4"), val = int32(-1)];
+            string var_18_pad_type_0 = const()[name = string("op_18_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_18_strides_0 = const()[name = string("op_18_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_18_pad_0 = const()[name = string("op_18_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_18_dilations_0 = const()[name = string("op_18_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_18_groups_0 = const()[name = string("op_18_groups_0"), val = int32(1)];
+            string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1025, 1024, 1]> module_decoder_layers_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1025, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524928))))[name = string("module_decoder_layers_0_weight_to_fp16_palettized")];
+            tensor<fp16, [1025]> module_decoder_layers_0_bias_to_fp16 = const()[name = string("module_decoder_layers_0_bias_to_fp16"), val = tensor<fp16, [1025]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525056)))];
+            tensor<fp16, [1, 1024, ?]> encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_1")];
+            tensor<fp16, [1, 1025, ?]> var_18_cast_fp16 = conv(bias = module_decoder_layers_0_bias_to_fp16, dilations = var_18_dilations_0, groups = var_18_groups_0, pad = var_18_pad_0, pad_type = var_18_pad_type_0, strides = var_18_strides_0, weight = module_decoder_layers_0_weight_to_fp16_palettized, x = encoder_to_fp16)[name = string("op_18_cast_fp16")];
+            tensor<int32, [3]> input_perm_0 = const()[name = string("input_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, ?, 1025]> input_cast_fp16 = transpose(perm = input_perm_0, x = var_18_cast_fp16)[name = string("transpose_0")];
+            tensor<fp16, [1, ?, 1025]> out_objects_softmax_cast_fp16 = softmax(axis = var_4, x = input_cast_fp16)[name = string("out_objects_softmax_cast_fp16")];
+            fp32 out_objects_epsilon_0 = const()[name = string("out_objects_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [1, ?, 1025]> out_objects_cast_fp16 = log(epsilon = out_objects_epsilon_0, x = out_objects_softmax_cast_fp16)[name = string("out_objects_cast_fp16")];
+            string out_objects_cast_fp16_to_fp32_dtype_0 = const()[name = string("out_objects_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [1, ?, 1025]> log_probs = cast(dtype = out_objects_cast_fp16_to_fp32_dtype_0, x = out_objects_cast_fp16)[name = string("cast_0")];
+        } -> (log_probs);
+}
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/weights/weight.bin b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..6023d1e172139687ca157f3cec725a9728edf156
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+++ b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a81ba8ca75a2a6bed883f6dc219d52bfe3ac9cec5929230f3ea577d47bed51e5
+size 527170
diff --git a/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..c220311c0c812b4a64390b43428bc8027249c315
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+++ b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e5a32996f7cdfb5cb2d252122710546e08cf98c7b1743878e9be8e82f2a31ec2
+size 243
diff --git a/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..eebe76097ef1a3b9eebc22382863ffae8b6d9b80
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9981193e84379ddd5c84a17ed58e95fa4185e49cdc6c075834a3279fefc9350e
+size 496
diff --git a/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/metadata.json b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..0f9352c23b3b4e53f9f3e79330f2c54559984109
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/metadata.json
@@ -0,0 +1,117 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "shortDescription" : "Parakeet CTC Mel+Encoder (15.0s window)",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 1024 × 188)",
+        "shortDescription" : "",
+        "shape" : "[1, 1024, 188]",
+        "name" : "encoder",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "encoder_length",
+        "type" : "MultiArray"
+      }
+    ],
+    "storagePrecision" : "Mixed (Float16, Int32, Palettized (4 bits), UInt4)",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Stack" : 1,
+      "Ios18.conv" : 79,
+      "Ios18.mul" : 107,
+      "Ios18.constexprLutToDense" : 298,
+      "Ios18.cast" : 12,
+      "Ios18.layerNorm" : 120,
+      "Ios18.log" : 1,
+      "Ios18.equal" : 1,
+      "Ios18.floorDiv" : 4,
+      "Ios16.reduceSum" : 4,
+      "Ios18.logicalNot" : 3,
+      "Ios18.reshape" : 147,
+      "Pad" : 49,
+      "Ios18.concat" : 1,
+      "Ios18.add" : 180,
+      "Ios18.realDiv" : 3,
+      "Ios18.greaterEqual" : 1,
+      "Ios18.notEqual" : 1,
+      "Ios18.relu" : 3,
+      "Ios18.sub" : 7,
+      "Ios18.matmul" : 73,
+      "Ios18.silu" : 72,
+      "Ios18.expandDims" : 25,
+      "Ios18.linear" : 193,
+      "Ios18.sigmoid" : 24,
+      "Ios18.sliceByIndex" : 51,
+      "Ios18.transpose" : 172,
+      "Split" : 24,
+      "Ios18.sqrt" : 1,
+      "Ios18.softmax" : 24,
+      "Ios18.pow" : 2,
+      "Select" : 78,
+      "Ios18.logicalAnd" : 2,
+      "Ios18.less" : 6,
+      "Tile" : 9
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 240000)",
+        "shortDescription" : "",
+        "shape" : "[1, 240000]",
+        "name" : "audio_signal",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "audio_length",
+        "type" : "MultiArray"
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-02-27",
+      "com.github.apple.coremltools.source" : "torch==2.10.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "parakeet_ctc_mel_encoder",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/model.mil b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..c62a26b3b60c9afd67d530b8e92e510d088e7787
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/model.mil
@@ -0,0 +1,3838 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}})]
+{
+    func main<ios18>(tensor<int32, [1]> audio_length, tensor<fp32, [1, 240000]> audio_signal) {
+            int32 var_20 = const()[name = string("op_20"), val = int32(0)];
+            int32 var_21 = const()[name = string("op_21"), val = int32(160)];
+            int32 var_22 = const()[name = string("op_22"), val = int32(1)];
+            int32 var_32 = const()[name = string("op_32"), val = int32(512)];
+            tensor<int32, [1]> var_33 = add(x = audio_length, y = var_32)[name = string("op_33")];
+            int32 var_34 = const()[name = string("op_34"), val = int32(512)];
+            tensor<int32, [1]> var_35 = sub(x = var_33, y = var_34)[name = string("op_35")];
+            tensor<int32, [1]> floor_div_0 = floor_div(x = var_35, y = var_21)[name = string("floor_div_0")];
+            tensor<bool, [1]> var_38 = equal(x = audio_length, y = var_20)[name = string("op_38")];
+            tensor<int32, [1]> var_39 = const()[name = string("op_39"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1]> seq_len = select(a = var_39, b = floor_div_0, cond = var_38)[name = string("seq_len")];
+            tensor<int32, [1, 240000]> var_43 = const()[name = string("op_43"), val = tensor<int32, [1, 240000]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<int32, [1]> var_44_axes_0 = const()[name = string("op_44_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1]> var_44 = expand_dims(axes = var_44_axes_0, x = audio_length)[name = string("op_44")];
+            tensor<bool, [1, 240000]> timemask = less(x = var_43, y = var_44)[name = string("timemask")];
+            tensor<int32, [2]> var_47_begin_0 = const()[name = string("op_47_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_47_end_0 = const()[name = string("op_47_end_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<bool, [2]> var_47_end_mask_0 = const()[name = string("op_47_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<bool, [2]> var_47_squeeze_mask_0 = const()[name = string("op_47_squeeze_mask_0"), val = tensor<bool, [2]>([false, true])];
+            string audio_signal_to_fp16_dtype_0 = const()[name = string("audio_signal_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 240000]> audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = string("cast_11")];
+            tensor<fp16, [1]> var_47_cast_fp16 = slice_by_index(begin = var_47_begin_0, end = var_47_end_0, end_mask = var_47_end_mask_0, squeeze_mask = var_47_squeeze_mask_0, x = audio_signal_to_fp16)[name = string("op_47_cast_fp16")];
+            tensor<int32, [1]> var_48_axes_0 = const()[name = string("op_48_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_48_cast_fp16 = expand_dims(axes = var_48_axes_0, x = var_47_cast_fp16)[name = string("op_48_cast_fp16")];
+            tensor<int32, [2]> var_50_begin_0 = const()[name = string("op_50_begin_0"), val = tensor<int32, [2]>([0, 1])];
+            tensor<int32, [2]> var_50_end_0 = const()[name = string("op_50_end_0"), val = tensor<int32, [2]>([1, 240000])];
+            tensor<bool, [2]> var_50_end_mask_0 = const()[name = string("op_50_end_mask_0"), val = tensor<bool, [2]>([true, true])];
+            tensor<fp16, [1, 239999]> var_50_cast_fp16 = slice_by_index(begin = var_50_begin_0, end = var_50_end_0, end_mask = var_50_end_mask_0, x = audio_signal_to_fp16)[name = string("op_50_cast_fp16")];
+            tensor<int32, [2]> var_52_begin_0 = const()[name = string("op_52_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_52_end_0 = const()[name = string("op_52_end_0"), val = tensor<int32, [2]>([1, 239999])];
+            tensor<bool, [2]> var_52_end_mask_0 = const()[name = string("op_52_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<fp16, [1, 239999]> var_52_cast_fp16 = slice_by_index(begin = var_52_begin_0, end = var_52_end_0, end_mask = var_52_end_mask_0, x = audio_signal_to_fp16)[name = string("op_52_cast_fp16")];
+            fp16 var_53_to_fp16 = const()[name = string("op_53_to_fp16"), val = fp16(0x1.f0cp-1)];
+            tensor<fp16, [1, 239999]> var_54_cast_fp16 = mul(x = var_52_cast_fp16, y = var_53_to_fp16)[name = string("op_54_cast_fp16")];
+            tensor<fp16, [1, 239999]> var_55_cast_fp16 = sub(x = var_50_cast_fp16, y = var_54_cast_fp16)[name = string("op_55_cast_fp16")];
+            bool x_3_interleave_0 = const()[name = string("x_3_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 240000]> x_3_cast_fp16 = concat(axis = var_22, interleave = x_3_interleave_0, values = (var_48_cast_fp16, var_55_cast_fp16))[name = string("x_3_cast_fp16")];
+            tensor<bool, [1, 240000]> var_58 = logical_not(x = timemask)[name = string("op_58")];
+            fp16 var_16_to_fp16 = const()[name = string("op_16_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 240000]> input_1_cast_fp16 = select(a = var_16_to_fp16, b = x_3_cast_fp16, cond = var_58)[name = string("input_1_cast_fp16")];
+            tensor<int32, [3]> var_63 = const()[name = string("op_63"), val = tensor<int32, [3]>([1, 1, 240000])];
+            tensor<fp16, [1, 1, 240000]> input_3_cast_fp16 = reshape(shape = var_63, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")];
+            tensor<int32, [6]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 256])];
+            string input_5_mode_0 = const()[name = string("input_5_mode_0"), val = string("constant")];
+            fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1, 240512]> input_5_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")];
+            tensor<int32, [2]> var_69 = const()[name = string("op_69"), val = tensor<int32, [2]>([1, 240512])];
+            tensor<fp16, [1, 240512]> input_7_cast_fp16 = reshape(shape = var_69, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")];
+            tensor<int32, [1]> expand_dims_10 = const()[name = string("expand_dims_10"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [1]> expand_dims_11_axes_0 = const()[name = string("expand_dims_11_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 240512]> expand_dims_11_cast_fp16 = expand_dims(axes = expand_dims_11_axes_0, x = input_7_cast_fp16)[name = string("expand_dims_11_cast_fp16")];
+            string conv_0_pad_type_0 = const()[name = string("conv_0_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv_0_pad_0 = const()[name = string("conv_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_0_dilations_0 = const()[name = string("conv_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 conv_0_groups_0 = const()[name = string("conv_0_groups_0"), val = int32(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_8_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [257, 1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(960128))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1025984))))[name = string("expand_dims_8_to_fp16_palettized")];
+            tensor<fp16, [1, 257, 1501]> 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_10, weight = expand_dims_8_to_fp16_palettized, x = expand_dims_11_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<int32, [2]> conv_1_pad_0 = const()[name = string("conv_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_1_dilations_0 = const()[name = string("conv_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 conv_1_groups_0 = const()[name = string("conv_1_groups_0"), val = int32(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_9_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [257, 1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1026112))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1091968))))[name = string("expand_dims_9_to_fp16_palettized")];
+            tensor<fp16, [1, 257, 1501]> 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_10, weight = expand_dims_9_to_fp16_palettized, x = expand_dims_11_cast_fp16)[name = string("conv_1_cast_fp16")];
+            int32 stack_0_axis_0 = const()[name = string("stack_0_axis_0"), val = int32(-1)];
+            tensor<fp16, [1, 257, 1501, 2]> 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_13_promoted_to_fp16 = const()[name = string("op_13_promoted_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1, 257, 1501, 2]> var_73_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_13_promoted_to_fp16)[name = string("op_73_cast_fp16")];
+            tensor<int32, [1]> var_75_axes_0 = const()[name = string("op_75_axes_0"), val = tensor<int32, [1]>([-1])];
+            bool var_75_keep_dims_0 = const()[name = string("op_75_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 257, 1501]> var_75_cast_fp16 = reduce_sum(axes = var_75_axes_0, keep_dims = var_75_keep_dims_0, x = var_73_cast_fp16)[name = string("op_75_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<fp16, [1, 80, 257]> const_9_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 80, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1092096))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1102464))))[name = string("const_9_to_fp16_palettized")];
+            tensor<fp16, [1, 80, 1501]> x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = const_9_to_fp16_palettized, y = var_75_cast_fp16)[name = string("x_13_cast_fp16")];
+            fp16 var_82_to_fp16 = const()[name = string("op_82_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [1, 80, 1501]> var_83_cast_fp16 = add(x = x_13_cast_fp16, y = var_82_to_fp16)[name = string("op_83_cast_fp16")];
+            fp32 x_15_epsilon_0 = const()[name = string("x_15_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [1, 80, 1501]> x_15_cast_fp16 = log(epsilon = x_15_epsilon_0, x = var_83_cast_fp16)[name = string("x_15_cast_fp16")];
+            tensor<int32, [1, 1501]> var_88 = const()[name = string("op_88"), val = tensor<int32, [1, 1501]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1102592)))];
+            tensor<int32, [1]> var_91_axes_0 = const()[name = string("op_91_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1]> var_91 = expand_dims(axes = var_91_axes_0, x = seq_len)[name = string("op_91")];
+            tensor<bool, [1, 1501]> valid_mask = less(x = var_88, y = var_91)[name = string("valid_mask")];
+            tensor<int32, [1]> var_93_axes_0 = const()[name = string("op_93_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_93 = expand_dims(axes = var_93_axes_0, x = valid_mask)[name = string("op_93")];
+            tensor<int32, [3]> var_93_after_broadcast_reps_0 = const()[name = string("op_93_after_broadcast_reps_0"), val = tensor<int32, [3]>([1, 80, 1])];
+            tensor<bool, [1, 80, 1501]> var_93_after_broadcast = tile(reps = var_93_after_broadcast_reps_0, x = var_93)[name = string("op_93_after_broadcast")];
+            tensor<fp16, [1, 80, 1501]> op_16_after_broadcast_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 80, 1501]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1108672))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1168832))))[name = string("op_16_after_broadcast_to_fp16_palettized")];
+            tensor<fp16, [1, 80, 1501]> var_94_cast_fp16 = select(a = x_15_cast_fp16, b = op_16_after_broadcast_to_fp16_palettized, cond = var_93_after_broadcast)[name = string("op_94_cast_fp16")];
+            tensor<int32, [1]> x_mean_numerator_axes_0 = const()[name = string("x_mean_numerator_axes_0"), val = tensor<int32, [1]>([2])];
+            bool x_mean_numerator_keep_dims_0 = const()[name = string("x_mean_numerator_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 80]> x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_94_cast_fp16)[name = string("x_mean_numerator_cast_fp16")];
+            tensor<int32, [1]> x_mean_denominator_axes_0 = const()[name = string("x_mean_denominator_axes_0"), val = tensor<int32, [1]>([1])];
+            bool x_mean_denominator_keep_dims_0 = const()[name = string("x_mean_denominator_keep_dims_0"), val = bool(false)];
+            string cast_2_to_fp16_dtype_0 = const()[name = string("cast_2_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 1501]> valid_mask_to_fp16 = cast(dtype = cast_2_to_fp16_dtype_0, x = valid_mask)[name = string("cast_10")];
+            tensor<fp16, [1]> x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = string("x_mean_denominator_cast_fp16")];
+            tensor<int32, [1]> var_99_axes_0 = const()[name = string("op_99_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_99_cast_fp16 = expand_dims(axes = var_99_axes_0, x = x_mean_denominator_cast_fp16)[name = string("op_99_cast_fp16")];
+            tensor<fp16, [1, 80]> x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_99_cast_fp16)[name = string("x_mean_cast_fp16")];
+            tensor<int32, [1]> var_102_axes_0 = const()[name = string("op_102_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 80, 1]> var_102_cast_fp16 = expand_dims(axes = var_102_axes_0, x = x_mean_cast_fp16)[name = string("op_102_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> var_103_cast_fp16 = sub(x = x_15_cast_fp16, y = var_102_cast_fp16)[name = string("op_103_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> var_104_cast_fp16 = select(a = var_103_cast_fp16, b = op_16_after_broadcast_to_fp16_palettized, cond = var_93_after_broadcast)[name = string("op_104_cast_fp16")];
+            fp16 var_13_promoted_1_to_fp16 = const()[name = string("op_13_promoted_1_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1, 80, 1501]> var_105_cast_fp16 = pow(x = var_104_cast_fp16, y = var_13_promoted_1_to_fp16)[name = string("op_105_cast_fp16")];
+            tensor<int32, [1]> var_107_axes_0 = const()[name = string("op_107_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_107_keep_dims_0 = const()[name = string("op_107_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 80]> var_107_cast_fp16 = reduce_sum(axes = var_107_axes_0, keep_dims = var_107_keep_dims_0, x = var_105_cast_fp16)[name = string("op_107_cast_fp16")];
+            fp16 var_109_to_fp16 = const()[name = string("op_109_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1]> var_110_cast_fp16 = sub(x = var_99_cast_fp16, y = var_109_to_fp16)[name = string("op_110_cast_fp16")];
+            tensor<fp16, [1, 80]> var_111_cast_fp16 = real_div(x = var_107_cast_fp16, y = var_110_cast_fp16)[name = string("op_111_cast_fp16")];
+            tensor<fp16, [1, 80]> x_std_1_cast_fp16 = sqrt(x = var_111_cast_fp16)[name = string("x_std_1_cast_fp16")];
+            tensor<bool, [1, 80]> var_113_cast_fp16 = not_equal(x = x_std_1_cast_fp16, y = x_std_1_cast_fp16)[name = string("op_113_cast_fp16")];
+            tensor<fp16, [1, 80]> x_std_3_cast_fp16 = select(a = var_16_to_fp16, b = x_std_1_cast_fp16, cond = var_113_cast_fp16)[name = string("x_std_3_cast_fp16")];
+            fp16 var_7_to_fp16 = const()[name = string("op_7_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 80]> x_std_cast_fp16 = add(x = x_std_3_cast_fp16, y = var_7_to_fp16)[name = string("x_std_cast_fp16")];
+            tensor<int32, [1]> var_118_axes_0 = const()[name = string("op_118_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 80, 1]> var_118_cast_fp16 = expand_dims(axes = var_118_axes_0, x = x_std_cast_fp16)[name = string("op_118_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> x_17_cast_fp16 = real_div(x = var_103_cast_fp16, y = var_118_cast_fp16)[name = string("x_17_cast_fp16")];
+            tensor<bool, [1, 1501]> mask_3 = greater_equal(x = var_88, y = var_91)[name = string("mask_3")];
+            tensor<int32, [1]> var_127_axes_0 = const()[name = string("op_127_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_127 = expand_dims(axes = var_127_axes_0, x = mask_3)[name = string("op_127")];
+            tensor<fp16, [1, 80, 1501]> processed_signal_cast_fp16 = select(a = var_16_to_fp16, b = x_17_cast_fp16, cond = var_127)[name = string("processed_signal_cast_fp16")];
+            int32 var_152 = const()[name = string("op_152"), val = int32(-1)];
+            tensor<int32, [3]> x_19_perm_0 = const()[name = string("x_19_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> tensor_1_axes_0 = const()[name = string("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1501, 80]> x_19_cast_fp16 = transpose(perm = x_19_perm_0, x = processed_signal_cast_fp16)[name = string("transpose_315")];
+            tensor<fp16, [1, 1, 1501, 80]> tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_19_cast_fp16)[name = string("tensor_1_cast_fp16")];
+            tensor<int32, [1]> var_242_axes_0 = const()[name = string("op_242_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 1501, 1]> var_242 = expand_dims(axes = var_242_axes_0, x = valid_mask)[name = string("op_242")];
+            tensor<int32, [3]> var_244_reps_0 = const()[name = string("op_244_reps_0"), val = tensor<int32, [3]>([1, 1, 80])];
+            tensor<bool, [1, 1501, 80]> var_244 = tile(reps = var_244_reps_0, x = var_242)[name = string("op_244")];
+            tensor<int32, [1]> var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_5_to_fp16_dtype_0 = const()[name = string("mask_5_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 1501, 80]> var_244_to_fp16 = cast(dtype = mask_5_to_fp16_dtype_0, x = var_244)[name = string("cast_9")];
+            tensor<fp16, [1, 1, 1501, 80]> var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = var_244_to_fp16)[name = string("op_250_cast_fp16")];
+            tensor<fp16, [1, 1, 1501, 80]> input_9_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_250_cast_fp16)[name = string("input_9_cast_fp16")];
+            string tensor_3_pad_type_0 = const()[name = string("tensor_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_3_pad_0 = const()[name = string("tensor_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_3_strides_0 = const()[name = string("tensor_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> tensor_3_dilations_0 = const()[name = string("tensor_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_3_groups_0 = const()[name = string("tensor_3_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1168960))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1170176))))[name = string("encoder_module_pre_encode_conv_0_weight_to_fp16_palettized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_0_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1170304)))];
+            tensor<fp16, [1, 256, 751, 40]> tensor_3_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_0_weight_to_fp16_palettized, x = input_9_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_261_promoted_to_fp16 = const()[name = string("op_261_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> seq_len_to_fp16 = cast(dtype = current_lengths_1_to_fp16_dtype_0, x = seq_len)[name = string("cast_8")];
+            tensor<fp16, [1]> var_262_cast_fp16 = add(x = seq_len_to_fp16, y = var_261_promoted_to_fp16)[name = string("op_262_cast_fp16")];
+            fp16 var_263_promoted_to_fp16 = const()[name = string("op_263_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_264_cast_fp16 = add(x = var_262_cast_fp16, y = var_263_promoted_to_fp16)[name = string("op_264_cast_fp16")];
+            fp16 var_265_promoted_to_fp16 = const()[name = string("op_265_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_266_cast_fp16 = sub(x = var_264_cast_fp16, y = var_265_promoted_to_fp16)[name = string("op_266_cast_fp16")];
+            fp16 var_154_promoted_to_fp16 = const()[name = string("op_154_promoted_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_1_cast_fp16 = floor_div(x = var_266_cast_fp16, y = var_154_promoted_to_fp16)[name = string("floor_div_1_cast_fp16")];
+            fp16 var_268_promoted_to_fp16 = const()[name = string("op_268_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_3_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_268_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<int32, [1, 751]> expand_dims_4 = const()[name = string("expand_dims_4"), val = tensor<int32, [1, 751]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1170880)))];
+            tensor<int32, [1]> var_277_axes_0 = const()[name = string("op_277_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_3_cast_fp16_to_int32 = cast(dtype = lengths_19_dtype_0, x = current_lengths_3_cast_fp16)[name = string("cast_7")];
+            tensor<int32, [1, 1]> var_277 = expand_dims(axes = var_277_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = string("op_277")];
+            tensor<bool, [1, 751]> time_mask_3 = less(x = expand_dims_4, y = var_277)[name = string("time_mask_3")];
+            tensor<int32, [1]> var_279_axes_0 = const()[name = string("op_279_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 751, 1]> var_279 = expand_dims(axes = var_279_axes_0, x = time_mask_3)[name = string("op_279")];
+            tensor<int32, [3]> var_281_reps_0 = const()[name = string("op_281_reps_0"), val = tensor<int32, [3]>([1, 1, 40])];
+            tensor<bool, [1, 751, 40]> var_281 = tile(reps = var_281_reps_0, x = var_279)[name = string("op_281")];
+            tensor<int32, [1]> var_287_axes_0 = const()[name = string("op_287_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_7_to_fp16_dtype_0 = const()[name = string("mask_7_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 751, 40]> var_281_to_fp16 = cast(dtype = mask_7_to_fp16_dtype_0, x = var_281)[name = string("cast_6")];
+            tensor<fp16, [1, 1, 751, 40]> var_287_cast_fp16 = expand_dims(axes = var_287_axes_0, x = var_281_to_fp16)[name = string("op_287_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_3_reps_0 = const()[name = string("expanded_mask_3_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 751, 40]> expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_287_cast_fp16)[name = string("expanded_mask_3_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> input_11_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_11_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> tensor_5_cast_fp16 = relu(x = input_11_cast_fp16)[name = string("tensor_5_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> input_13_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_13_cast_fp16")];
+            string tensor_7_pad_type_0 = const()[name = string("tensor_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_7_pad_0 = const()[name = string("tensor_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_7_strides_0 = const()[name = string("tensor_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 tensor_7_groups_0 = const()[name = string("tensor_7_groups_0"), val = int32(256)];
+            tensor<int32, [2]> tensor_7_dilations_0 = const()[name = string("tensor_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1173952))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1175168))))[name = string("encoder_module_pre_encode_conv_2_weight_to_fp16_palettized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_2_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1175296)))];
+            tensor<fp16, [1, 256, 376, 20]> tensor_7_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_2_weight_to_fp16_palettized, x = input_13_cast_fp16)[name = string("tensor_7_cast_fp16")];
+            fp16 var_307_promoted_to_fp16 = const()[name = string("op_307_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_308_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_307_promoted_to_fp16)[name = string("op_308_cast_fp16")];
+            fp16 var_309_promoted_to_fp16 = const()[name = string("op_309_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_310_cast_fp16 = add(x = var_308_cast_fp16, y = var_309_promoted_to_fp16)[name = string("op_310_cast_fp16")];
+            fp16 var_311_promoted_to_fp16 = const()[name = string("op_311_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_312_cast_fp16 = sub(x = var_310_cast_fp16, y = var_311_promoted_to_fp16)[name = string("op_312_cast_fp16")];
+            fp16 var_154_promoted_1_to_fp16 = const()[name = string("op_154_promoted_1_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_2_cast_fp16 = floor_div(x = var_312_cast_fp16, y = var_154_promoted_1_to_fp16)[name = string("floor_div_2_cast_fp16")];
+            fp16 var_314_promoted_to_fp16 = const()[name = string("op_314_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_5_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_314_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<int32, [1, 376]> expand_dims_5 = const()[name = string("expand_dims_5"), val = tensor<int32, [1, 376]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1175872)))];
+            tensor<int32, [1]> var_323_axes_0 = const()[name = string("op_323_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_5_cast_fp16_to_int32 = cast(dtype = lengths_21_dtype_0, x = current_lengths_5_cast_fp16)[name = string("cast_5")];
+            tensor<int32, [1, 1]> var_323 = expand_dims(axes = var_323_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = string("op_323")];
+            tensor<bool, [1, 376]> time_mask_5 = less(x = expand_dims_5, y = var_323)[name = string("time_mask_5")];
+            tensor<int32, [1]> var_325_axes_0 = const()[name = string("op_325_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 376, 1]> var_325 = expand_dims(axes = var_325_axes_0, x = time_mask_5)[name = string("op_325")];
+            tensor<int32, [3]> var_327_reps_0 = const()[name = string("op_327_reps_0"), val = tensor<int32, [3]>([1, 1, 20])];
+            tensor<bool, [1, 376, 20]> var_327 = tile(reps = var_327_reps_0, x = var_325)[name = string("op_327")];
+            tensor<int32, [1]> var_333_axes_0 = const()[name = string("op_333_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_9_to_fp16_dtype_0 = const()[name = string("mask_9_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 376, 20]> var_327_to_fp16 = cast(dtype = mask_9_to_fp16_dtype_0, x = var_327)[name = string("cast_4")];
+            tensor<fp16, [1, 1, 376, 20]> var_333_cast_fp16 = expand_dims(axes = var_333_axes_0, x = var_327_to_fp16)[name = string("op_333_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_7_reps_0 = const()[name = string("expanded_mask_7_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 376, 20]> expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_333_cast_fp16)[name = string("expanded_mask_7_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_15_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_15_cast_fp16")];
+            string tensor_9_pad_type_0 = const()[name = string("tensor_9_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> tensor_9_strides_0 = const()[name = string("tensor_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> tensor_9_pad_0 = const()[name = string("tensor_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> tensor_9_dilations_0 = const()[name = string("tensor_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_9_groups_0 = const()[name = string("tensor_9_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 1, 1]> encoder_module_pre_encode_conv_3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1177472))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1210304))))[name = string("encoder_module_pre_encode_conv_3_weight_to_fp16_palettized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_3_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_3_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1210432)))];
+            tensor<fp16, [1, 256, 376, 20]> tensor_9_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_3_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = string("tensor_9_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_17_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_17_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> tensor_11_cast_fp16 = relu(x = input_17_cast_fp16)[name = string("tensor_11_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_19_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_19_cast_fp16")];
+            string tensor_13_pad_type_0 = const()[name = string("tensor_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_13_pad_0 = const()[name = string("tensor_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_13_strides_0 = const()[name = string("tensor_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 tensor_13_groups_0 = const()[name = string("tensor_13_groups_0"), val = int32(256)];
+            tensor<int32, [2]> tensor_13_dilations_0 = const()[name = string("tensor_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_5_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1211008))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1212224))))[name = string("encoder_module_pre_encode_conv_5_weight_to_fp16_palettized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_5_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_5_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1212352)))];
+            tensor<fp16, [1, 256, 188, 10]> tensor_13_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_5_weight_to_fp16_palettized, x = input_19_cast_fp16)[name = string("tensor_13_cast_fp16")];
+            fp16 var_368_promoted_to_fp16 = const()[name = string("op_368_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_369_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_368_promoted_to_fp16)[name = string("op_369_cast_fp16")];
+            fp16 var_370_promoted_to_fp16 = const()[name = string("op_370_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_371_cast_fp16 = add(x = var_369_cast_fp16, y = var_370_promoted_to_fp16)[name = string("op_371_cast_fp16")];
+            fp16 var_372_promoted_to_fp16 = const()[name = string("op_372_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_373_cast_fp16 = sub(x = var_371_cast_fp16, y = var_372_promoted_to_fp16)[name = string("op_373_cast_fp16")];
+            fp16 var_154_promoted_2_to_fp16 = const()[name = string("op_154_promoted_2_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_3_cast_fp16 = floor_div(x = var_373_cast_fp16, y = var_154_promoted_2_to_fp16)[name = string("floor_div_3_cast_fp16")];
+            fp16 var_375_promoted_to_fp16 = const()[name = string("op_375_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_cast_fp16 = add(x = floor_div_3_cast_fp16, y = var_375_promoted_to_fp16)[name = string("current_lengths_cast_fp16")];
+            string lengths_dtype_0 = const()[name = string("lengths_dtype_0"), val = string("int32")];
+            tensor<int32, [1, 188]> expand_dims_6 = const()[name = string("expand_dims_6"), val = tensor<int32, [1, 188]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1212928)))];
+            tensor<int32, [1]> var_384_axes_0 = const()[name = string("op_384_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_cast_fp16_to_int32 = cast(dtype = lengths_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_3")];
+            tensor<int32, [1, 1]> var_384 = expand_dims(axes = var_384_axes_0, x = current_lengths_cast_fp16_to_int32)[name = string("op_384")];
+            tensor<bool, [1, 188]> time_mask = less(x = expand_dims_6, y = var_384)[name = string("time_mask")];
+            tensor<int32, [1]> var_386_axes_0 = const()[name = string("op_386_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 188, 1]> var_386 = expand_dims(axes = var_386_axes_0, x = time_mask)[name = string("op_386")];
+            tensor<int32, [3]> var_388_reps_0 = const()[name = string("op_388_reps_0"), val = tensor<int32, [3]>([1, 1, 10])];
+            tensor<bool, [1, 188, 10]> var_388 = tile(reps = var_388_reps_0, x = var_386)[name = string("op_388")];
+            tensor<int32, [1]> var_394_axes_0 = const()[name = string("op_394_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_11_to_fp16_dtype_0 = const()[name = string("mask_11_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 188, 10]> var_388_to_fp16 = cast(dtype = mask_11_to_fp16_dtype_0, x = var_388)[name = string("cast_2")];
+            tensor<fp16, [1, 1, 188, 10]> var_394_cast_fp16 = expand_dims(axes = var_394_axes_0, x = var_388_to_fp16)[name = string("op_394_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_13_reps_0 = const()[name = string("expanded_mask_13_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 188, 10]> expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_394_cast_fp16)[name = string("expanded_mask_13_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> input_21_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_21_cast_fp16")];
+            string tensor_15_pad_type_0 = const()[name = string("tensor_15_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> tensor_15_strides_0 = const()[name = string("tensor_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> tensor_15_pad_0 = const()[name = string("tensor_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> tensor_15_dilations_0 = const()[name = string("tensor_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_15_groups_0 = const()[name = string("tensor_15_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 1, 1]> encoder_module_pre_encode_conv_6_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1213760))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1246592))))[name = string("encoder_module_pre_encode_conv_6_weight_to_fp16_palettized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_6_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_6_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1246720)))];
+            tensor<fp16, [1, 256, 188, 10]> tensor_15_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_6_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = string("tensor_15_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> input_23_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_23_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> tensor_cast_fp16 = relu(x = input_23_cast_fp16)[name = string("tensor_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> x_21_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("x_21_cast_fp16")];
+            tensor<int32, [4]> var_428_perm_0 = const()[name = string("op_428_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_429 = const()[name = string("op_429"), val = tensor<int32, [3]>([1, 188, -1])];
+            tensor<fp16, [1, 188, 256, 10]> var_428_cast_fp16 = transpose(perm = var_428_perm_0, x = x_21_cast_fp16)[name = string("transpose_314")];
+            tensor<fp16, [1, 188, 2560]> input_25_cast_fp16 = reshape(shape = var_429, x = var_428_cast_fp16)[name = string("input_25_cast_fp16")];
+            tensor<fp16, [1024, 2560]> encoder_module_pre_encode_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1247296))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2558080))))[name = string("encoder_module_pre_encode_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_pre_encode_out_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2558208)))];
+            tensor<fp16, [1, 188, 1024]> linear_0_cast_fp16 = linear(bias = encoder_module_pre_encode_out_bias_to_fp16, weight = encoder_module_pre_encode_out_weight_to_fp16_palettized, x = input_25_cast_fp16)[name = string("linear_0_cast_fp16")];
+            string padding_length_dtype_0 = const()[name = string("padding_length_dtype_0"), val = string("int32")];
+            fp16 var_440_to_fp16 = const()[name = string("op_440_to_fp16"), val = fp16(0x1p+5)];
+            tensor<fp16, [1, 188, 1024]> x_23_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_440_to_fp16)[name = string("x_23_cast_fp16")];
+            tensor<int32, [1]> var_469_axes_0 = const()[name = string("op_469_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<int32, [1]> encoder_length = cast(dtype = padding_length_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_1")];
+            tensor<int32, [1, 1]> var_469 = expand_dims(axes = var_469_axes_0, x = encoder_length)[name = string("op_469")];
+            tensor<bool, [1, 188]> pad_mask_1 = less(x = expand_dims_6, y = var_469)[name = string("pad_mask_1")];
+            tensor<int32, [1]> var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188]> var_471 = expand_dims(axes = var_471_axes_0, x = pad_mask_1)[name = string("op_471")];
+            tensor<int32, [3]> var_472 = const()[name = string("op_472"), val = tensor<int32, [3]>([1, 188, 1])];
+            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask_1 = tile(reps = var_472, x = var_471)[name = string("pad_mask_for_att_mask_1")];
+            tensor<int32, [3]> var_474_perm_0 = const()[name = string("op_474_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<bool, [1, 188, 188]> var_474 = transpose(perm = var_474_perm_0, x = pad_mask_for_att_mask_1)[name = string("transpose_313")];
+            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_474)[name = string("pad_mask_for_att_mask")];
+            tensor<bool, [1, 188, 188]> const_81 = const()[name = string("const_81"), val = tensor<bool, [1, 188, 188]>([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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true, true, true, true, true, true]]])];
+            tensor<bool, [1, 188, 188]> att_mask = logical_and(x = pad_mask_for_att_mask, y = const_81)[name = string("att_mask")];
+            tensor<bool, [1, 188, 188]> mask_13 = logical_not(x = att_mask)[name = string("mask_13")];
+            tensor<bool, [1, 188]> pad_mask = logical_not(x = pad_mask_1)[name = string("pad_mask")];
+            tensor<int32, [1]> input_29_axes_0 = const()[name = string("input_29_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2560320)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2562432)))];
+            fp16 var_166_to_fp16 = const()[name = string("op_166_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 188, 1024]> input_29_cast_fp16 = layer_norm(axes = input_29_axes_0, beta = encoder_module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_feed_forward1_weight_to_fp16, x = x_23_cast_fp16)[name = string("input_29_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564544))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4661760))))[name = string("encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4661888)))];
+            tensor<fp16, [1, 188, 4096]> linear_1_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16_palettized, x = input_29_cast_fp16)[name = string("linear_1_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_33_cast_fp16 = silu(x = linear_1_cast_fp16)[name = string("input_33_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4670144))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6767360))))[name = string("encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6767488)))];
+            tensor<fp16, [1, 188, 1024]> linear_2_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16_palettized, x = input_33_cast_fp16)[name = string("linear_2_cast_fp16")];
+            fp16 var_507_to_fp16 = const()[name = string("op_507_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_508_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_507_to_fp16)[name = string("op_508_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_39_cast_fp16 = add(x = x_23_cast_fp16, y = var_508_cast_fp16)[name = string("input_39_cast_fp16")];
+            tensor<int32, [1]> query_1_axes_0 = const()[name = string("query_1_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6769600)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6771712)))];
+            tensor<fp16, [1, 188, 1024]> query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = encoder_module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_self_att_weight_to_fp16, x = input_39_cast_fp16)[name = string("query_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6773824))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7298176))))[name = string("encoder_module_layers_0_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7298304)))];
+            tensor<fp16, [1, 188, 1024]> linear_3_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_q_weight_to_fp16_palettized, x = query_1_cast_fp16)[name = string("linear_3_cast_fp16")];
+            tensor<int32, [4]> var_525 = const()[name = string("op_525"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_1_cast_fp16 = reshape(shape = var_525, x = linear_3_cast_fp16)[name = string("q_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7300416))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7824768))))[name = string("encoder_module_layers_0_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7824896)))];
+            tensor<fp16, [1, 188, 1024]> linear_4_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_k_weight_to_fp16_palettized, x = query_1_cast_fp16)[name = string("linear_4_cast_fp16")];
+            tensor<int32, [4]> var_530 = const()[name = string("op_530"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_1_cast_fp16 = reshape(shape = var_530, x = linear_4_cast_fp16)[name = string("k_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7827008))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8351360))))[name = string("encoder_module_layers_0_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8351488)))];
+            tensor<fp16, [1, 188, 1024]> linear_5_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_v_weight_to_fp16_palettized, x = query_1_cast_fp16)[name = string("linear_5_cast_fp16")];
+            tensor<int32, [4]> var_535 = const()[name = string("op_535"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_1_cast_fp16 = reshape(shape = var_535, x = linear_5_cast_fp16)[name = string("v_1_cast_fp16")];
+            tensor<int32, [4]> value_5_perm_0 = const()[name = string("value_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8353600)))];
+            tensor<fp16, [1, 188, 8, 128]> var_547_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_module_layers_0_self_attn_pos_bias_u_to_fp16)[name = string("op_547_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8355712)))];
+            tensor<fp16, [1, 188, 8, 128]> var_549_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_module_layers_0_self_attn_pos_bias_v_to_fp16)[name = string("op_549_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_1_perm_0 = const()[name = string("q_with_bias_v_1_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_27_transpose_x_0 = const()[name = string("x_27_transpose_x_0"), val = bool(false)];
+            bool x_27_transpose_y_0 = const()[name = string("x_27_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_551_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8357824))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8549888))))[name = string("op_551_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_549_cast_fp16)[name = string("transpose_312")];
+            tensor<fp16, [1, 8, 188, 375]> x_27_cast_fp16 = matmul(transpose_x = x_27_transpose_x_0, transpose_y = x_27_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = op_551_to_fp16_palettized)[name = string("x_27_cast_fp16")];
+            tensor<int32, [8]> x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_29_mode_0 = const()[name = string("x_29_mode_0"), val = string("constant")];
+            fp16 const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_29_cast_fp16 = pad(constant_val = const_88_to_fp16, mode = x_29_mode_0, pad = x_29_pad_0, x = x_27_cast_fp16)[name = string("x_29_cast_fp16")];
+            tensor<int32, [4]> var_559 = const()[name = string("op_559"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_31_cast_fp16 = reshape(shape = var_559, x = x_29_cast_fp16)[name = string("x_31_cast_fp16")];
+            tensor<int32, [4]> var_563_begin_0 = const()[name = string("op_563_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_563_end_0 = const()[name = string("op_563_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_563_end_mask_0 = const()[name = string("op_563_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_563_cast_fp16 = slice_by_index(begin = var_563_begin_0, end = var_563_end_0, end_mask = var_563_end_mask_0, x = x_31_cast_fp16)[name = string("op_563_cast_fp16")];
+            tensor<int32, [4]> var_564 = const()[name = string("op_564"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_1_cast_fp16 = reshape(shape = var_564, x = var_563_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<int32, [4]> transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = string("transpose_310")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_96 = transpose(perm = transpose_96_perm_0, x = var_547_cast_fp16)[name = string("transpose_311")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_3_end_0 = const()[name = string("matrix_bd_3_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_573_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = string("op_573_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<fp16, [1, 8, 188, 188]> _inversed_scores_1_cast_fp16 = mul(x = var_573_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")];
+            tensor<int32, [1]> mask_15_axes_0 = const()[name = string("mask_15_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188, 188]> mask_15 = expand_dims(axes = mask_15_axes_0, x = mask_13)[name = string("mask_15")];
+            fp16 var_163_to_fp16 = const()[name = string("op_163_to_fp16"), val = fp16(-0x1.388p+13)];
+            tensor<fp16, [1, 8, 188, 188]> scores_3_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_15)[name = string("scores_3_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_579_cast_fp16 = softmax(axis = var_152, x = scores_3_cast_fp16)[name = string("op_579_cast_fp16")];
+            fp16 var_164_to_fp16 = const()[name = string("op_164_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 188]> input_41_cast_fp16 = select(a = var_164_to_fp16, b = var_579_cast_fp16, cond = mask_15)[name = string("input_41_cast_fp16")];
+            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<fp16, [1, 8, 188, 128]> value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_1_cast_fp16)[name = string("transpose_309")];
+            tensor<fp16, [1, 8, 188, 128]> x_33_cast_fp16 = matmul(transpose_x = x_33_transpose_x_0, transpose_y = x_33_transpose_y_0, x = input_41_cast_fp16, y = value_5_cast_fp16)[name = string("x_33_cast_fp16")];
+            tensor<int32, [4]> var_583_perm_0 = const()[name = string("op_583_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_584 = const()[name = string("op_584"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_583_cast_fp16 = transpose(perm = var_583_perm_0, x = x_33_cast_fp16)[name = string("transpose_308")];
+            tensor<fp16, [1, 188, 1024]> input_43_cast_fp16 = reshape(shape = var_584, x = var_583_cast_fp16)[name = string("input_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8550016))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9074368))))[name = string("encoder_module_layers_0_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9074496)))];
+            tensor<fp16, [1, 188, 1024]> linear_7_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_out_weight_to_fp16_palettized, x = input_43_cast_fp16)[name = string("linear_7_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_47_cast_fp16 = add(x = input_39_cast_fp16, y = linear_7_cast_fp16)[name = string("input_47_cast_fp16")];
+            tensor<int32, [1]> x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9076608)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9078720)))];
+            tensor<fp16, [1, 188, 1024]> x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, beta = encoder_module_layers_0_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_conv_weight_to_fp16, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")];
+            tensor<int32, [3]> input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_0_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9080832))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10129472))))[name = string("encoder_module_layers_0_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10129600)))];
+            tensor<fp16, [1, 1024, 188]> input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_37_cast_fp16)[name = string("transpose_307")];
+            tensor<fp16, [1, 2048, 188]> input_51_cast_fp16 = conv(bias = encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_0_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")];
+            int32 x_39_split_num_splits_0 = const()[name = string("x_39_split_num_splits_0"), val = int32(2)];
+            int32 x_39_split_axis_0 = const()[name = string("x_39_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_39_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_39_split_cast_fp16_1 = split(axis = x_39_split_axis_0, num_splits = x_39_split_num_splits_0, x = input_51_cast_fp16)[name = string("x_39_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_39_split_1_sigmoid_cast_fp16 = sigmoid(x = x_39_split_cast_fp16_1)[name = string("x_39_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_39_cast_fp16 = mul(x = x_39_split_cast_fp16_0, y = x_39_split_1_sigmoid_cast_fp16)[name = string("x_39_cast_fp16")];
+            tensor<int32, [1]> var_608_axes_0 = const()[name = string("op_608_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188]> var_608 = expand_dims(axes = var_608_axes_0, x = pad_mask)[name = string("op_608")];
+            tensor<fp16, [1, 1024, 188]> input_53_cast_fp16 = select(a = var_164_to_fp16, b = x_39_cast_fp16, cond = var_608)[name = string("input_53_cast_fp16")];
+            tensor<int32, [6]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("constant")];
+            fp16 const_91_to_fp16 = const()[name = string("const_91_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_55_cast_fp16 = pad(constant_val = const_91_to_fp16, mode = input_55_mode_0, pad = input_55_pad_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")];
+            string input_57_pad_type_0 = const()[name = string("input_57_pad_type_0"), val = string("valid")];
+            int32 input_57_groups_0 = const()[name = string("input_57_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_57_strides_0 = const()[name = string("input_57_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_57_dilations_0 = const()[name = string("input_57_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_322_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10133760))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10138432))))[name = string("const_322_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_323_to_fp16 = const()[name = string("const_323_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10138560)))];
+            tensor<fp16, [1, 1024, 188]> input_59_cast_fp16 = conv(bias = const_323_to_fp16, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_322_to_fp16_palettized, x = input_55_cast_fp16)[name = string("input_59_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_61_cast_fp16 = silu(x = input_59_cast_fp16)[name = string("input_61_cast_fp16")];
+            string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10140672))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10665024))))[name = string("encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10665152)))];
+            tensor<fp16, [1, 1024, 188]> x_41_cast_fp16 = conv(bias = encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = string("x_41_cast_fp16")];
+            tensor<int32, [3]> input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_41_cast_fp16)[name = string("transpose_306")];
+            tensor<fp16, [1, 188, 1024]> input_65_cast_fp16 = add(x = input_47_cast_fp16, y = input_63_cast_fp16)[name = string("input_65_cast_fp16")];
+            tensor<int32, [1]> input_67_axes_0 = const()[name = string("input_67_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10667264)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10669376)))];
+            tensor<fp16, [1, 188, 1024]> input_67_cast_fp16 = layer_norm(axes = input_67_axes_0, beta = encoder_module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10671488))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12768704))))[name = string("encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12768832)))];
+            tensor<fp16, [1, 188, 4096]> linear_8_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16_palettized, x = input_67_cast_fp16)[name = string("linear_8_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_71_cast_fp16 = silu(x = linear_8_cast_fp16)[name = string("input_71_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12777088))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14874304))))[name = string("encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14874432)))];
+            tensor<fp16, [1, 188, 1024]> linear_9_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_9_cast_fp16")];
+            fp16 var_650_to_fp16 = const()[name = string("op_650_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_651_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_650_to_fp16)[name = string("op_651_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_77_cast_fp16 = add(x = input_65_cast_fp16, y = var_651_cast_fp16)[name = string("input_77_cast_fp16")];
+            tensor<int32, [1]> input_79_axes_0 = const()[name = string("input_79_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14876544)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14878656)))];
+            tensor<fp16, [1, 188, 1024]> input_79_cast_fp16 = layer_norm(axes = input_79_axes_0, beta = encoder_module_layers_0_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_out_weight_to_fp16, x = input_77_cast_fp16)[name = string("input_79_cast_fp16")];
+            tensor<int32, [1]> input_81_axes_0 = const()[name = string("input_81_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14880768)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14882880)))];
+            tensor<fp16, [1, 188, 1024]> input_81_cast_fp16 = layer_norm(axes = input_81_axes_0, beta = encoder_module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_79_cast_fp16)[name = string("input_81_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14884992))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16982208))))[name = string("encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16982336)))];
+            tensor<fp16, [1, 188, 4096]> linear_10_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16_palettized, x = input_81_cast_fp16)[name = string("linear_10_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_85_cast_fp16 = silu(x = linear_10_cast_fp16)[name = string("input_85_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16990592))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19087808))))[name = string("encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19087936)))];
+            tensor<fp16, [1, 188, 1024]> linear_11_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = string("linear_11_cast_fp16")];
+            fp16 var_681_to_fp16 = const()[name = string("op_681_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_682_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_681_to_fp16)[name = string("op_682_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_91_cast_fp16 = add(x = input_79_cast_fp16, y = var_682_cast_fp16)[name = string("input_91_cast_fp16")];
+            tensor<int32, [1]> query_3_axes_0 = const()[name = string("query_3_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19090048)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19092160)))];
+            tensor<fp16, [1, 188, 1024]> query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = encoder_module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_self_att_weight_to_fp16, x = input_91_cast_fp16)[name = string("query_3_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19094272))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19618624))))[name = string("encoder_module_layers_1_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19618752)))];
+            tensor<fp16, [1, 188, 1024]> linear_12_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_q_weight_to_fp16_palettized, x = query_3_cast_fp16)[name = string("linear_12_cast_fp16")];
+            tensor<int32, [4]> var_699 = const()[name = string("op_699"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_7_cast_fp16 = reshape(shape = var_699, x = linear_12_cast_fp16)[name = string("q_7_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19620864))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20145216))))[name = string("encoder_module_layers_1_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20145344)))];
+            tensor<fp16, [1, 188, 1024]> linear_13_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_k_weight_to_fp16_palettized, x = query_3_cast_fp16)[name = string("linear_13_cast_fp16")];
+            tensor<int32, [4]> var_704 = const()[name = string("op_704"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_5_cast_fp16 = reshape(shape = var_704, x = linear_13_cast_fp16)[name = string("k_5_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20147456))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20671808))))[name = string("encoder_module_layers_1_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20671936)))];
+            tensor<fp16, [1, 188, 1024]> linear_14_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_v_weight_to_fp16_palettized, x = query_3_cast_fp16)[name = string("linear_14_cast_fp16")];
+            tensor<int32, [4]> var_709 = const()[name = string("op_709"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_3_cast_fp16 = reshape(shape = var_709, x = linear_14_cast_fp16)[name = string("v_3_cast_fp16")];
+            tensor<int32, [4]> value_7_perm_0 = const()[name = string("value_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20674048)))];
+            tensor<fp16, [1, 188, 8, 128]> var_721_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_module_layers_1_self_attn_pos_bias_u_to_fp16)[name = string("op_721_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20676160)))];
+            tensor<fp16, [1, 188, 8, 128]> var_723_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_module_layers_1_self_attn_pos_bias_v_to_fp16)[name = string("op_723_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_3_perm_0 = const()[name = string("q_with_bias_v_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_49_transpose_x_0 = const()[name = string("x_49_transpose_x_0"), val = bool(false)];
+            bool x_49_transpose_y_0 = const()[name = string("x_49_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_725_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20678272))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20870336))))[name = string("op_725_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_723_cast_fp16)[name = string("transpose_305")];
+            tensor<fp16, [1, 8, 188, 375]> x_49_cast_fp16 = matmul(transpose_x = x_49_transpose_x_0, transpose_y = x_49_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = op_725_to_fp16_palettized)[name = string("x_49_cast_fp16")];
+            tensor<int32, [8]> x_51_pad_0 = const()[name = string("x_51_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_51_mode_0 = const()[name = string("x_51_mode_0"), val = string("constant")];
+            fp16 const_98_to_fp16 = const()[name = string("const_98_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_51_cast_fp16 = pad(constant_val = const_98_to_fp16, mode = x_51_mode_0, pad = x_51_pad_0, x = x_49_cast_fp16)[name = string("x_51_cast_fp16")];
+            tensor<int32, [4]> var_733 = const()[name = string("op_733"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_53_cast_fp16 = reshape(shape = var_733, x = x_51_cast_fp16)[name = string("x_53_cast_fp16")];
+            tensor<int32, [4]> var_737_begin_0 = const()[name = string("op_737_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_737_end_0 = const()[name = string("op_737_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_737_end_mask_0 = const()[name = string("op_737_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_737_cast_fp16 = slice_by_index(begin = var_737_begin_0, end = var_737_end_0, end_mask = var_737_end_mask_0, x = x_53_cast_fp16)[name = string("op_737_cast_fp16")];
+            tensor<int32, [4]> var_738 = const()[name = string("op_738"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_5_cast_fp16 = reshape(shape = var_738, x = var_737_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<int32, [4]> transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = string("transpose_303")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_98 = transpose(perm = transpose_98_perm_0, x = var_721_cast_fp16)[name = string("transpose_304")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_7_end_0 = const()[name = string("matrix_bd_7_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_747_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = string("op_747_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<fp16, [1, 8, 188, 188]> _inversed_scores_5_cast_fp16 = mul(x = var_747_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_7_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_15)[name = string("scores_7_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_753_cast_fp16 = softmax(axis = var_152, x = scores_7_cast_fp16)[name = string("op_753_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_93_cast_fp16 = select(a = var_164_to_fp16, b = var_753_cast_fp16, cond = mask_15)[name = string("input_93_cast_fp16")];
+            bool x_55_transpose_x_0 = const()[name = string("x_55_transpose_x_0"), val = bool(false)];
+            bool x_55_transpose_y_0 = const()[name = string("x_55_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_3_cast_fp16)[name = string("transpose_302")];
+            tensor<fp16, [1, 8, 188, 128]> x_55_cast_fp16 = matmul(transpose_x = x_55_transpose_x_0, transpose_y = x_55_transpose_y_0, x = input_93_cast_fp16, y = value_7_cast_fp16)[name = string("x_55_cast_fp16")];
+            tensor<int32, [4]> var_757_perm_0 = const()[name = string("op_757_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_758 = const()[name = string("op_758"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_757_cast_fp16 = transpose(perm = var_757_perm_0, x = x_55_cast_fp16)[name = string("transpose_301")];
+            tensor<fp16, [1, 188, 1024]> input_95_cast_fp16 = reshape(shape = var_758, x = var_757_cast_fp16)[name = string("input_95_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20870464))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21394816))))[name = string("encoder_module_layers_1_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21394944)))];
+            tensor<fp16, [1, 188, 1024]> linear_16_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_out_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_16_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_99_cast_fp16 = add(x = input_91_cast_fp16, y = linear_16_cast_fp16)[name = string("input_99_cast_fp16")];
+            tensor<int32, [1]> x_59_axes_0 = const()[name = string("x_59_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21397056)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21399168)))];
+            tensor<fp16, [1, 188, 1024]> x_59_cast_fp16 = layer_norm(axes = x_59_axes_0, beta = encoder_module_layers_1_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_conv_weight_to_fp16, x = input_99_cast_fp16)[name = string("x_59_cast_fp16")];
+            tensor<int32, [3]> input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_1_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21401280))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22449920))))[name = string("encoder_module_layers_1_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22450048)))];
+            tensor<fp16, [1, 1024, 188]> input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_59_cast_fp16)[name = string("transpose_300")];
+            tensor<fp16, [1, 2048, 188]> input_103_cast_fp16 = conv(bias = encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_1_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")];
+            int32 x_61_split_num_splits_0 = const()[name = string("x_61_split_num_splits_0"), val = int32(2)];
+            int32 x_61_split_axis_0 = const()[name = string("x_61_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_61_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_61_split_cast_fp16_1 = split(axis = x_61_split_axis_0, num_splits = x_61_split_num_splits_0, x = input_103_cast_fp16)[name = string("x_61_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_61_split_1_sigmoid_cast_fp16 = sigmoid(x = x_61_split_cast_fp16_1)[name = string("x_61_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_61_cast_fp16 = mul(x = x_61_split_cast_fp16_0, y = x_61_split_1_sigmoid_cast_fp16)[name = string("x_61_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_105_cast_fp16 = select(a = var_164_to_fp16, b = x_61_cast_fp16, cond = var_608)[name = string("input_105_cast_fp16")];
+            tensor<int32, [6]> input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("constant")];
+            fp16 const_101_to_fp16 = const()[name = string("const_101_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_107_cast_fp16 = pad(constant_val = const_101_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")];
+            string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("valid")];
+            int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_324_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22454208))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22458880))))[name = string("const_324_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_325_to_fp16 = const()[name = string("const_325_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22459008)))];
+            tensor<fp16, [1, 1024, 188]> input_111_cast_fp16 = conv(bias = const_325_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_324_to_fp16_palettized, x = input_107_cast_fp16)[name = string("input_111_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_113_cast_fp16 = silu(x = input_111_cast_fp16)[name = string("input_113_cast_fp16")];
+            string x_63_pad_type_0 = const()[name = string("x_63_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_63_strides_0 = const()[name = string("x_63_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_63_pad_0 = const()[name = string("x_63_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_63_dilations_0 = const()[name = string("x_63_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_63_groups_0 = const()[name = string("x_63_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22461120))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22985472))))[name = string("encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22985600)))];
+            tensor<fp16, [1, 1024, 188]> x_63_cast_fp16 = conv(bias = encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16, dilations = x_63_dilations_0, groups = x_63_groups_0, pad = x_63_pad_0, pad_type = x_63_pad_type_0, strides = x_63_strides_0, weight = encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_113_cast_fp16)[name = string("x_63_cast_fp16")];
+            tensor<int32, [3]> input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_63_cast_fp16)[name = string("transpose_299")];
+            tensor<fp16, [1, 188, 1024]> input_117_cast_fp16 = add(x = input_99_cast_fp16, y = input_115_cast_fp16)[name = string("input_117_cast_fp16")];
+            tensor<int32, [1]> input_119_axes_0 = const()[name = string("input_119_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22987712)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22989824)))];
+            tensor<fp16, [1, 188, 1024]> input_119_cast_fp16 = layer_norm(axes = input_119_axes_0, beta = encoder_module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22991936))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25089152))))[name = string("encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25089280)))];
+            tensor<fp16, [1, 188, 4096]> linear_17_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("linear_17_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_123_cast_fp16 = silu(x = linear_17_cast_fp16)[name = string("input_123_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25097536))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27194752))))[name = string("encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27194880)))];
+            tensor<fp16, [1, 188, 1024]> linear_18_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = string("linear_18_cast_fp16")];
+            fp16 var_824_to_fp16 = const()[name = string("op_824_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_825_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_824_to_fp16)[name = string("op_825_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_129_cast_fp16 = add(x = input_117_cast_fp16, y = var_825_cast_fp16)[name = string("input_129_cast_fp16")];
+            tensor<int32, [1]> input_131_axes_0 = const()[name = string("input_131_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27196992)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27199104)))];
+            tensor<fp16, [1, 188, 1024]> input_131_cast_fp16 = layer_norm(axes = input_131_axes_0, beta = encoder_module_layers_1_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_out_weight_to_fp16, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")];
+            tensor<int32, [1]> input_133_axes_0 = const()[name = string("input_133_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27201216)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27203328)))];
+            tensor<fp16, [1, 188, 1024]> input_133_cast_fp16 = layer_norm(axes = input_133_axes_0, beta = encoder_module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_131_cast_fp16)[name = string("input_133_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27205440))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29302656))))[name = string("encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29302784)))];
+            tensor<fp16, [1, 188, 4096]> linear_19_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16_palettized, x = input_133_cast_fp16)[name = string("linear_19_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_137_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("input_137_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29311040))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31408256))))[name = string("encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31408384)))];
+            tensor<fp16, [1, 188, 1024]> linear_20_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16_palettized, x = input_137_cast_fp16)[name = string("linear_20_cast_fp16")];
+            fp16 var_855_to_fp16 = const()[name = string("op_855_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_856_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_855_to_fp16)[name = string("op_856_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_143_cast_fp16 = add(x = input_131_cast_fp16, y = var_856_cast_fp16)[name = string("input_143_cast_fp16")];
+            tensor<int32, [1]> query_5_axes_0 = const()[name = string("query_5_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31410496)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31412608)))];
+            tensor<fp16, [1, 188, 1024]> query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = encoder_module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_self_att_weight_to_fp16, x = input_143_cast_fp16)[name = string("query_5_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31414720))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31939072))))[name = string("encoder_module_layers_2_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31939200)))];
+            tensor<fp16, [1, 188, 1024]> linear_21_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_q_weight_to_fp16_palettized, x = query_5_cast_fp16)[name = string("linear_21_cast_fp16")];
+            tensor<int32, [4]> var_873 = const()[name = string("op_873"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_13_cast_fp16 = reshape(shape = var_873, x = linear_21_cast_fp16)[name = string("q_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31941312))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32465664))))[name = string("encoder_module_layers_2_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32465792)))];
+            tensor<fp16, [1, 188, 1024]> linear_22_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_k_weight_to_fp16_palettized, x = query_5_cast_fp16)[name = string("linear_22_cast_fp16")];
+            tensor<int32, [4]> var_878 = const()[name = string("op_878"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_9_cast_fp16 = reshape(shape = var_878, x = linear_22_cast_fp16)[name = string("k_9_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32467904))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32992256))))[name = string("encoder_module_layers_2_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32992384)))];
+            tensor<fp16, [1, 188, 1024]> linear_23_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_v_weight_to_fp16_palettized, x = query_5_cast_fp16)[name = string("linear_23_cast_fp16")];
+            tensor<int32, [4]> var_883 = const()[name = string("op_883"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_5_cast_fp16 = reshape(shape = var_883, x = linear_23_cast_fp16)[name = string("v_5_cast_fp16")];
+            tensor<int32, [4]> value_9_perm_0 = const()[name = string("value_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32994496)))];
+            tensor<fp16, [1, 188, 8, 128]> var_895_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_module_layers_2_self_attn_pos_bias_u_to_fp16)[name = string("op_895_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32996608)))];
+            tensor<fp16, [1, 188, 8, 128]> var_897_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_module_layers_2_self_attn_pos_bias_v_to_fp16)[name = string("op_897_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_5_perm_0 = const()[name = string("q_with_bias_v_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_71_transpose_x_0 = const()[name = string("x_71_transpose_x_0"), val = bool(false)];
+            bool x_71_transpose_y_0 = const()[name = string("x_71_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_899_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32998720))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33190784))))[name = string("op_899_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_897_cast_fp16)[name = string("transpose_298")];
+            tensor<fp16, [1, 8, 188, 375]> x_71_cast_fp16 = matmul(transpose_x = x_71_transpose_x_0, transpose_y = x_71_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = op_899_to_fp16_palettized)[name = string("x_71_cast_fp16")];
+            tensor<int32, [8]> x_73_pad_0 = const()[name = string("x_73_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("constant")];
+            fp16 const_108_to_fp16 = const()[name = string("const_108_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_73_cast_fp16 = pad(constant_val = const_108_to_fp16, mode = x_73_mode_0, pad = x_73_pad_0, x = x_71_cast_fp16)[name = string("x_73_cast_fp16")];
+            tensor<int32, [4]> var_907 = const()[name = string("op_907"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_75_cast_fp16 = reshape(shape = var_907, x = x_73_cast_fp16)[name = string("x_75_cast_fp16")];
+            tensor<int32, [4]> var_911_begin_0 = const()[name = string("op_911_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_911_end_0 = const()[name = string("op_911_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_911_end_mask_0 = const()[name = string("op_911_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_911_cast_fp16 = slice_by_index(begin = var_911_begin_0, end = var_911_end_0, end_mask = var_911_end_mask_0, x = x_75_cast_fp16)[name = string("op_911_cast_fp16")];
+            tensor<int32, [4]> var_912 = const()[name = string("op_912"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_9_cast_fp16 = reshape(shape = var_912, x = var_911_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<int32, [4]> transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = string("transpose_296")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_100 = transpose(perm = transpose_100_perm_0, x = var_895_cast_fp16)[name = string("transpose_297")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_11_end_0 = const()[name = string("matrix_bd_11_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_921_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = string("op_921_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<fp16, [1, 8, 188, 188]> _inversed_scores_9_cast_fp16 = mul(x = var_921_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_11_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_15)[name = string("scores_11_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_927_cast_fp16 = softmax(axis = var_152, x = scores_11_cast_fp16)[name = string("op_927_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_145_cast_fp16 = select(a = var_164_to_fp16, b = var_927_cast_fp16, cond = mask_15)[name = string("input_145_cast_fp16")];
+            bool x_77_transpose_x_0 = const()[name = string("x_77_transpose_x_0"), val = bool(false)];
+            bool x_77_transpose_y_0 = const()[name = string("x_77_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_5_cast_fp16)[name = string("transpose_295")];
+            tensor<fp16, [1, 8, 188, 128]> x_77_cast_fp16 = matmul(transpose_x = x_77_transpose_x_0, transpose_y = x_77_transpose_y_0, x = input_145_cast_fp16, y = value_9_cast_fp16)[name = string("x_77_cast_fp16")];
+            tensor<int32, [4]> var_931_perm_0 = const()[name = string("op_931_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_932 = const()[name = string("op_932"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_931_cast_fp16 = transpose(perm = var_931_perm_0, x = x_77_cast_fp16)[name = string("transpose_294")];
+            tensor<fp16, [1, 188, 1024]> input_147_cast_fp16 = reshape(shape = var_932, x = var_931_cast_fp16)[name = string("input_147_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33190912))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33715264))))[name = string("encoder_module_layers_2_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33715392)))];
+            tensor<fp16, [1, 188, 1024]> linear_25_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_out_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("linear_25_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_151_cast_fp16 = add(x = input_143_cast_fp16, y = linear_25_cast_fp16)[name = string("input_151_cast_fp16")];
+            tensor<int32, [1]> x_81_axes_0 = const()[name = string("x_81_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33717504)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33719616)))];
+            tensor<fp16, [1, 188, 1024]> x_81_cast_fp16 = layer_norm(axes = x_81_axes_0, beta = encoder_module_layers_2_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_conv_weight_to_fp16, x = input_151_cast_fp16)[name = string("x_81_cast_fp16")];
+            tensor<int32, [3]> input_153_perm_0 = const()[name = string("input_153_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_2_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33721728))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34770368))))[name = string("encoder_module_layers_2_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34770496)))];
+            tensor<fp16, [1, 1024, 188]> input_153_cast_fp16 = transpose(perm = input_153_perm_0, x = x_81_cast_fp16)[name = string("transpose_293")];
+            tensor<fp16, [1, 2048, 188]> input_155_cast_fp16 = conv(bias = encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_2_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_153_cast_fp16)[name = string("input_155_cast_fp16")];
+            int32 x_83_split_num_splits_0 = const()[name = string("x_83_split_num_splits_0"), val = int32(2)];
+            int32 x_83_split_axis_0 = const()[name = string("x_83_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_83_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_83_split_cast_fp16_1 = split(axis = x_83_split_axis_0, num_splits = x_83_split_num_splits_0, x = input_155_cast_fp16)[name = string("x_83_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_83_split_1_sigmoid_cast_fp16 = sigmoid(x = x_83_split_cast_fp16_1)[name = string("x_83_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_83_cast_fp16 = mul(x = x_83_split_cast_fp16_0, y = x_83_split_1_sigmoid_cast_fp16)[name = string("x_83_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_157_cast_fp16 = select(a = var_164_to_fp16, b = x_83_cast_fp16, cond = var_608)[name = string("input_157_cast_fp16")];
+            tensor<int32, [6]> input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_159_mode_0 = const()[name = string("input_159_mode_0"), val = string("constant")];
+            fp16 const_111_to_fp16 = const()[name = string("const_111_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_159_cast_fp16 = pad(constant_val = const_111_to_fp16, mode = input_159_mode_0, pad = input_159_pad_0, x = input_157_cast_fp16)[name = string("input_159_cast_fp16")];
+            string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("valid")];
+            int32 input_161_groups_0 = const()[name = string("input_161_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_161_strides_0 = const()[name = string("input_161_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_161_dilations_0 = const()[name = string("input_161_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_326_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34774656))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34779328))))[name = string("const_326_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_327_to_fp16 = const()[name = string("const_327_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34779456)))];
+            tensor<fp16, [1, 1024, 188]> input_163_cast_fp16 = conv(bias = const_327_to_fp16, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_326_to_fp16_palettized, x = input_159_cast_fp16)[name = string("input_163_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_165_cast_fp16 = silu(x = input_163_cast_fp16)[name = string("input_165_cast_fp16")];
+            string x_85_pad_type_0 = const()[name = string("x_85_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_85_strides_0 = const()[name = string("x_85_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_85_pad_0 = const()[name = string("x_85_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_85_dilations_0 = const()[name = string("x_85_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_85_groups_0 = const()[name = string("x_85_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34781568))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35305920))))[name = string("encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35306048)))];
+            tensor<fp16, [1, 1024, 188]> x_85_cast_fp16 = conv(bias = encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16, dilations = x_85_dilations_0, groups = x_85_groups_0, pad = x_85_pad_0, pad_type = x_85_pad_type_0, strides = x_85_strides_0, weight = encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = string("x_85_cast_fp16")];
+            tensor<int32, [3]> input_167_perm_0 = const()[name = string("input_167_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_167_cast_fp16 = transpose(perm = input_167_perm_0, x = x_85_cast_fp16)[name = string("transpose_292")];
+            tensor<fp16, [1, 188, 1024]> input_169_cast_fp16 = add(x = input_151_cast_fp16, y = input_167_cast_fp16)[name = string("input_169_cast_fp16")];
+            tensor<int32, [1]> input_171_axes_0 = const()[name = string("input_171_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35308160)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35310272)))];
+            tensor<fp16, [1, 188, 1024]> input_171_cast_fp16 = layer_norm(axes = input_171_axes_0, beta = encoder_module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_169_cast_fp16)[name = string("input_171_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35312384))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37409600))))[name = string("encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37409728)))];
+            tensor<fp16, [1, 188, 4096]> linear_26_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16_palettized, x = input_171_cast_fp16)[name = string("linear_26_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_175_cast_fp16 = silu(x = linear_26_cast_fp16)[name = string("input_175_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37417984))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39515200))))[name = string("encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39515328)))];
+            tensor<fp16, [1, 188, 1024]> linear_27_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16_palettized, x = input_175_cast_fp16)[name = string("linear_27_cast_fp16")];
+            fp16 var_998_to_fp16 = const()[name = string("op_998_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_999_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_998_to_fp16)[name = string("op_999_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_181_cast_fp16 = add(x = input_169_cast_fp16, y = var_999_cast_fp16)[name = string("input_181_cast_fp16")];
+            tensor<int32, [1]> input_183_axes_0 = const()[name = string("input_183_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39517440)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39519552)))];
+            tensor<fp16, [1, 188, 1024]> input_183_cast_fp16 = layer_norm(axes = input_183_axes_0, beta = encoder_module_layers_2_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_out_weight_to_fp16, x = input_181_cast_fp16)[name = string("input_183_cast_fp16")];
+            tensor<int32, [1]> input_185_axes_0 = const()[name = string("input_185_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39521664)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39523776)))];
+            tensor<fp16, [1, 188, 1024]> input_185_cast_fp16 = layer_norm(axes = input_185_axes_0, beta = encoder_module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_183_cast_fp16)[name = string("input_185_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39525888))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41623104))))[name = string("encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41623232)))];
+            tensor<fp16, [1, 188, 4096]> linear_28_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = string("linear_28_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_189_cast_fp16 = silu(x = linear_28_cast_fp16)[name = string("input_189_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41631488))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43728704))))[name = string("encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43728832)))];
+            tensor<fp16, [1, 188, 1024]> linear_29_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16_palettized, x = input_189_cast_fp16)[name = string("linear_29_cast_fp16")];
+            fp16 var_1029_to_fp16 = const()[name = string("op_1029_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1030_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_1029_to_fp16)[name = string("op_1030_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_195_cast_fp16 = add(x = input_183_cast_fp16, y = var_1030_cast_fp16)[name = string("input_195_cast_fp16")];
+            tensor<int32, [1]> query_7_axes_0 = const()[name = string("query_7_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43730944)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43733056)))];
+            tensor<fp16, [1, 188, 1024]> query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = encoder_module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_self_att_weight_to_fp16, x = input_195_cast_fp16)[name = string("query_7_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43735168))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44259520))))[name = string("encoder_module_layers_3_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44259648)))];
+            tensor<fp16, [1, 188, 1024]> linear_30_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_q_weight_to_fp16_palettized, x = query_7_cast_fp16)[name = string("linear_30_cast_fp16")];
+            tensor<int32, [4]> var_1047 = const()[name = string("op_1047"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_19_cast_fp16 = reshape(shape = var_1047, x = linear_30_cast_fp16)[name = string("q_19_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44261760))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44786112))))[name = string("encoder_module_layers_3_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44786240)))];
+            tensor<fp16, [1, 188, 1024]> linear_31_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_k_weight_to_fp16_palettized, x = query_7_cast_fp16)[name = string("linear_31_cast_fp16")];
+            tensor<int32, [4]> var_1052 = const()[name = string("op_1052"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_13_cast_fp16 = reshape(shape = var_1052, x = linear_31_cast_fp16)[name = string("k_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44788352))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45312704))))[name = string("encoder_module_layers_3_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45312832)))];
+            tensor<fp16, [1, 188, 1024]> linear_32_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_v_weight_to_fp16_palettized, x = query_7_cast_fp16)[name = string("linear_32_cast_fp16")];
+            tensor<int32, [4]> var_1057 = const()[name = string("op_1057"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_7_cast_fp16 = reshape(shape = var_1057, x = linear_32_cast_fp16)[name = string("v_7_cast_fp16")];
+            tensor<int32, [4]> value_11_perm_0 = const()[name = string("value_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45314944)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1069_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_module_layers_3_self_attn_pos_bias_u_to_fp16)[name = string("op_1069_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45317056)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1071_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_module_layers_3_self_attn_pos_bias_v_to_fp16)[name = string("op_1071_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_7_perm_0 = const()[name = string("q_with_bias_v_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_93_transpose_x_0 = const()[name = string("x_93_transpose_x_0"), val = bool(false)];
+            bool x_93_transpose_y_0 = const()[name = string("x_93_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1073_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45319168))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45511232))))[name = string("op_1073_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_1071_cast_fp16)[name = string("transpose_291")];
+            tensor<fp16, [1, 8, 188, 375]> x_93_cast_fp16 = matmul(transpose_x = x_93_transpose_x_0, transpose_y = x_93_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = op_1073_to_fp16_palettized)[name = string("x_93_cast_fp16")];
+            tensor<int32, [8]> x_95_pad_0 = const()[name = string("x_95_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("constant")];
+            fp16 const_118_to_fp16 = const()[name = string("const_118_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_95_cast_fp16 = pad(constant_val = const_118_to_fp16, mode = x_95_mode_0, pad = x_95_pad_0, x = x_93_cast_fp16)[name = string("x_95_cast_fp16")];
+            tensor<int32, [4]> var_1081 = const()[name = string("op_1081"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_97_cast_fp16 = reshape(shape = var_1081, x = x_95_cast_fp16)[name = string("x_97_cast_fp16")];
+            tensor<int32, [4]> var_1085_begin_0 = const()[name = string("op_1085_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1085_end_0 = const()[name = string("op_1085_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1085_end_mask_0 = const()[name = string("op_1085_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1085_cast_fp16 = slice_by_index(begin = var_1085_begin_0, end = var_1085_end_0, end_mask = var_1085_end_mask_0, x = x_97_cast_fp16)[name = string("op_1085_cast_fp16")];
+            tensor<int32, [4]> var_1086 = const()[name = string("op_1086"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_13_cast_fp16 = reshape(shape = var_1086, x = var_1085_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<int32, [4]> transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = string("transpose_289")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1069_cast_fp16)[name = string("transpose_290")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_15_end_0 = const()[name = string("matrix_bd_15_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1095_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = string("op_1095_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<fp16, [1, 8, 188, 188]> _inversed_scores_13_cast_fp16 = mul(x = var_1095_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = string("_inversed_scores_13_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_15_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_15)[name = string("scores_15_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1101_cast_fp16 = softmax(axis = var_152, x = scores_15_cast_fp16)[name = string("op_1101_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_197_cast_fp16 = select(a = var_164_to_fp16, b = var_1101_cast_fp16, cond = mask_15)[name = string("input_197_cast_fp16")];
+            bool x_99_transpose_x_0 = const()[name = string("x_99_transpose_x_0"), val = bool(false)];
+            bool x_99_transpose_y_0 = const()[name = string("x_99_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_7_cast_fp16)[name = string("transpose_288")];
+            tensor<fp16, [1, 8, 188, 128]> x_99_cast_fp16 = matmul(transpose_x = x_99_transpose_x_0, transpose_y = x_99_transpose_y_0, x = input_197_cast_fp16, y = value_11_cast_fp16)[name = string("x_99_cast_fp16")];
+            tensor<int32, [4]> var_1105_perm_0 = const()[name = string("op_1105_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1106 = const()[name = string("op_1106"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1105_cast_fp16 = transpose(perm = var_1105_perm_0, x = x_99_cast_fp16)[name = string("transpose_287")];
+            tensor<fp16, [1, 188, 1024]> input_199_cast_fp16 = reshape(shape = var_1106, x = var_1105_cast_fp16)[name = string("input_199_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45511360))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46035712))))[name = string("encoder_module_layers_3_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46035840)))];
+            tensor<fp16, [1, 188, 1024]> linear_34_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_out_weight_to_fp16_palettized, x = input_199_cast_fp16)[name = string("linear_34_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_203_cast_fp16 = add(x = input_195_cast_fp16, y = linear_34_cast_fp16)[name = string("input_203_cast_fp16")];
+            tensor<int32, [1]> x_103_axes_0 = const()[name = string("x_103_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46037952)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46040064)))];
+            tensor<fp16, [1, 188, 1024]> x_103_cast_fp16 = layer_norm(axes = x_103_axes_0, beta = encoder_module_layers_3_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_conv_weight_to_fp16, x = input_203_cast_fp16)[name = string("x_103_cast_fp16")];
+            tensor<int32, [3]> input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_207_pad_type_0 = const()[name = string("input_207_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_207_strides_0 = const()[name = string("input_207_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_207_pad_0 = const()[name = string("input_207_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_207_dilations_0 = const()[name = string("input_207_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_207_groups_0 = const()[name = string("input_207_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_3_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46042176))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47090816))))[name = string("encoder_module_layers_3_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47090944)))];
+            tensor<fp16, [1, 1024, 188]> input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = x_103_cast_fp16)[name = string("transpose_286")];
+            tensor<fp16, [1, 2048, 188]> input_207_cast_fp16 = conv(bias = encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_3_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_205_cast_fp16)[name = string("input_207_cast_fp16")];
+            int32 x_105_split_num_splits_0 = const()[name = string("x_105_split_num_splits_0"), val = int32(2)];
+            int32 x_105_split_axis_0 = const()[name = string("x_105_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_105_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_105_split_cast_fp16_1 = split(axis = x_105_split_axis_0, num_splits = x_105_split_num_splits_0, x = input_207_cast_fp16)[name = string("x_105_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_105_split_1_sigmoid_cast_fp16 = sigmoid(x = x_105_split_cast_fp16_1)[name = string("x_105_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_105_cast_fp16 = mul(x = x_105_split_cast_fp16_0, y = x_105_split_1_sigmoid_cast_fp16)[name = string("x_105_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_209_cast_fp16 = select(a = var_164_to_fp16, b = x_105_cast_fp16, cond = var_608)[name = string("input_209_cast_fp16")];
+            tensor<int32, [6]> input_211_pad_0 = const()[name = string("input_211_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_211_mode_0 = const()[name = string("input_211_mode_0"), val = string("constant")];
+            fp16 const_121_to_fp16 = const()[name = string("const_121_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_211_cast_fp16 = pad(constant_val = const_121_to_fp16, mode = input_211_mode_0, pad = input_211_pad_0, x = input_209_cast_fp16)[name = string("input_211_cast_fp16")];
+            string input_213_pad_type_0 = const()[name = string("input_213_pad_type_0"), val = string("valid")];
+            int32 input_213_groups_0 = const()[name = string("input_213_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_213_strides_0 = const()[name = string("input_213_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_213_dilations_0 = const()[name = string("input_213_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_328_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47095104))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47099776))))[name = string("const_328_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_329_to_fp16 = const()[name = string("const_329_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47099904)))];
+            tensor<fp16, [1, 1024, 188]> input_215_cast_fp16 = conv(bias = const_329_to_fp16, dilations = input_213_dilations_0, groups = input_213_groups_0, pad = input_213_pad_0, pad_type = input_213_pad_type_0, strides = input_213_strides_0, weight = const_328_to_fp16_palettized, x = input_211_cast_fp16)[name = string("input_215_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_217_cast_fp16 = silu(x = input_215_cast_fp16)[name = string("input_217_cast_fp16")];
+            string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47102016))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47626368))))[name = string("encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47626496)))];
+            tensor<fp16, [1, 1024, 188]> x_107_cast_fp16 = conv(bias = encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_217_cast_fp16)[name = string("x_107_cast_fp16")];
+            tensor<int32, [3]> input_219_perm_0 = const()[name = string("input_219_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_219_cast_fp16 = transpose(perm = input_219_perm_0, x = x_107_cast_fp16)[name = string("transpose_285")];
+            tensor<fp16, [1, 188, 1024]> input_221_cast_fp16 = add(x = input_203_cast_fp16, y = input_219_cast_fp16)[name = string("input_221_cast_fp16")];
+            tensor<int32, [1]> input_223_axes_0 = const()[name = string("input_223_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47628608)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47630720)))];
+            tensor<fp16, [1, 188, 1024]> input_223_cast_fp16 = layer_norm(axes = input_223_axes_0, beta = encoder_module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_221_cast_fp16)[name = string("input_223_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47632832))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49730048))))[name = string("encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49730176)))];
+            tensor<fp16, [1, 188, 4096]> linear_35_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16_palettized, x = input_223_cast_fp16)[name = string("linear_35_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_227_cast_fp16 = silu(x = linear_35_cast_fp16)[name = string("input_227_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49738432))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51835648))))[name = string("encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51835776)))];
+            tensor<fp16, [1, 188, 1024]> linear_36_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16_palettized, x = input_227_cast_fp16)[name = string("linear_36_cast_fp16")];
+            fp16 var_1172_to_fp16 = const()[name = string("op_1172_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1173_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_1172_to_fp16)[name = string("op_1173_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_233_cast_fp16 = add(x = input_221_cast_fp16, y = var_1173_cast_fp16)[name = string("input_233_cast_fp16")];
+            tensor<int32, [1]> input_235_axes_0 = const()[name = string("input_235_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51837888)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51840000)))];
+            tensor<fp16, [1, 188, 1024]> input_235_cast_fp16 = layer_norm(axes = input_235_axes_0, beta = encoder_module_layers_3_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_out_weight_to_fp16, x = input_233_cast_fp16)[name = string("input_235_cast_fp16")];
+            tensor<int32, [1]> input_237_axes_0 = const()[name = string("input_237_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51842112)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51844224)))];
+            tensor<fp16, [1, 188, 1024]> input_237_cast_fp16 = layer_norm(axes = input_237_axes_0, beta = encoder_module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51846336))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53943552))))[name = string("encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53943680)))];
+            tensor<fp16, [1, 188, 4096]> linear_37_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = string("linear_37_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_241_cast_fp16 = silu(x = linear_37_cast_fp16)[name = string("input_241_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53951936))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56049152))))[name = string("encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56049280)))];
+            tensor<fp16, [1, 188, 1024]> linear_38_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16_palettized, x = input_241_cast_fp16)[name = string("linear_38_cast_fp16")];
+            fp16 var_1203_to_fp16 = const()[name = string("op_1203_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1204_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_1203_to_fp16)[name = string("op_1204_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_247_cast_fp16 = add(x = input_235_cast_fp16, y = var_1204_cast_fp16)[name = string("input_247_cast_fp16")];
+            tensor<int32, [1]> query_9_axes_0 = const()[name = string("query_9_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56051392)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56053504)))];
+            tensor<fp16, [1, 188, 1024]> query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = encoder_module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_self_att_weight_to_fp16, x = input_247_cast_fp16)[name = string("query_9_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56055616))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56579968))))[name = string("encoder_module_layers_4_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56580096)))];
+            tensor<fp16, [1, 188, 1024]> linear_39_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_q_weight_to_fp16_palettized, x = query_9_cast_fp16)[name = string("linear_39_cast_fp16")];
+            tensor<int32, [4]> var_1221 = const()[name = string("op_1221"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_25_cast_fp16 = reshape(shape = var_1221, x = linear_39_cast_fp16)[name = string("q_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56582208))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57106560))))[name = string("encoder_module_layers_4_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57106688)))];
+            tensor<fp16, [1, 188, 1024]> linear_40_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_k_weight_to_fp16_palettized, x = query_9_cast_fp16)[name = string("linear_40_cast_fp16")];
+            tensor<int32, [4]> var_1226 = const()[name = string("op_1226"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_17_cast_fp16 = reshape(shape = var_1226, x = linear_40_cast_fp16)[name = string("k_17_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57108800))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57633152))))[name = string("encoder_module_layers_4_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57633280)))];
+            tensor<fp16, [1, 188, 1024]> linear_41_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_v_weight_to_fp16_palettized, x = query_9_cast_fp16)[name = string("linear_41_cast_fp16")];
+            tensor<int32, [4]> var_1231 = const()[name = string("op_1231"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_9_cast_fp16 = reshape(shape = var_1231, x = linear_41_cast_fp16)[name = string("v_9_cast_fp16")];
+            tensor<int32, [4]> value_13_perm_0 = const()[name = string("value_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57635392)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1243_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_module_layers_4_self_attn_pos_bias_u_to_fp16)[name = string("op_1243_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57637504)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1245_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_module_layers_4_self_attn_pos_bias_v_to_fp16)[name = string("op_1245_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_9_perm_0 = const()[name = string("q_with_bias_v_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_115_transpose_x_0 = const()[name = string("x_115_transpose_x_0"), val = bool(false)];
+            bool x_115_transpose_y_0 = const()[name = string("x_115_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1247_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57639616))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57831680))))[name = string("op_1247_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_1245_cast_fp16)[name = string("transpose_284")];
+            tensor<fp16, [1, 8, 188, 375]> x_115_cast_fp16 = matmul(transpose_x = x_115_transpose_x_0, transpose_y = x_115_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = op_1247_to_fp16_palettized)[name = string("x_115_cast_fp16")];
+            tensor<int32, [8]> x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_117_mode_0 = const()[name = string("x_117_mode_0"), val = string("constant")];
+            fp16 const_128_to_fp16 = const()[name = string("const_128_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_117_cast_fp16 = pad(constant_val = const_128_to_fp16, mode = x_117_mode_0, pad = x_117_pad_0, x = x_115_cast_fp16)[name = string("x_117_cast_fp16")];
+            tensor<int32, [4]> var_1255 = const()[name = string("op_1255"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_119_cast_fp16 = reshape(shape = var_1255, x = x_117_cast_fp16)[name = string("x_119_cast_fp16")];
+            tensor<int32, [4]> var_1259_begin_0 = const()[name = string("op_1259_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1259_end_0 = const()[name = string("op_1259_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1259_end_mask_0 = const()[name = string("op_1259_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1259_cast_fp16 = slice_by_index(begin = var_1259_begin_0, end = var_1259_end_0, end_mask = var_1259_end_mask_0, x = x_119_cast_fp16)[name = string("op_1259_cast_fp16")];
+            tensor<int32, [4]> var_1260 = const()[name = string("op_1260"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_17_cast_fp16 = reshape(shape = var_1260, x = var_1259_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<int32, [4]> transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = string("transpose_282")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_104 = transpose(perm = transpose_104_perm_0, x = var_1243_cast_fp16)[name = string("transpose_283")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_19_end_0 = const()[name = string("matrix_bd_19_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1269_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = string("op_1269_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<fp16, [1, 8, 188, 188]> _inversed_scores_17_cast_fp16 = mul(x = var_1269_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = string("_inversed_scores_17_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_19_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_15)[name = string("scores_19_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1275_cast_fp16 = softmax(axis = var_152, x = scores_19_cast_fp16)[name = string("op_1275_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_249_cast_fp16 = select(a = var_164_to_fp16, b = var_1275_cast_fp16, cond = mask_15)[name = string("input_249_cast_fp16")];
+            bool x_121_transpose_x_0 = const()[name = string("x_121_transpose_x_0"), val = bool(false)];
+            bool x_121_transpose_y_0 = const()[name = string("x_121_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_9_cast_fp16)[name = string("transpose_281")];
+            tensor<fp16, [1, 8, 188, 128]> x_121_cast_fp16 = matmul(transpose_x = x_121_transpose_x_0, transpose_y = x_121_transpose_y_0, x = input_249_cast_fp16, y = value_13_cast_fp16)[name = string("x_121_cast_fp16")];
+            tensor<int32, [4]> var_1279_perm_0 = const()[name = string("op_1279_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1280 = const()[name = string("op_1280"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1279_cast_fp16 = transpose(perm = var_1279_perm_0, x = x_121_cast_fp16)[name = string("transpose_280")];
+            tensor<fp16, [1, 188, 1024]> input_251_cast_fp16 = reshape(shape = var_1280, x = var_1279_cast_fp16)[name = string("input_251_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57831808))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58356160))))[name = string("encoder_module_layers_4_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58356288)))];
+            tensor<fp16, [1, 188, 1024]> linear_43_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_out_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = string("linear_43_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_255_cast_fp16 = add(x = input_247_cast_fp16, y = linear_43_cast_fp16)[name = string("input_255_cast_fp16")];
+            tensor<int32, [1]> x_125_axes_0 = const()[name = string("x_125_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58358400)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58360512)))];
+            tensor<fp16, [1, 188, 1024]> x_125_cast_fp16 = layer_norm(axes = x_125_axes_0, beta = encoder_module_layers_4_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_conv_weight_to_fp16, x = input_255_cast_fp16)[name = string("x_125_cast_fp16")];
+            tensor<int32, [3]> input_257_perm_0 = const()[name = string("input_257_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_259_pad_type_0 = const()[name = string("input_259_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_259_strides_0 = const()[name = string("input_259_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_259_pad_0 = const()[name = string("input_259_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_259_dilations_0 = const()[name = string("input_259_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_259_groups_0 = const()[name = string("input_259_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_4_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58362624))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59411264))))[name = string("encoder_module_layers_4_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59411392)))];
+            tensor<fp16, [1, 1024, 188]> input_257_cast_fp16 = transpose(perm = input_257_perm_0, x = x_125_cast_fp16)[name = string("transpose_279")];
+            tensor<fp16, [1, 2048, 188]> input_259_cast_fp16 = conv(bias = encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_4_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_257_cast_fp16)[name = string("input_259_cast_fp16")];
+            int32 x_127_split_num_splits_0 = const()[name = string("x_127_split_num_splits_0"), val = int32(2)];
+            int32 x_127_split_axis_0 = const()[name = string("x_127_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_127_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_127_split_cast_fp16_1 = split(axis = x_127_split_axis_0, num_splits = x_127_split_num_splits_0, x = input_259_cast_fp16)[name = string("x_127_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_127_split_1_sigmoid_cast_fp16 = sigmoid(x = x_127_split_cast_fp16_1)[name = string("x_127_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_127_cast_fp16 = mul(x = x_127_split_cast_fp16_0, y = x_127_split_1_sigmoid_cast_fp16)[name = string("x_127_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_261_cast_fp16 = select(a = var_164_to_fp16, b = x_127_cast_fp16, cond = var_608)[name = string("input_261_cast_fp16")];
+            tensor<int32, [6]> input_263_pad_0 = const()[name = string("input_263_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_263_mode_0 = const()[name = string("input_263_mode_0"), val = string("constant")];
+            fp16 const_131_to_fp16 = const()[name = string("const_131_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_263_cast_fp16 = pad(constant_val = const_131_to_fp16, mode = input_263_mode_0, pad = input_263_pad_0, x = input_261_cast_fp16)[name = string("input_263_cast_fp16")];
+            string input_265_pad_type_0 = const()[name = string("input_265_pad_type_0"), val = string("valid")];
+            int32 input_265_groups_0 = const()[name = string("input_265_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_265_strides_0 = const()[name = string("input_265_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_265_pad_0 = const()[name = string("input_265_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_265_dilations_0 = const()[name = string("input_265_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_330_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59415552))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59420224))))[name = string("const_330_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_331_to_fp16 = const()[name = string("const_331_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59420352)))];
+            tensor<fp16, [1, 1024, 188]> input_267_cast_fp16 = conv(bias = const_331_to_fp16, dilations = input_265_dilations_0, groups = input_265_groups_0, pad = input_265_pad_0, pad_type = input_265_pad_type_0, strides = input_265_strides_0, weight = const_330_to_fp16_palettized, x = input_263_cast_fp16)[name = string("input_267_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_269_cast_fp16 = silu(x = input_267_cast_fp16)[name = string("input_269_cast_fp16")];
+            string x_129_pad_type_0 = const()[name = string("x_129_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_129_strides_0 = const()[name = string("x_129_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_129_pad_0 = const()[name = string("x_129_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_129_dilations_0 = const()[name = string("x_129_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_129_groups_0 = const()[name = string("x_129_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_4_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59422464))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59946816))))[name = string("encoder_module_layers_4_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59946944)))];
+            tensor<fp16, [1, 1024, 188]> x_129_cast_fp16 = conv(bias = encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_4_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_269_cast_fp16)[name = string("x_129_cast_fp16")];
+            tensor<int32, [3]> input_271_perm_0 = const()[name = string("input_271_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_271_cast_fp16 = transpose(perm = input_271_perm_0, x = x_129_cast_fp16)[name = string("transpose_278")];
+            tensor<fp16, [1, 188, 1024]> input_273_cast_fp16 = add(x = input_255_cast_fp16, y = input_271_cast_fp16)[name = string("input_273_cast_fp16")];
+            tensor<int32, [1]> input_275_axes_0 = const()[name = string("input_275_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59949056)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59951168)))];
+            tensor<fp16, [1, 188, 1024]> input_275_cast_fp16 = layer_norm(axes = input_275_axes_0, beta = encoder_module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59953280))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62050496))))[name = string("encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62050624)))];
+            tensor<fp16, [1, 188, 4096]> linear_44_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16_palettized, x = input_275_cast_fp16)[name = string("linear_44_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_279_cast_fp16 = silu(x = linear_44_cast_fp16)[name = string("input_279_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62058880))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64156096))))[name = string("encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64156224)))];
+            tensor<fp16, [1, 188, 1024]> linear_45_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16_palettized, x = input_279_cast_fp16)[name = string("linear_45_cast_fp16")];
+            fp16 var_1346_to_fp16 = const()[name = string("op_1346_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1347_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1346_to_fp16)[name = string("op_1347_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_285_cast_fp16 = add(x = input_273_cast_fp16, y = var_1347_cast_fp16)[name = string("input_285_cast_fp16")];
+            tensor<int32, [1]> input_287_axes_0 = const()[name = string("input_287_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64158336)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64160448)))];
+            tensor<fp16, [1, 188, 1024]> input_287_cast_fp16 = layer_norm(axes = input_287_axes_0, beta = encoder_module_layers_4_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_out_weight_to_fp16, x = input_285_cast_fp16)[name = string("input_287_cast_fp16")];
+            tensor<int32, [1]> input_289_axes_0 = const()[name = string("input_289_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64162560)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64164672)))];
+            tensor<fp16, [1, 188, 1024]> input_289_cast_fp16 = layer_norm(axes = input_289_axes_0, beta = encoder_module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_287_cast_fp16)[name = string("input_289_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64166784))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66264000))))[name = string("encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66264128)))];
+            tensor<fp16, [1, 188, 4096]> linear_46_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16_palettized, x = input_289_cast_fp16)[name = string("linear_46_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_293_cast_fp16 = silu(x = linear_46_cast_fp16)[name = string("input_293_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66272384))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68369600))))[name = string("encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68369728)))];
+            tensor<fp16, [1, 188, 1024]> linear_47_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16_palettized, x = input_293_cast_fp16)[name = string("linear_47_cast_fp16")];
+            fp16 var_1377_to_fp16 = const()[name = string("op_1377_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1378_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1377_to_fp16)[name = string("op_1378_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_299_cast_fp16 = add(x = input_287_cast_fp16, y = var_1378_cast_fp16)[name = string("input_299_cast_fp16")];
+            tensor<int32, [1]> query_11_axes_0 = const()[name = string("query_11_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68371840)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68373952)))];
+            tensor<fp16, [1, 188, 1024]> query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = encoder_module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_self_att_weight_to_fp16, x = input_299_cast_fp16)[name = string("query_11_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68376064))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68900416))))[name = string("encoder_module_layers_5_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68900544)))];
+            tensor<fp16, [1, 188, 1024]> linear_48_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_q_weight_to_fp16_palettized, x = query_11_cast_fp16)[name = string("linear_48_cast_fp16")];
+            tensor<int32, [4]> var_1395 = const()[name = string("op_1395"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_31_cast_fp16 = reshape(shape = var_1395, x = linear_48_cast_fp16)[name = string("q_31_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68902656))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69427008))))[name = string("encoder_module_layers_5_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69427136)))];
+            tensor<fp16, [1, 188, 1024]> linear_49_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_k_weight_to_fp16_palettized, x = query_11_cast_fp16)[name = string("linear_49_cast_fp16")];
+            tensor<int32, [4]> var_1400 = const()[name = string("op_1400"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_21_cast_fp16 = reshape(shape = var_1400, x = linear_49_cast_fp16)[name = string("k_21_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69429248))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69953600))))[name = string("encoder_module_layers_5_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69953728)))];
+            tensor<fp16, [1, 188, 1024]> linear_50_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_v_weight_to_fp16_palettized, x = query_11_cast_fp16)[name = string("linear_50_cast_fp16")];
+            tensor<int32, [4]> var_1405 = const()[name = string("op_1405"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_11_cast_fp16 = reshape(shape = var_1405, x = linear_50_cast_fp16)[name = string("v_11_cast_fp16")];
+            tensor<int32, [4]> value_15_perm_0 = const()[name = string("value_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69955840)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1417_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_module_layers_5_self_attn_pos_bias_u_to_fp16)[name = string("op_1417_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69957952)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1419_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_module_layers_5_self_attn_pos_bias_v_to_fp16)[name = string("op_1419_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_11_perm_0 = const()[name = string("q_with_bias_v_11_perm_0"), val = tensor<int32, [4]>([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<fp16, [1, 8, 128, 375]> op_1421_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69960064))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70152128))))[name = string("op_1421_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1419_cast_fp16)[name = string("transpose_277")];
+            tensor<fp16, [1, 8, 188, 375]> 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_1421_to_fp16_palettized)[name = string("x_137_cast_fp16")];
+            tensor<int32, [8]> x_139_pad_0 = const()[name = string("x_139_pad_0"), val = tensor<int32, [8]>([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_138_to_fp16 = const()[name = string("const_138_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_139_cast_fp16 = pad(constant_val = const_138_to_fp16, mode = x_139_mode_0, pad = x_139_pad_0, x = x_137_cast_fp16)[name = string("x_139_cast_fp16")];
+            tensor<int32, [4]> var_1429 = const()[name = string("op_1429"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_141_cast_fp16 = reshape(shape = var_1429, x = x_139_cast_fp16)[name = string("x_141_cast_fp16")];
+            tensor<int32, [4]> var_1433_begin_0 = const()[name = string("op_1433_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1433_end_0 = const()[name = string("op_1433_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1433_end_mask_0 = const()[name = string("op_1433_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1433_cast_fp16 = slice_by_index(begin = var_1433_begin_0, end = var_1433_end_0, end_mask = var_1433_end_mask_0, x = x_141_cast_fp16)[name = string("op_1433_cast_fp16")];
+            tensor<int32, [4]> var_1434 = const()[name = string("op_1434"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_21_cast_fp16 = reshape(shape = var_1434, x = var_1433_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<int32, [4]> transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = string("transpose_275")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1417_cast_fp16)[name = string("transpose_276")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_23_end_0 = const()[name = string("matrix_bd_23_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1443_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = string("op_1443_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<fp16, [1, 8, 188, 188]> _inversed_scores_21_cast_fp16 = mul(x = var_1443_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = string("_inversed_scores_21_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_23_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_15)[name = string("scores_23_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1449_cast_fp16 = softmax(axis = var_152, x = scores_23_cast_fp16)[name = string("op_1449_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_301_cast_fp16 = select(a = var_164_to_fp16, b = var_1449_cast_fp16, cond = mask_15)[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<fp16, [1, 8, 188, 128]> value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_11_cast_fp16)[name = string("transpose_274")];
+            tensor<fp16, [1, 8, 188, 128]> 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_15_cast_fp16)[name = string("x_143_cast_fp16")];
+            tensor<int32, [4]> var_1453_perm_0 = const()[name = string("op_1453_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1454 = const()[name = string("op_1454"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1453_cast_fp16 = transpose(perm = var_1453_perm_0, x = x_143_cast_fp16)[name = string("transpose_273")];
+            tensor<fp16, [1, 188, 1024]> input_303_cast_fp16 = reshape(shape = var_1454, x = var_1453_cast_fp16)[name = string("input_303_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70152256))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70676608))))[name = string("encoder_module_layers_5_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70676736)))];
+            tensor<fp16, [1, 188, 1024]> linear_52_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_out_weight_to_fp16_palettized, x = input_303_cast_fp16)[name = string("linear_52_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_307_cast_fp16 = add(x = input_299_cast_fp16, y = linear_52_cast_fp16)[name = string("input_307_cast_fp16")];
+            tensor<int32, [1]> x_147_axes_0 = const()[name = string("x_147_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70678848)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70680960)))];
+            tensor<fp16, [1, 188, 1024]> x_147_cast_fp16 = layer_norm(axes = x_147_axes_0, beta = encoder_module_layers_5_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_conv_weight_to_fp16, x = input_307_cast_fp16)[name = string("x_147_cast_fp16")];
+            tensor<int32, [3]> input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_311_pad_type_0 = const()[name = string("input_311_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_311_strides_0 = const()[name = string("input_311_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_311_pad_0 = const()[name = string("input_311_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_311_dilations_0 = const()[name = string("input_311_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_311_groups_0 = const()[name = string("input_311_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_5_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70683072))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71731712))))[name = string("encoder_module_layers_5_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71731840)))];
+            tensor<fp16, [1, 1024, 188]> input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_147_cast_fp16)[name = string("transpose_272")];
+            tensor<fp16, [1, 2048, 188]> input_311_cast_fp16 = conv(bias = encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_5_conv_pointwise_conv1_weight_to_fp16_palettized, 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<fp16, [1, 1024, 188]> x_149_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> input_313_cast_fp16 = select(a = var_164_to_fp16, b = x_149_cast_fp16, cond = var_608)[name = string("input_313_cast_fp16")];
+            tensor<int32, [6]> input_315_pad_0 = const()[name = string("input_315_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_315_mode_0 = const()[name = string("input_315_mode_0"), val = string("constant")];
+            fp16 const_141_to_fp16 = const()[name = string("const_141_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_315_cast_fp16 = pad(constant_val = const_141_to_fp16, mode = input_315_mode_0, pad = input_315_pad_0, x = input_313_cast_fp16)[name = string("input_315_cast_fp16")];
+            string input_317_pad_type_0 = const()[name = string("input_317_pad_type_0"), val = string("valid")];
+            int32 input_317_groups_0 = const()[name = string("input_317_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_317_strides_0 = const()[name = string("input_317_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_317_pad_0 = const()[name = string("input_317_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_317_dilations_0 = const()[name = string("input_317_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_332_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71736000))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71740672))))[name = string("const_332_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_333_to_fp16 = const()[name = string("const_333_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71740800)))];
+            tensor<fp16, [1, 1024, 188]> input_319_cast_fp16 = conv(bias = const_333_to_fp16, dilations = input_317_dilations_0, groups = input_317_groups_0, pad = input_317_pad_0, pad_type = input_317_pad_type_0, strides = input_317_strides_0, weight = const_332_to_fp16_palettized, x = input_315_cast_fp16)[name = string("input_319_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_321_cast_fp16 = silu(x = input_319_cast_fp16)[name = string("input_321_cast_fp16")];
+            string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_5_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71742912))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72267264))))[name = string("encoder_module_layers_5_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72267392)))];
+            tensor<fp16, [1, 1024, 188]> x_151_cast_fp16 = conv(bias = encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_5_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_321_cast_fp16)[name = string("x_151_cast_fp16")];
+            tensor<int32, [3]> input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = x_151_cast_fp16)[name = string("transpose_271")];
+            tensor<fp16, [1, 188, 1024]> input_325_cast_fp16 = add(x = input_307_cast_fp16, y = input_323_cast_fp16)[name = string("input_325_cast_fp16")];
+            tensor<int32, [1]> input_327_axes_0 = const()[name = string("input_327_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72269504)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72271616)))];
+            tensor<fp16, [1, 188, 1024]> input_327_cast_fp16 = layer_norm(axes = input_327_axes_0, beta = encoder_module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_325_cast_fp16)[name = string("input_327_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72273728))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74370944))))[name = string("encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74371072)))];
+            tensor<fp16, [1, 188, 4096]> linear_53_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16_palettized, x = input_327_cast_fp16)[name = string("linear_53_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_331_cast_fp16 = silu(x = linear_53_cast_fp16)[name = string("input_331_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74379328))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76476544))))[name = string("encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76476672)))];
+            tensor<fp16, [1, 188, 1024]> linear_54_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16_palettized, x = input_331_cast_fp16)[name = string("linear_54_cast_fp16")];
+            fp16 var_1520_to_fp16 = const()[name = string("op_1520_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1521_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1520_to_fp16)[name = string("op_1521_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_337_cast_fp16 = add(x = input_325_cast_fp16, y = var_1521_cast_fp16)[name = string("input_337_cast_fp16")];
+            tensor<int32, [1]> input_339_axes_0 = const()[name = string("input_339_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76478784)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76480896)))];
+            tensor<fp16, [1, 188, 1024]> input_339_cast_fp16 = layer_norm(axes = input_339_axes_0, beta = encoder_module_layers_5_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_out_weight_to_fp16, x = input_337_cast_fp16)[name = string("input_339_cast_fp16")];
+            tensor<int32, [1]> input_341_axes_0 = const()[name = string("input_341_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76483008)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76485120)))];
+            tensor<fp16, [1, 188, 1024]> input_341_cast_fp16 = layer_norm(axes = input_341_axes_0, beta = encoder_module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_339_cast_fp16)[name = string("input_341_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76487232))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78584448))))[name = string("encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78584576)))];
+            tensor<fp16, [1, 188, 4096]> linear_55_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16_palettized, x = input_341_cast_fp16)[name = string("linear_55_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_345_cast_fp16 = silu(x = linear_55_cast_fp16)[name = string("input_345_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78592832))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80690048))))[name = string("encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80690176)))];
+            tensor<fp16, [1, 188, 1024]> linear_56_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16_palettized, x = input_345_cast_fp16)[name = string("linear_56_cast_fp16")];
+            fp16 var_1551_to_fp16 = const()[name = string("op_1551_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1552_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1551_to_fp16)[name = string("op_1552_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_351_cast_fp16 = add(x = input_339_cast_fp16, y = var_1552_cast_fp16)[name = string("input_351_cast_fp16")];
+            tensor<int32, [1]> query_13_axes_0 = const()[name = string("query_13_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80692288)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80694400)))];
+            tensor<fp16, [1, 188, 1024]> query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = encoder_module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_self_att_weight_to_fp16, x = input_351_cast_fp16)[name = string("query_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80696512))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81220864))))[name = string("encoder_module_layers_6_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81220992)))];
+            tensor<fp16, [1, 188, 1024]> linear_57_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_q_weight_to_fp16_palettized, x = query_13_cast_fp16)[name = string("linear_57_cast_fp16")];
+            tensor<int32, [4]> var_1569 = const()[name = string("op_1569"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_37_cast_fp16 = reshape(shape = var_1569, x = linear_57_cast_fp16)[name = string("q_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81223104))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81747456))))[name = string("encoder_module_layers_6_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81747584)))];
+            tensor<fp16, [1, 188, 1024]> linear_58_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_k_weight_to_fp16_palettized, x = query_13_cast_fp16)[name = string("linear_58_cast_fp16")];
+            tensor<int32, [4]> var_1574 = const()[name = string("op_1574"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_25_cast_fp16 = reshape(shape = var_1574, x = linear_58_cast_fp16)[name = string("k_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81749696))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82274048))))[name = string("encoder_module_layers_6_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82274176)))];
+            tensor<fp16, [1, 188, 1024]> linear_59_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_v_weight_to_fp16_palettized, x = query_13_cast_fp16)[name = string("linear_59_cast_fp16")];
+            tensor<int32, [4]> var_1579 = const()[name = string("op_1579"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_13_cast_fp16 = reshape(shape = var_1579, x = linear_59_cast_fp16)[name = string("v_13_cast_fp16")];
+            tensor<int32, [4]> value_17_perm_0 = const()[name = string("value_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82276288)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1591_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_module_layers_6_self_attn_pos_bias_u_to_fp16)[name = string("op_1591_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82278400)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1593_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_module_layers_6_self_attn_pos_bias_v_to_fp16)[name = string("op_1593_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_13_perm_0 = const()[name = string("q_with_bias_v_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_159_transpose_x_0 = const()[name = string("x_159_transpose_x_0"), val = bool(false)];
+            bool x_159_transpose_y_0 = const()[name = string("x_159_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1595_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82280512))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82472576))))[name = string("op_1595_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1593_cast_fp16)[name = string("transpose_270")];
+            tensor<fp16, [1, 8, 188, 375]> x_159_cast_fp16 = matmul(transpose_x = x_159_transpose_x_0, transpose_y = x_159_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = op_1595_to_fp16_palettized)[name = string("x_159_cast_fp16")];
+            tensor<int32, [8]> x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_161_mode_0 = const()[name = string("x_161_mode_0"), val = string("constant")];
+            fp16 const_148_to_fp16 = const()[name = string("const_148_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_161_cast_fp16 = pad(constant_val = const_148_to_fp16, mode = x_161_mode_0, pad = x_161_pad_0, x = x_159_cast_fp16)[name = string("x_161_cast_fp16")];
+            tensor<int32, [4]> var_1603 = const()[name = string("op_1603"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_163_cast_fp16 = reshape(shape = var_1603, x = x_161_cast_fp16)[name = string("x_163_cast_fp16")];
+            tensor<int32, [4]> var_1607_begin_0 = const()[name = string("op_1607_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1607_end_0 = const()[name = string("op_1607_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1607_end_mask_0 = const()[name = string("op_1607_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1607_cast_fp16 = slice_by_index(begin = var_1607_begin_0, end = var_1607_end_0, end_mask = var_1607_end_mask_0, x = x_163_cast_fp16)[name = string("op_1607_cast_fp16")];
+            tensor<int32, [4]> var_1608 = const()[name = string("op_1608"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_25_cast_fp16 = reshape(shape = var_1608, x = var_1607_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<int32, [4]> transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = string("transpose_268")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1591_cast_fp16)[name = string("transpose_269")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_27_end_0 = const()[name = string("matrix_bd_27_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1617_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = string("op_1617_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<fp16, [1, 8, 188, 188]> _inversed_scores_25_cast_fp16 = mul(x = var_1617_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = string("_inversed_scores_25_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_27_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_15)[name = string("scores_27_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1623_cast_fp16 = softmax(axis = var_152, x = scores_27_cast_fp16)[name = string("op_1623_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_353_cast_fp16 = select(a = var_164_to_fp16, b = var_1623_cast_fp16, cond = mask_15)[name = string("input_353_cast_fp16")];
+            bool x_165_transpose_x_0 = const()[name = string("x_165_transpose_x_0"), val = bool(false)];
+            bool x_165_transpose_y_0 = const()[name = string("x_165_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_13_cast_fp16)[name = string("transpose_267")];
+            tensor<fp16, [1, 8, 188, 128]> x_165_cast_fp16 = matmul(transpose_x = x_165_transpose_x_0, transpose_y = x_165_transpose_y_0, x = input_353_cast_fp16, y = value_17_cast_fp16)[name = string("x_165_cast_fp16")];
+            tensor<int32, [4]> var_1627_perm_0 = const()[name = string("op_1627_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1628 = const()[name = string("op_1628"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1627_cast_fp16 = transpose(perm = var_1627_perm_0, x = x_165_cast_fp16)[name = string("transpose_266")];
+            tensor<fp16, [1, 188, 1024]> input_355_cast_fp16 = reshape(shape = var_1628, x = var_1627_cast_fp16)[name = string("input_355_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82472704))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82997056))))[name = string("encoder_module_layers_6_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82997184)))];
+            tensor<fp16, [1, 188, 1024]> linear_61_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_out_weight_to_fp16_palettized, x = input_355_cast_fp16)[name = string("linear_61_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_359_cast_fp16 = add(x = input_351_cast_fp16, y = linear_61_cast_fp16)[name = string("input_359_cast_fp16")];
+            tensor<int32, [1]> x_169_axes_0 = const()[name = string("x_169_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82999296)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83001408)))];
+            tensor<fp16, [1, 188, 1024]> x_169_cast_fp16 = layer_norm(axes = x_169_axes_0, beta = encoder_module_layers_6_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_conv_weight_to_fp16, x = input_359_cast_fp16)[name = string("x_169_cast_fp16")];
+            tensor<int32, [3]> input_361_perm_0 = const()[name = string("input_361_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_363_pad_type_0 = const()[name = string("input_363_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_363_strides_0 = const()[name = string("input_363_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_363_pad_0 = const()[name = string("input_363_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_363_dilations_0 = const()[name = string("input_363_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_363_groups_0 = const()[name = string("input_363_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_6_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83003520))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84052160))))[name = string("encoder_module_layers_6_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84052288)))];
+            tensor<fp16, [1, 1024, 188]> input_361_cast_fp16 = transpose(perm = input_361_perm_0, x = x_169_cast_fp16)[name = string("transpose_265")];
+            tensor<fp16, [1, 2048, 188]> input_363_cast_fp16 = conv(bias = encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_6_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = string("input_363_cast_fp16")];
+            int32 x_171_split_num_splits_0 = const()[name = string("x_171_split_num_splits_0"), val = int32(2)];
+            int32 x_171_split_axis_0 = const()[name = string("x_171_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_171_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_171_split_cast_fp16_1 = split(axis = x_171_split_axis_0, num_splits = x_171_split_num_splits_0, x = input_363_cast_fp16)[name = string("x_171_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_171_split_1_sigmoid_cast_fp16 = sigmoid(x = x_171_split_cast_fp16_1)[name = string("x_171_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_171_cast_fp16 = mul(x = x_171_split_cast_fp16_0, y = x_171_split_1_sigmoid_cast_fp16)[name = string("x_171_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_365_cast_fp16 = select(a = var_164_to_fp16, b = x_171_cast_fp16, cond = var_608)[name = string("input_365_cast_fp16")];
+            tensor<int32, [6]> input_367_pad_0 = const()[name = string("input_367_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_367_mode_0 = const()[name = string("input_367_mode_0"), val = string("constant")];
+            fp16 const_151_to_fp16 = const()[name = string("const_151_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_367_cast_fp16 = pad(constant_val = const_151_to_fp16, mode = input_367_mode_0, pad = input_367_pad_0, x = input_365_cast_fp16)[name = string("input_367_cast_fp16")];
+            string input_369_pad_type_0 = const()[name = string("input_369_pad_type_0"), val = string("valid")];
+            int32 input_369_groups_0 = const()[name = string("input_369_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_369_strides_0 = const()[name = string("input_369_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_369_dilations_0 = const()[name = string("input_369_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_334_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84056448))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84061120))))[name = string("const_334_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_335_to_fp16 = const()[name = string("const_335_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84061248)))];
+            tensor<fp16, [1, 1024, 188]> input_371_cast_fp16 = conv(bias = const_335_to_fp16, dilations = input_369_dilations_0, groups = input_369_groups_0, pad = input_369_pad_0, pad_type = input_369_pad_type_0, strides = input_369_strides_0, weight = const_334_to_fp16_palettized, x = input_367_cast_fp16)[name = string("input_371_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_373_cast_fp16 = silu(x = input_371_cast_fp16)[name = string("input_373_cast_fp16")];
+            string x_173_pad_type_0 = const()[name = string("x_173_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_173_strides_0 = const()[name = string("x_173_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_173_pad_0 = const()[name = string("x_173_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_173_dilations_0 = const()[name = string("x_173_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_173_groups_0 = const()[name = string("x_173_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84063360))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84587712))))[name = string("encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84587840)))];
+            tensor<fp16, [1, 1024, 188]> x_173_cast_fp16 = conv(bias = encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16, dilations = x_173_dilations_0, groups = x_173_groups_0, pad = x_173_pad_0, pad_type = x_173_pad_type_0, strides = x_173_strides_0, weight = encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_373_cast_fp16)[name = string("x_173_cast_fp16")];
+            tensor<int32, [3]> input_375_perm_0 = const()[name = string("input_375_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_375_cast_fp16 = transpose(perm = input_375_perm_0, x = x_173_cast_fp16)[name = string("transpose_264")];
+            tensor<fp16, [1, 188, 1024]> input_377_cast_fp16 = add(x = input_359_cast_fp16, y = input_375_cast_fp16)[name = string("input_377_cast_fp16")];
+            tensor<int32, [1]> input_379_axes_0 = const()[name = string("input_379_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84589952)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84592064)))];
+            tensor<fp16, [1, 188, 1024]> input_379_cast_fp16 = layer_norm(axes = input_379_axes_0, beta = encoder_module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_377_cast_fp16)[name = string("input_379_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84594176))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86691392))))[name = string("encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86691520)))];
+            tensor<fp16, [1, 188, 4096]> linear_62_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16_palettized, x = input_379_cast_fp16)[name = string("linear_62_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_383_cast_fp16 = silu(x = linear_62_cast_fp16)[name = string("input_383_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86699776))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88796992))))[name = string("encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88797120)))];
+            tensor<fp16, [1, 188, 1024]> linear_63_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16_palettized, x = input_383_cast_fp16)[name = string("linear_63_cast_fp16")];
+            fp16 var_1694_to_fp16 = const()[name = string("op_1694_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1695_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1694_to_fp16)[name = string("op_1695_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_389_cast_fp16 = add(x = input_377_cast_fp16, y = var_1695_cast_fp16)[name = string("input_389_cast_fp16")];
+            tensor<int32, [1]> input_391_axes_0 = const()[name = string("input_391_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88799232)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88801344)))];
+            tensor<fp16, [1, 188, 1024]> input_391_cast_fp16 = layer_norm(axes = input_391_axes_0, beta = encoder_module_layers_6_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_out_weight_to_fp16, x = input_389_cast_fp16)[name = string("input_391_cast_fp16")];
+            tensor<int32, [1]> input_393_axes_0 = const()[name = string("input_393_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88803456)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88805568)))];
+            tensor<fp16, [1, 188, 1024]> input_393_cast_fp16 = layer_norm(axes = input_393_axes_0, beta = encoder_module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_391_cast_fp16)[name = string("input_393_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88807680))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90904896))))[name = string("encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90905024)))];
+            tensor<fp16, [1, 188, 4096]> linear_64_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16_palettized, x = input_393_cast_fp16)[name = string("linear_64_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_397_cast_fp16 = silu(x = linear_64_cast_fp16)[name = string("input_397_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90913280))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93010496))))[name = string("encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93010624)))];
+            tensor<fp16, [1, 188, 1024]> linear_65_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16_palettized, x = input_397_cast_fp16)[name = string("linear_65_cast_fp16")];
+            fp16 var_1725_to_fp16 = const()[name = string("op_1725_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1726_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1725_to_fp16)[name = string("op_1726_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_403_cast_fp16 = add(x = input_391_cast_fp16, y = var_1726_cast_fp16)[name = string("input_403_cast_fp16")];
+            tensor<int32, [1]> query_15_axes_0 = const()[name = string("query_15_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93012736)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93014848)))];
+            tensor<fp16, [1, 188, 1024]> query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = encoder_module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_self_att_weight_to_fp16, x = input_403_cast_fp16)[name = string("query_15_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93016960))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93541312))))[name = string("encoder_module_layers_7_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93541440)))];
+            tensor<fp16, [1, 188, 1024]> linear_66_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_q_weight_to_fp16_palettized, x = query_15_cast_fp16)[name = string("linear_66_cast_fp16")];
+            tensor<int32, [4]> var_1743 = const()[name = string("op_1743"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_43_cast_fp16 = reshape(shape = var_1743, x = linear_66_cast_fp16)[name = string("q_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93543552))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94067904))))[name = string("encoder_module_layers_7_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94068032)))];
+            tensor<fp16, [1, 188, 1024]> linear_67_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_k_weight_to_fp16_palettized, x = query_15_cast_fp16)[name = string("linear_67_cast_fp16")];
+            tensor<int32, [4]> var_1748 = const()[name = string("op_1748"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_29_cast_fp16 = reshape(shape = var_1748, x = linear_67_cast_fp16)[name = string("k_29_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94070144))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94594496))))[name = string("encoder_module_layers_7_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94594624)))];
+            tensor<fp16, [1, 188, 1024]> linear_68_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_v_weight_to_fp16_palettized, x = query_15_cast_fp16)[name = string("linear_68_cast_fp16")];
+            tensor<int32, [4]> var_1753 = const()[name = string("op_1753"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_15_cast_fp16 = reshape(shape = var_1753, x = linear_68_cast_fp16)[name = string("v_15_cast_fp16")];
+            tensor<int32, [4]> value_19_perm_0 = const()[name = string("value_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94596736)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1765_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_module_layers_7_self_attn_pos_bias_u_to_fp16)[name = string("op_1765_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94598848)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1767_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_module_layers_7_self_attn_pos_bias_v_to_fp16)[name = string("op_1767_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_15_perm_0 = const()[name = string("q_with_bias_v_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_181_transpose_x_0 = const()[name = string("x_181_transpose_x_0"), val = bool(false)];
+            bool x_181_transpose_y_0 = const()[name = string("x_181_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1769_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94600960))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94793024))))[name = string("op_1769_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1767_cast_fp16)[name = string("transpose_263")];
+            tensor<fp16, [1, 8, 188, 375]> x_181_cast_fp16 = matmul(transpose_x = x_181_transpose_x_0, transpose_y = x_181_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = op_1769_to_fp16_palettized)[name = string("x_181_cast_fp16")];
+            tensor<int32, [8]> x_183_pad_0 = const()[name = string("x_183_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_183_mode_0 = const()[name = string("x_183_mode_0"), val = string("constant")];
+            fp16 const_158_to_fp16 = const()[name = string("const_158_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_183_cast_fp16 = pad(constant_val = const_158_to_fp16, mode = x_183_mode_0, pad = x_183_pad_0, x = x_181_cast_fp16)[name = string("x_183_cast_fp16")];
+            tensor<int32, [4]> var_1777 = const()[name = string("op_1777"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_185_cast_fp16 = reshape(shape = var_1777, x = x_183_cast_fp16)[name = string("x_185_cast_fp16")];
+            tensor<int32, [4]> var_1781_begin_0 = const()[name = string("op_1781_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1781_end_0 = const()[name = string("op_1781_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1781_end_mask_0 = const()[name = string("op_1781_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1781_cast_fp16 = slice_by_index(begin = var_1781_begin_0, end = var_1781_end_0, end_mask = var_1781_end_mask_0, x = x_185_cast_fp16)[name = string("op_1781_cast_fp16")];
+            tensor<int32, [4]> var_1782 = const()[name = string("op_1782"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_29_cast_fp16 = reshape(shape = var_1782, x = var_1781_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<int32, [4]> transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = string("transpose_261")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_110 = transpose(perm = transpose_110_perm_0, x = var_1765_cast_fp16)[name = string("transpose_262")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_31_end_0 = const()[name = string("matrix_bd_31_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1791_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = string("op_1791_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<fp16, [1, 8, 188, 188]> _inversed_scores_29_cast_fp16 = mul(x = var_1791_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = string("_inversed_scores_29_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_31_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_15)[name = string("scores_31_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1797_cast_fp16 = softmax(axis = var_152, x = scores_31_cast_fp16)[name = string("op_1797_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_405_cast_fp16 = select(a = var_164_to_fp16, b = var_1797_cast_fp16, cond = mask_15)[name = string("input_405_cast_fp16")];
+            bool x_187_transpose_x_0 = const()[name = string("x_187_transpose_x_0"), val = bool(false)];
+            bool x_187_transpose_y_0 = const()[name = string("x_187_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_15_cast_fp16)[name = string("transpose_260")];
+            tensor<fp16, [1, 8, 188, 128]> x_187_cast_fp16 = matmul(transpose_x = x_187_transpose_x_0, transpose_y = x_187_transpose_y_0, x = input_405_cast_fp16, y = value_19_cast_fp16)[name = string("x_187_cast_fp16")];
+            tensor<int32, [4]> var_1801_perm_0 = const()[name = string("op_1801_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1802 = const()[name = string("op_1802"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1801_cast_fp16 = transpose(perm = var_1801_perm_0, x = x_187_cast_fp16)[name = string("transpose_259")];
+            tensor<fp16, [1, 188, 1024]> input_407_cast_fp16 = reshape(shape = var_1802, x = var_1801_cast_fp16)[name = string("input_407_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94793152))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95317504))))[name = string("encoder_module_layers_7_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95317632)))];
+            tensor<fp16, [1, 188, 1024]> linear_70_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_out_weight_to_fp16_palettized, x = input_407_cast_fp16)[name = string("linear_70_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_411_cast_fp16 = add(x = input_403_cast_fp16, y = linear_70_cast_fp16)[name = string("input_411_cast_fp16")];
+            tensor<int32, [1]> x_191_axes_0 = const()[name = string("x_191_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95319744)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95321856)))];
+            tensor<fp16, [1, 188, 1024]> x_191_cast_fp16 = layer_norm(axes = x_191_axes_0, beta = encoder_module_layers_7_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_conv_weight_to_fp16, x = input_411_cast_fp16)[name = string("x_191_cast_fp16")];
+            tensor<int32, [3]> input_413_perm_0 = const()[name = string("input_413_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_415_pad_type_0 = const()[name = string("input_415_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_415_strides_0 = const()[name = string("input_415_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_415_pad_0 = const()[name = string("input_415_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_415_dilations_0 = const()[name = string("input_415_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_415_groups_0 = const()[name = string("input_415_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_7_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95323968))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96372608))))[name = string("encoder_module_layers_7_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96372736)))];
+            tensor<fp16, [1, 1024, 188]> input_413_cast_fp16 = transpose(perm = input_413_perm_0, x = x_191_cast_fp16)[name = string("transpose_258")];
+            tensor<fp16, [1, 2048, 188]> input_415_cast_fp16 = conv(bias = encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_7_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_413_cast_fp16)[name = string("input_415_cast_fp16")];
+            int32 x_193_split_num_splits_0 = const()[name = string("x_193_split_num_splits_0"), val = int32(2)];
+            int32 x_193_split_axis_0 = const()[name = string("x_193_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_193_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_193_split_cast_fp16_1 = split(axis = x_193_split_axis_0, num_splits = x_193_split_num_splits_0, x = input_415_cast_fp16)[name = string("x_193_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_193_split_1_sigmoid_cast_fp16 = sigmoid(x = x_193_split_cast_fp16_1)[name = string("x_193_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_193_cast_fp16 = mul(x = x_193_split_cast_fp16_0, y = x_193_split_1_sigmoid_cast_fp16)[name = string("x_193_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_417_cast_fp16 = select(a = var_164_to_fp16, b = x_193_cast_fp16, cond = var_608)[name = string("input_417_cast_fp16")];
+            tensor<int32, [6]> input_419_pad_0 = const()[name = string("input_419_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("constant")];
+            fp16 const_161_to_fp16 = const()[name = string("const_161_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_419_cast_fp16 = pad(constant_val = const_161_to_fp16, mode = input_419_mode_0, pad = input_419_pad_0, x = input_417_cast_fp16)[name = string("input_419_cast_fp16")];
+            string input_421_pad_type_0 = const()[name = string("input_421_pad_type_0"), val = string("valid")];
+            int32 input_421_groups_0 = const()[name = string("input_421_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_421_strides_0 = const()[name = string("input_421_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_421_pad_0 = const()[name = string("input_421_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_421_dilations_0 = const()[name = string("input_421_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_336_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96376896))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96381568))))[name = string("const_336_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_337_to_fp16 = const()[name = string("const_337_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96381696)))];
+            tensor<fp16, [1, 1024, 188]> input_423_cast_fp16 = conv(bias = const_337_to_fp16, dilations = input_421_dilations_0, groups = input_421_groups_0, pad = input_421_pad_0, pad_type = input_421_pad_type_0, strides = input_421_strides_0, weight = const_336_to_fp16_palettized, x = input_419_cast_fp16)[name = string("input_423_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_425_cast_fp16 = silu(x = input_423_cast_fp16)[name = string("input_425_cast_fp16")];
+            string x_195_pad_type_0 = const()[name = string("x_195_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_195_strides_0 = const()[name = string("x_195_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_195_pad_0 = const()[name = string("x_195_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_195_dilations_0 = const()[name = string("x_195_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_195_groups_0 = const()[name = string("x_195_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96383808))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96908160))))[name = string("encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96908288)))];
+            tensor<fp16, [1, 1024, 188]> x_195_cast_fp16 = conv(bias = encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16, dilations = x_195_dilations_0, groups = x_195_groups_0, pad = x_195_pad_0, pad_type = x_195_pad_type_0, strides = x_195_strides_0, weight = encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_425_cast_fp16)[name = string("x_195_cast_fp16")];
+            tensor<int32, [3]> input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_195_cast_fp16)[name = string("transpose_257")];
+            tensor<fp16, [1, 188, 1024]> input_429_cast_fp16 = add(x = input_411_cast_fp16, y = input_427_cast_fp16)[name = string("input_429_cast_fp16")];
+            tensor<int32, [1]> input_431_axes_0 = const()[name = string("input_431_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96910400)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96912512)))];
+            tensor<fp16, [1, 188, 1024]> input_431_cast_fp16 = layer_norm(axes = input_431_axes_0, beta = encoder_module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_429_cast_fp16)[name = string("input_431_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96914624))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99011840))))[name = string("encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99011968)))];
+            tensor<fp16, [1, 188, 4096]> linear_71_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16_palettized, x = input_431_cast_fp16)[name = string("linear_71_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_435_cast_fp16 = silu(x = linear_71_cast_fp16)[name = string("input_435_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99020224))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101117440))))[name = string("encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101117568)))];
+            tensor<fp16, [1, 188, 1024]> linear_72_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16_palettized, x = input_435_cast_fp16)[name = string("linear_72_cast_fp16")];
+            fp16 var_1868_to_fp16 = const()[name = string("op_1868_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1869_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1868_to_fp16)[name = string("op_1869_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_441_cast_fp16 = add(x = input_429_cast_fp16, y = var_1869_cast_fp16)[name = string("input_441_cast_fp16")];
+            tensor<int32, [1]> input_443_axes_0 = const()[name = string("input_443_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101119680)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101121792)))];
+            tensor<fp16, [1, 188, 1024]> input_443_cast_fp16 = layer_norm(axes = input_443_axes_0, beta = encoder_module_layers_7_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_out_weight_to_fp16, x = input_441_cast_fp16)[name = string("input_443_cast_fp16")];
+            tensor<int32, [1]> input_445_axes_0 = const()[name = string("input_445_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101123904)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101126016)))];
+            tensor<fp16, [1, 188, 1024]> input_445_cast_fp16 = layer_norm(axes = input_445_axes_0, beta = encoder_module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_443_cast_fp16)[name = string("input_445_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101128128))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103225344))))[name = string("encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103225472)))];
+            tensor<fp16, [1, 188, 4096]> linear_73_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16_palettized, x = input_445_cast_fp16)[name = string("linear_73_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_449_cast_fp16 = silu(x = linear_73_cast_fp16)[name = string("input_449_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103233728))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105330944))))[name = string("encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105331072)))];
+            tensor<fp16, [1, 188, 1024]> linear_74_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16_palettized, x = input_449_cast_fp16)[name = string("linear_74_cast_fp16")];
+            fp16 var_1899_to_fp16 = const()[name = string("op_1899_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1900_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1899_to_fp16)[name = string("op_1900_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_455_cast_fp16 = add(x = input_443_cast_fp16, y = var_1900_cast_fp16)[name = string("input_455_cast_fp16")];
+            tensor<int32, [1]> query_17_axes_0 = const()[name = string("query_17_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105333184)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105335296)))];
+            tensor<fp16, [1, 188, 1024]> query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = encoder_module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_self_att_weight_to_fp16, x = input_455_cast_fp16)[name = string("query_17_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105337408))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105861760))))[name = string("encoder_module_layers_8_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105861888)))];
+            tensor<fp16, [1, 188, 1024]> linear_75_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_q_weight_to_fp16_palettized, x = query_17_cast_fp16)[name = string("linear_75_cast_fp16")];
+            tensor<int32, [4]> var_1917 = const()[name = string("op_1917"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_49_cast_fp16 = reshape(shape = var_1917, x = linear_75_cast_fp16)[name = string("q_49_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105864000))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106388352))))[name = string("encoder_module_layers_8_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106388480)))];
+            tensor<fp16, [1, 188, 1024]> linear_76_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_k_weight_to_fp16_palettized, x = query_17_cast_fp16)[name = string("linear_76_cast_fp16")];
+            tensor<int32, [4]> var_1922 = const()[name = string("op_1922"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_33_cast_fp16 = reshape(shape = var_1922, x = linear_76_cast_fp16)[name = string("k_33_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106390592))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106914944))))[name = string("encoder_module_layers_8_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106915072)))];
+            tensor<fp16, [1, 188, 1024]> linear_77_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_v_weight_to_fp16_palettized, x = query_17_cast_fp16)[name = string("linear_77_cast_fp16")];
+            tensor<int32, [4]> var_1927 = const()[name = string("op_1927"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_17_cast_fp16 = reshape(shape = var_1927, x = linear_77_cast_fp16)[name = string("v_17_cast_fp16")];
+            tensor<int32, [4]> value_21_perm_0 = const()[name = string("value_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106917184)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1939_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_module_layers_8_self_attn_pos_bias_u_to_fp16)[name = string("op_1939_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106919296)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1941_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_module_layers_8_self_attn_pos_bias_v_to_fp16)[name = string("op_1941_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_17_perm_0 = const()[name = string("q_with_bias_v_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_203_transpose_x_0 = const()[name = string("x_203_transpose_x_0"), val = bool(false)];
+            bool x_203_transpose_y_0 = const()[name = string("x_203_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1943_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106921408))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107113472))))[name = string("op_1943_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1941_cast_fp16)[name = string("transpose_256")];
+            tensor<fp16, [1, 8, 188, 375]> x_203_cast_fp16 = matmul(transpose_x = x_203_transpose_x_0, transpose_y = x_203_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = op_1943_to_fp16_palettized)[name = string("x_203_cast_fp16")];
+            tensor<int32, [8]> x_205_pad_0 = const()[name = string("x_205_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_205_mode_0 = const()[name = string("x_205_mode_0"), val = string("constant")];
+            fp16 const_168_to_fp16 = const()[name = string("const_168_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_205_cast_fp16 = pad(constant_val = const_168_to_fp16, mode = x_205_mode_0, pad = x_205_pad_0, x = x_203_cast_fp16)[name = string("x_205_cast_fp16")];
+            tensor<int32, [4]> var_1951 = const()[name = string("op_1951"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_207_cast_fp16 = reshape(shape = var_1951, x = x_205_cast_fp16)[name = string("x_207_cast_fp16")];
+            tensor<int32, [4]> var_1955_begin_0 = const()[name = string("op_1955_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1955_end_0 = const()[name = string("op_1955_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1955_end_mask_0 = const()[name = string("op_1955_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1955_cast_fp16 = slice_by_index(begin = var_1955_begin_0, end = var_1955_end_0, end_mask = var_1955_end_mask_0, x = x_207_cast_fp16)[name = string("op_1955_cast_fp16")];
+            tensor<int32, [4]> var_1956 = const()[name = string("op_1956"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_33_cast_fp16 = reshape(shape = var_1956, x = var_1955_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<int32, [4]> transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = string("transpose_254")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_112 = transpose(perm = transpose_112_perm_0, x = var_1939_cast_fp16)[name = string("transpose_255")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_35_end_0 = const()[name = string("matrix_bd_35_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1965_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = string("op_1965_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<fp16, [1, 8, 188, 188]> _inversed_scores_33_cast_fp16 = mul(x = var_1965_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = string("_inversed_scores_33_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_35_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_15)[name = string("scores_35_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1971_cast_fp16 = softmax(axis = var_152, x = scores_35_cast_fp16)[name = string("op_1971_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_457_cast_fp16 = select(a = var_164_to_fp16, b = var_1971_cast_fp16, cond = mask_15)[name = string("input_457_cast_fp16")];
+            bool x_209_transpose_x_0 = const()[name = string("x_209_transpose_x_0"), val = bool(false)];
+            bool x_209_transpose_y_0 = const()[name = string("x_209_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_17_cast_fp16)[name = string("transpose_253")];
+            tensor<fp16, [1, 8, 188, 128]> x_209_cast_fp16 = matmul(transpose_x = x_209_transpose_x_0, transpose_y = x_209_transpose_y_0, x = input_457_cast_fp16, y = value_21_cast_fp16)[name = string("x_209_cast_fp16")];
+            tensor<int32, [4]> var_1975_perm_0 = const()[name = string("op_1975_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1976 = const()[name = string("op_1976"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1975_cast_fp16 = transpose(perm = var_1975_perm_0, x = x_209_cast_fp16)[name = string("transpose_252")];
+            tensor<fp16, [1, 188, 1024]> input_459_cast_fp16 = reshape(shape = var_1976, x = var_1975_cast_fp16)[name = string("input_459_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107113600))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107637952))))[name = string("encoder_module_layers_8_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107638080)))];
+            tensor<fp16, [1, 188, 1024]> linear_79_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_out_weight_to_fp16_palettized, x = input_459_cast_fp16)[name = string("linear_79_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_463_cast_fp16 = add(x = input_455_cast_fp16, y = linear_79_cast_fp16)[name = string("input_463_cast_fp16")];
+            tensor<int32, [1]> x_213_axes_0 = const()[name = string("x_213_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107640192)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107642304)))];
+            tensor<fp16, [1, 188, 1024]> x_213_cast_fp16 = layer_norm(axes = x_213_axes_0, beta = encoder_module_layers_8_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_conv_weight_to_fp16, x = input_463_cast_fp16)[name = string("x_213_cast_fp16")];
+            tensor<int32, [3]> input_465_perm_0 = const()[name = string("input_465_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_467_pad_type_0 = const()[name = string("input_467_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_467_strides_0 = const()[name = string("input_467_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_467_pad_0 = const()[name = string("input_467_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_467_dilations_0 = const()[name = string("input_467_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_467_groups_0 = const()[name = string("input_467_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_8_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107644416))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108693056))))[name = string("encoder_module_layers_8_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108693184)))];
+            tensor<fp16, [1, 1024, 188]> input_465_cast_fp16 = transpose(perm = input_465_perm_0, x = x_213_cast_fp16)[name = string("transpose_251")];
+            tensor<fp16, [1, 2048, 188]> input_467_cast_fp16 = conv(bias = encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_8_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_465_cast_fp16)[name = string("input_467_cast_fp16")];
+            int32 x_215_split_num_splits_0 = const()[name = string("x_215_split_num_splits_0"), val = int32(2)];
+            int32 x_215_split_axis_0 = const()[name = string("x_215_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_215_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_215_split_cast_fp16_1 = split(axis = x_215_split_axis_0, num_splits = x_215_split_num_splits_0, x = input_467_cast_fp16)[name = string("x_215_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_215_split_1_sigmoid_cast_fp16 = sigmoid(x = x_215_split_cast_fp16_1)[name = string("x_215_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_215_cast_fp16 = mul(x = x_215_split_cast_fp16_0, y = x_215_split_1_sigmoid_cast_fp16)[name = string("x_215_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_469_cast_fp16 = select(a = var_164_to_fp16, b = x_215_cast_fp16, cond = var_608)[name = string("input_469_cast_fp16")];
+            tensor<int32, [6]> input_471_pad_0 = const()[name = string("input_471_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_471_mode_0 = const()[name = string("input_471_mode_0"), val = string("constant")];
+            fp16 const_171_to_fp16 = const()[name = string("const_171_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_471_cast_fp16 = pad(constant_val = const_171_to_fp16, mode = input_471_mode_0, pad = input_471_pad_0, x = input_469_cast_fp16)[name = string("input_471_cast_fp16")];
+            string input_473_pad_type_0 = const()[name = string("input_473_pad_type_0"), val = string("valid")];
+            int32 input_473_groups_0 = const()[name = string("input_473_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_473_strides_0 = const()[name = string("input_473_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_473_pad_0 = const()[name = string("input_473_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_473_dilations_0 = const()[name = string("input_473_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_338_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108697344))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108702016))))[name = string("const_338_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_339_to_fp16 = const()[name = string("const_339_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108702144)))];
+            tensor<fp16, [1, 1024, 188]> input_475_cast_fp16 = conv(bias = const_339_to_fp16, dilations = input_473_dilations_0, groups = input_473_groups_0, pad = input_473_pad_0, pad_type = input_473_pad_type_0, strides = input_473_strides_0, weight = const_338_to_fp16_palettized, x = input_471_cast_fp16)[name = string("input_475_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_477_cast_fp16 = silu(x = input_475_cast_fp16)[name = string("input_477_cast_fp16")];
+            string x_217_pad_type_0 = const()[name = string("x_217_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_217_strides_0 = const()[name = string("x_217_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_217_pad_0 = const()[name = string("x_217_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_217_dilations_0 = const()[name = string("x_217_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_217_groups_0 = const()[name = string("x_217_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108704256))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109228608))))[name = string("encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109228736)))];
+            tensor<fp16, [1, 1024, 188]> x_217_cast_fp16 = conv(bias = encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16, dilations = x_217_dilations_0, groups = x_217_groups_0, pad = x_217_pad_0, pad_type = x_217_pad_type_0, strides = x_217_strides_0, weight = encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_477_cast_fp16)[name = string("x_217_cast_fp16")];
+            tensor<int32, [3]> input_479_perm_0 = const()[name = string("input_479_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_479_cast_fp16 = transpose(perm = input_479_perm_0, x = x_217_cast_fp16)[name = string("transpose_250")];
+            tensor<fp16, [1, 188, 1024]> input_481_cast_fp16 = add(x = input_463_cast_fp16, y = input_479_cast_fp16)[name = string("input_481_cast_fp16")];
+            tensor<int32, [1]> input_483_axes_0 = const()[name = string("input_483_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109230848)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109232960)))];
+            tensor<fp16, [1, 188, 1024]> input_483_cast_fp16 = layer_norm(axes = input_483_axes_0, beta = encoder_module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_481_cast_fp16)[name = string("input_483_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109235072))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111332288))))[name = string("encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111332416)))];
+            tensor<fp16, [1, 188, 4096]> linear_80_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16_palettized, x = input_483_cast_fp16)[name = string("linear_80_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_487_cast_fp16 = silu(x = linear_80_cast_fp16)[name = string("input_487_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111340672))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113437888))))[name = string("encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113438016)))];
+            tensor<fp16, [1, 188, 1024]> linear_81_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16_palettized, x = input_487_cast_fp16)[name = string("linear_81_cast_fp16")];
+            fp16 var_2042_to_fp16 = const()[name = string("op_2042_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2043_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_2042_to_fp16)[name = string("op_2043_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_493_cast_fp16 = add(x = input_481_cast_fp16, y = var_2043_cast_fp16)[name = string("input_493_cast_fp16")];
+            tensor<int32, [1]> input_495_axes_0 = const()[name = string("input_495_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113440128)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113442240)))];
+            tensor<fp16, [1, 188, 1024]> input_495_cast_fp16 = layer_norm(axes = input_495_axes_0, beta = encoder_module_layers_8_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_out_weight_to_fp16, x = input_493_cast_fp16)[name = string("input_495_cast_fp16")];
+            tensor<int32, [1]> input_497_axes_0 = const()[name = string("input_497_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113444352)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113446464)))];
+            tensor<fp16, [1, 188, 1024]> input_497_cast_fp16 = layer_norm(axes = input_497_axes_0, beta = encoder_module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_495_cast_fp16)[name = string("input_497_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113448576))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115545792))))[name = string("encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115545920)))];
+            tensor<fp16, [1, 188, 4096]> linear_82_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16_palettized, x = input_497_cast_fp16)[name = string("linear_82_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_501_cast_fp16 = silu(x = linear_82_cast_fp16)[name = string("input_501_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115554176))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117651392))))[name = string("encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117651520)))];
+            tensor<fp16, [1, 188, 1024]> linear_83_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16_palettized, x = input_501_cast_fp16)[name = string("linear_83_cast_fp16")];
+            fp16 var_2073_to_fp16 = const()[name = string("op_2073_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2074_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_2073_to_fp16)[name = string("op_2074_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_507_cast_fp16 = add(x = input_495_cast_fp16, y = var_2074_cast_fp16)[name = string("input_507_cast_fp16")];
+            tensor<int32, [1]> query_19_axes_0 = const()[name = string("query_19_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117653632)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117655744)))];
+            tensor<fp16, [1, 188, 1024]> query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = encoder_module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_self_att_weight_to_fp16, x = input_507_cast_fp16)[name = string("query_19_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117657856))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118182208))))[name = string("encoder_module_layers_9_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118182336)))];
+            tensor<fp16, [1, 188, 1024]> linear_84_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_q_weight_to_fp16_palettized, x = query_19_cast_fp16)[name = string("linear_84_cast_fp16")];
+            tensor<int32, [4]> var_2091 = const()[name = string("op_2091"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_55_cast_fp16 = reshape(shape = var_2091, x = linear_84_cast_fp16)[name = string("q_55_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118184448))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118708800))))[name = string("encoder_module_layers_9_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118708928)))];
+            tensor<fp16, [1, 188, 1024]> linear_85_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_k_weight_to_fp16_palettized, x = query_19_cast_fp16)[name = string("linear_85_cast_fp16")];
+            tensor<int32, [4]> var_2096 = const()[name = string("op_2096"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_37_cast_fp16 = reshape(shape = var_2096, x = linear_85_cast_fp16)[name = string("k_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118711040))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119235392))))[name = string("encoder_module_layers_9_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119235520)))];
+            tensor<fp16, [1, 188, 1024]> linear_86_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_v_weight_to_fp16_palettized, x = query_19_cast_fp16)[name = string("linear_86_cast_fp16")];
+            tensor<int32, [4]> var_2101 = const()[name = string("op_2101"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_19_cast_fp16 = reshape(shape = var_2101, x = linear_86_cast_fp16)[name = string("v_19_cast_fp16")];
+            tensor<int32, [4]> value_23_perm_0 = const()[name = string("value_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119237632)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2113_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_module_layers_9_self_attn_pos_bias_u_to_fp16)[name = string("op_2113_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119239744)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2115_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_module_layers_9_self_attn_pos_bias_v_to_fp16)[name = string("op_2115_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_19_perm_0 = const()[name = string("q_with_bias_v_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_225_transpose_x_0 = const()[name = string("x_225_transpose_x_0"), val = bool(false)];
+            bool x_225_transpose_y_0 = const()[name = string("x_225_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2117_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119241856))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119433920))))[name = string("op_2117_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_2115_cast_fp16)[name = string("transpose_249")];
+            tensor<fp16, [1, 8, 188, 375]> x_225_cast_fp16 = matmul(transpose_x = x_225_transpose_x_0, transpose_y = x_225_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = op_2117_to_fp16_palettized)[name = string("x_225_cast_fp16")];
+            tensor<int32, [8]> x_227_pad_0 = const()[name = string("x_227_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_227_mode_0 = const()[name = string("x_227_mode_0"), val = string("constant")];
+            fp16 const_178_to_fp16 = const()[name = string("const_178_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_227_cast_fp16 = pad(constant_val = const_178_to_fp16, mode = x_227_mode_0, pad = x_227_pad_0, x = x_225_cast_fp16)[name = string("x_227_cast_fp16")];
+            tensor<int32, [4]> var_2125 = const()[name = string("op_2125"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_229_cast_fp16 = reshape(shape = var_2125, x = x_227_cast_fp16)[name = string("x_229_cast_fp16")];
+            tensor<int32, [4]> var_2129_begin_0 = const()[name = string("op_2129_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2129_end_0 = const()[name = string("op_2129_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2129_end_mask_0 = const()[name = string("op_2129_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2129_cast_fp16 = slice_by_index(begin = var_2129_begin_0, end = var_2129_end_0, end_mask = var_2129_end_mask_0, x = x_229_cast_fp16)[name = string("op_2129_cast_fp16")];
+            tensor<int32, [4]> var_2130 = const()[name = string("op_2130"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_37_cast_fp16 = reshape(shape = var_2130, x = var_2129_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<int32, [4]> transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = string("transpose_247")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_114 = transpose(perm = transpose_114_perm_0, x = var_2113_cast_fp16)[name = string("transpose_248")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_39_begin_0 = const()[name = string("matrix_bd_39_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_39_end_0 = const()[name = string("matrix_bd_39_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_39_end_mask_0 = const()[name = string("matrix_bd_39_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2139_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = string("op_2139_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<fp16, [1, 8, 188, 188]> _inversed_scores_37_cast_fp16 = mul(x = var_2139_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = string("_inversed_scores_37_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_39_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_15)[name = string("scores_39_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2145_cast_fp16 = softmax(axis = var_152, x = scores_39_cast_fp16)[name = string("op_2145_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_509_cast_fp16 = select(a = var_164_to_fp16, b = var_2145_cast_fp16, cond = mask_15)[name = string("input_509_cast_fp16")];
+            bool x_231_transpose_x_0 = const()[name = string("x_231_transpose_x_0"), val = bool(false)];
+            bool x_231_transpose_y_0 = const()[name = string("x_231_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_19_cast_fp16)[name = string("transpose_246")];
+            tensor<fp16, [1, 8, 188, 128]> x_231_cast_fp16 = matmul(transpose_x = x_231_transpose_x_0, transpose_y = x_231_transpose_y_0, x = input_509_cast_fp16, y = value_23_cast_fp16)[name = string("x_231_cast_fp16")];
+            tensor<int32, [4]> var_2149_perm_0 = const()[name = string("op_2149_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2150 = const()[name = string("op_2150"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2149_cast_fp16 = transpose(perm = var_2149_perm_0, x = x_231_cast_fp16)[name = string("transpose_245")];
+            tensor<fp16, [1, 188, 1024]> input_511_cast_fp16 = reshape(shape = var_2150, x = var_2149_cast_fp16)[name = string("input_511_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119434048))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119958400))))[name = string("encoder_module_layers_9_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119958528)))];
+            tensor<fp16, [1, 188, 1024]> linear_88_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_out_weight_to_fp16_palettized, x = input_511_cast_fp16)[name = string("linear_88_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_515_cast_fp16 = add(x = input_507_cast_fp16, y = linear_88_cast_fp16)[name = string("input_515_cast_fp16")];
+            tensor<int32, [1]> x_235_axes_0 = const()[name = string("x_235_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119960640)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119962752)))];
+            tensor<fp16, [1, 188, 1024]> x_235_cast_fp16 = layer_norm(axes = x_235_axes_0, beta = encoder_module_layers_9_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_conv_weight_to_fp16, x = input_515_cast_fp16)[name = string("x_235_cast_fp16")];
+            tensor<int32, [3]> input_517_perm_0 = const()[name = string("input_517_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_519_pad_type_0 = const()[name = string("input_519_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_519_strides_0 = const()[name = string("input_519_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_519_pad_0 = const()[name = string("input_519_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_519_dilations_0 = const()[name = string("input_519_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_519_groups_0 = const()[name = string("input_519_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_9_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119964864))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121013504))))[name = string("encoder_module_layers_9_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121013632)))];
+            tensor<fp16, [1, 1024, 188]> input_517_cast_fp16 = transpose(perm = input_517_perm_0, x = x_235_cast_fp16)[name = string("transpose_244")];
+            tensor<fp16, [1, 2048, 188]> input_519_cast_fp16 = conv(bias = encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_9_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_517_cast_fp16)[name = string("input_519_cast_fp16")];
+            int32 x_237_split_num_splits_0 = const()[name = string("x_237_split_num_splits_0"), val = int32(2)];
+            int32 x_237_split_axis_0 = const()[name = string("x_237_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_237_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_237_split_cast_fp16_1 = split(axis = x_237_split_axis_0, num_splits = x_237_split_num_splits_0, x = input_519_cast_fp16)[name = string("x_237_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_237_split_1_sigmoid_cast_fp16 = sigmoid(x = x_237_split_cast_fp16_1)[name = string("x_237_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_237_cast_fp16 = mul(x = x_237_split_cast_fp16_0, y = x_237_split_1_sigmoid_cast_fp16)[name = string("x_237_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_521_cast_fp16 = select(a = var_164_to_fp16, b = x_237_cast_fp16, cond = var_608)[name = string("input_521_cast_fp16")];
+            tensor<int32, [6]> input_523_pad_0 = const()[name = string("input_523_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_523_mode_0 = const()[name = string("input_523_mode_0"), val = string("constant")];
+            fp16 const_181_to_fp16 = const()[name = string("const_181_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_523_cast_fp16 = pad(constant_val = const_181_to_fp16, mode = input_523_mode_0, pad = input_523_pad_0, x = input_521_cast_fp16)[name = string("input_523_cast_fp16")];
+            string input_525_pad_type_0 = const()[name = string("input_525_pad_type_0"), val = string("valid")];
+            int32 input_525_groups_0 = const()[name = string("input_525_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_525_strides_0 = const()[name = string("input_525_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_525_pad_0 = const()[name = string("input_525_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_525_dilations_0 = const()[name = string("input_525_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_340_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121017792))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121022464))))[name = string("const_340_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_341_to_fp16 = const()[name = string("const_341_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121022592)))];
+            tensor<fp16, [1, 1024, 188]> input_527_cast_fp16 = conv(bias = const_341_to_fp16, dilations = input_525_dilations_0, groups = input_525_groups_0, pad = input_525_pad_0, pad_type = input_525_pad_type_0, strides = input_525_strides_0, weight = const_340_to_fp16_palettized, x = input_523_cast_fp16)[name = string("input_527_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_529_cast_fp16 = silu(x = input_527_cast_fp16)[name = string("input_529_cast_fp16")];
+            string x_239_pad_type_0 = const()[name = string("x_239_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_239_strides_0 = const()[name = string("x_239_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_239_pad_0 = const()[name = string("x_239_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_239_dilations_0 = const()[name = string("x_239_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_239_groups_0 = const()[name = string("x_239_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121024704))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121549056))))[name = string("encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121549184)))];
+            tensor<fp16, [1, 1024, 188]> x_239_cast_fp16 = conv(bias = encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16, dilations = x_239_dilations_0, groups = x_239_groups_0, pad = x_239_pad_0, pad_type = x_239_pad_type_0, strides = x_239_strides_0, weight = encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_529_cast_fp16)[name = string("x_239_cast_fp16")];
+            tensor<int32, [3]> input_531_perm_0 = const()[name = string("input_531_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_531_cast_fp16 = transpose(perm = input_531_perm_0, x = x_239_cast_fp16)[name = string("transpose_243")];
+            tensor<fp16, [1, 188, 1024]> input_533_cast_fp16 = add(x = input_515_cast_fp16, y = input_531_cast_fp16)[name = string("input_533_cast_fp16")];
+            tensor<int32, [1]> input_535_axes_0 = const()[name = string("input_535_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121551296)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121553408)))];
+            tensor<fp16, [1, 188, 1024]> input_535_cast_fp16 = layer_norm(axes = input_535_axes_0, beta = encoder_module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_533_cast_fp16)[name = string("input_535_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121555520))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123652736))))[name = string("encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123652864)))];
+            tensor<fp16, [1, 188, 4096]> linear_89_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16_palettized, x = input_535_cast_fp16)[name = string("linear_89_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_539_cast_fp16 = silu(x = linear_89_cast_fp16)[name = string("input_539_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123661120))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125758336))))[name = string("encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125758464)))];
+            tensor<fp16, [1, 188, 1024]> linear_90_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16_palettized, x = input_539_cast_fp16)[name = string("linear_90_cast_fp16")];
+            fp16 var_2216_to_fp16 = const()[name = string("op_2216_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2217_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_2216_to_fp16)[name = string("op_2217_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_545_cast_fp16 = add(x = input_533_cast_fp16, y = var_2217_cast_fp16)[name = string("input_545_cast_fp16")];
+            tensor<int32, [1]> input_547_axes_0 = const()[name = string("input_547_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125760576)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125762688)))];
+            tensor<fp16, [1, 188, 1024]> input_547_cast_fp16 = layer_norm(axes = input_547_axes_0, beta = encoder_module_layers_9_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_out_weight_to_fp16, x = input_545_cast_fp16)[name = string("input_547_cast_fp16")];
+            tensor<int32, [1]> input_549_axes_0 = const()[name = string("input_549_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125764800)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125766912)))];
+            tensor<fp16, [1, 188, 1024]> input_549_cast_fp16 = layer_norm(axes = input_549_axes_0, beta = encoder_module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_547_cast_fp16)[name = string("input_549_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125769024))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127866240))))[name = string("encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127866368)))];
+            tensor<fp16, [1, 188, 4096]> linear_91_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16_palettized, x = input_549_cast_fp16)[name = string("linear_91_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_553_cast_fp16 = silu(x = linear_91_cast_fp16)[name = string("input_553_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127874624))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129971840))))[name = string("encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129971968)))];
+            tensor<fp16, [1, 188, 1024]> linear_92_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16_palettized, x = input_553_cast_fp16)[name = string("linear_92_cast_fp16")];
+            fp16 var_2247_to_fp16 = const()[name = string("op_2247_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2248_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_2247_to_fp16)[name = string("op_2248_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_559_cast_fp16 = add(x = input_547_cast_fp16, y = var_2248_cast_fp16)[name = string("input_559_cast_fp16")];
+            tensor<int32, [1]> query_21_axes_0 = const()[name = string("query_21_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129974080)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129976192)))];
+            tensor<fp16, [1, 188, 1024]> query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = encoder_module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_self_att_weight_to_fp16, x = input_559_cast_fp16)[name = string("query_21_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129978304))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130502656))))[name = string("encoder_module_layers_10_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130502784)))];
+            tensor<fp16, [1, 188, 1024]> linear_93_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_q_weight_to_fp16_palettized, x = query_21_cast_fp16)[name = string("linear_93_cast_fp16")];
+            tensor<int32, [4]> var_2265 = const()[name = string("op_2265"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_61_cast_fp16 = reshape(shape = var_2265, x = linear_93_cast_fp16)[name = string("q_61_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130504896))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131029248))))[name = string("encoder_module_layers_10_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131029376)))];
+            tensor<fp16, [1, 188, 1024]> linear_94_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_k_weight_to_fp16_palettized, x = query_21_cast_fp16)[name = string("linear_94_cast_fp16")];
+            tensor<int32, [4]> var_2270 = const()[name = string("op_2270"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_41_cast_fp16 = reshape(shape = var_2270, x = linear_94_cast_fp16)[name = string("k_41_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131031488))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131555840))))[name = string("encoder_module_layers_10_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131555968)))];
+            tensor<fp16, [1, 188, 1024]> linear_95_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_v_weight_to_fp16_palettized, x = query_21_cast_fp16)[name = string("linear_95_cast_fp16")];
+            tensor<int32, [4]> var_2275 = const()[name = string("op_2275"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_21_cast_fp16 = reshape(shape = var_2275, x = linear_95_cast_fp16)[name = string("v_21_cast_fp16")];
+            tensor<int32, [4]> value_25_perm_0 = const()[name = string("value_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131558080)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2287_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_module_layers_10_self_attn_pos_bias_u_to_fp16)[name = string("op_2287_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131560192)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2289_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_module_layers_10_self_attn_pos_bias_v_to_fp16)[name = string("op_2289_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_21_perm_0 = const()[name = string("q_with_bias_v_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            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<fp16, [1, 8, 128, 375]> op_2291_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131562304))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131754368))))[name = string("op_2291_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2289_cast_fp16)[name = string("transpose_242")];
+            tensor<fp16, [1, 8, 188, 375]> x_247_cast_fp16 = matmul(transpose_x = x_247_transpose_x_0, transpose_y = x_247_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = op_2291_to_fp16_palettized)[name = string("x_247_cast_fp16")];
+            tensor<int32, [8]> x_249_pad_0 = const()[name = string("x_249_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_249_mode_0 = const()[name = string("x_249_mode_0"), val = string("constant")];
+            fp16 const_188_to_fp16 = const()[name = string("const_188_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_249_cast_fp16 = pad(constant_val = const_188_to_fp16, mode = x_249_mode_0, pad = x_249_pad_0, x = x_247_cast_fp16)[name = string("x_249_cast_fp16")];
+            tensor<int32, [4]> var_2299 = const()[name = string("op_2299"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_251_cast_fp16 = reshape(shape = var_2299, x = x_249_cast_fp16)[name = string("x_251_cast_fp16")];
+            tensor<int32, [4]> var_2303_begin_0 = const()[name = string("op_2303_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2303_end_0 = const()[name = string("op_2303_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2303_end_mask_0 = const()[name = string("op_2303_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2303_cast_fp16 = slice_by_index(begin = var_2303_begin_0, end = var_2303_end_0, end_mask = var_2303_end_mask_0, x = x_251_cast_fp16)[name = string("op_2303_cast_fp16")];
+            tensor<int32, [4]> var_2304 = const()[name = string("op_2304"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_41_cast_fp16 = reshape(shape = var_2304, x = var_2303_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<int32, [4]> transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = string("transpose_240")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_116 = transpose(perm = transpose_116_perm_0, x = var_2287_cast_fp16)[name = string("transpose_241")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_43_begin_0 = const()[name = string("matrix_bd_43_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_43_end_0 = const()[name = string("matrix_bd_43_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_43_end_mask_0 = const()[name = string("matrix_bd_43_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2313_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = string("op_2313_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<fp16, [1, 8, 188, 188]> _inversed_scores_41_cast_fp16 = mul(x = var_2313_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = string("_inversed_scores_41_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_43_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_15)[name = string("scores_43_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2319_cast_fp16 = softmax(axis = var_152, x = scores_43_cast_fp16)[name = string("op_2319_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_561_cast_fp16 = select(a = var_164_to_fp16, b = var_2319_cast_fp16, cond = mask_15)[name = string("input_561_cast_fp16")];
+            bool x_253_transpose_x_0 = const()[name = string("x_253_transpose_x_0"), val = bool(false)];
+            bool x_253_transpose_y_0 = const()[name = string("x_253_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_21_cast_fp16)[name = string("transpose_239")];
+            tensor<fp16, [1, 8, 188, 128]> x_253_cast_fp16 = matmul(transpose_x = x_253_transpose_x_0, transpose_y = x_253_transpose_y_0, x = input_561_cast_fp16, y = value_25_cast_fp16)[name = string("x_253_cast_fp16")];
+            tensor<int32, [4]> var_2323_perm_0 = const()[name = string("op_2323_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2324 = const()[name = string("op_2324"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2323_cast_fp16 = transpose(perm = var_2323_perm_0, x = x_253_cast_fp16)[name = string("transpose_238")];
+            tensor<fp16, [1, 188, 1024]> input_563_cast_fp16 = reshape(shape = var_2324, x = var_2323_cast_fp16)[name = string("input_563_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131754496))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132278848))))[name = string("encoder_module_layers_10_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132278976)))];
+            tensor<fp16, [1, 188, 1024]> linear_97_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_out_weight_to_fp16_palettized, x = input_563_cast_fp16)[name = string("linear_97_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_567_cast_fp16 = add(x = input_559_cast_fp16, y = linear_97_cast_fp16)[name = string("input_567_cast_fp16")];
+            tensor<int32, [1]> x_257_axes_0 = const()[name = string("x_257_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132281088)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132283200)))];
+            tensor<fp16, [1, 188, 1024]> x_257_cast_fp16 = layer_norm(axes = x_257_axes_0, beta = encoder_module_layers_10_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_conv_weight_to_fp16, x = input_567_cast_fp16)[name = string("x_257_cast_fp16")];
+            tensor<int32, [3]> input_569_perm_0 = const()[name = string("input_569_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_571_pad_type_0 = const()[name = string("input_571_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_571_strides_0 = const()[name = string("input_571_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_571_pad_0 = const()[name = string("input_571_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_571_dilations_0 = const()[name = string("input_571_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_571_groups_0 = const()[name = string("input_571_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_10_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132285312))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133333952))))[name = string("encoder_module_layers_10_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133334080)))];
+            tensor<fp16, [1, 1024, 188]> input_569_cast_fp16 = transpose(perm = input_569_perm_0, x = x_257_cast_fp16)[name = string("transpose_237")];
+            tensor<fp16, [1, 2048, 188]> input_571_cast_fp16 = conv(bias = encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_10_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_569_cast_fp16)[name = string("input_571_cast_fp16")];
+            int32 x_259_split_num_splits_0 = const()[name = string("x_259_split_num_splits_0"), val = int32(2)];
+            int32 x_259_split_axis_0 = const()[name = string("x_259_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_259_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_259_split_cast_fp16_1 = split(axis = x_259_split_axis_0, num_splits = x_259_split_num_splits_0, x = input_571_cast_fp16)[name = string("x_259_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_259_split_1_sigmoid_cast_fp16 = sigmoid(x = x_259_split_cast_fp16_1)[name = string("x_259_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_259_cast_fp16 = mul(x = x_259_split_cast_fp16_0, y = x_259_split_1_sigmoid_cast_fp16)[name = string("x_259_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_573_cast_fp16 = select(a = var_164_to_fp16, b = x_259_cast_fp16, cond = var_608)[name = string("input_573_cast_fp16")];
+            tensor<int32, [6]> input_575_pad_0 = const()[name = string("input_575_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_575_mode_0 = const()[name = string("input_575_mode_0"), val = string("constant")];
+            fp16 const_191_to_fp16 = const()[name = string("const_191_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_575_cast_fp16 = pad(constant_val = const_191_to_fp16, mode = input_575_mode_0, pad = input_575_pad_0, x = input_573_cast_fp16)[name = string("input_575_cast_fp16")];
+            string input_577_pad_type_0 = const()[name = string("input_577_pad_type_0"), val = string("valid")];
+            int32 input_577_groups_0 = const()[name = string("input_577_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_577_strides_0 = const()[name = string("input_577_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_577_pad_0 = const()[name = string("input_577_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_577_dilations_0 = const()[name = string("input_577_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_342_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133338240))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133342912))))[name = string("const_342_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_343_to_fp16 = const()[name = string("const_343_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133343040)))];
+            tensor<fp16, [1, 1024, 188]> input_579_cast_fp16 = conv(bias = const_343_to_fp16, dilations = input_577_dilations_0, groups = input_577_groups_0, pad = input_577_pad_0, pad_type = input_577_pad_type_0, strides = input_577_strides_0, weight = const_342_to_fp16_palettized, x = input_575_cast_fp16)[name = string("input_579_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_581_cast_fp16 = silu(x = input_579_cast_fp16)[name = string("input_581_cast_fp16")];
+            string x_261_pad_type_0 = const()[name = string("x_261_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_261_strides_0 = const()[name = string("x_261_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_261_pad_0 = const()[name = string("x_261_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_261_dilations_0 = const()[name = string("x_261_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_261_groups_0 = const()[name = string("x_261_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133345152))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133869504))))[name = string("encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133869632)))];
+            tensor<fp16, [1, 1024, 188]> x_261_cast_fp16 = conv(bias = encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16, dilations = x_261_dilations_0, groups = x_261_groups_0, pad = x_261_pad_0, pad_type = x_261_pad_type_0, strides = x_261_strides_0, weight = encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_581_cast_fp16)[name = string("x_261_cast_fp16")];
+            tensor<int32, [3]> input_583_perm_0 = const()[name = string("input_583_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_583_cast_fp16 = transpose(perm = input_583_perm_0, x = x_261_cast_fp16)[name = string("transpose_236")];
+            tensor<fp16, [1, 188, 1024]> input_585_cast_fp16 = add(x = input_567_cast_fp16, y = input_583_cast_fp16)[name = string("input_585_cast_fp16")];
+            tensor<int32, [1]> input_587_axes_0 = const()[name = string("input_587_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133871744)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133873856)))];
+            tensor<fp16, [1, 188, 1024]> input_587_cast_fp16 = layer_norm(axes = input_587_axes_0, beta = encoder_module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_585_cast_fp16)[name = string("input_587_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133875968))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135973184))))[name = string("encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135973312)))];
+            tensor<fp16, [1, 188, 4096]> linear_98_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16_palettized, x = input_587_cast_fp16)[name = string("linear_98_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_591_cast_fp16 = silu(x = linear_98_cast_fp16)[name = string("input_591_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135981568))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138078784))))[name = string("encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138078912)))];
+            tensor<fp16, [1, 188, 1024]> linear_99_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16_palettized, x = input_591_cast_fp16)[name = string("linear_99_cast_fp16")];
+            fp16 var_2390_to_fp16 = const()[name = string("op_2390_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2391_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2390_to_fp16)[name = string("op_2391_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_597_cast_fp16 = add(x = input_585_cast_fp16, y = var_2391_cast_fp16)[name = string("input_597_cast_fp16")];
+            tensor<int32, [1]> input_599_axes_0 = const()[name = string("input_599_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138081024)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138083136)))];
+            tensor<fp16, [1, 188, 1024]> input_599_cast_fp16 = layer_norm(axes = input_599_axes_0, beta = encoder_module_layers_10_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_out_weight_to_fp16, x = input_597_cast_fp16)[name = string("input_599_cast_fp16")];
+            tensor<int32, [1]> input_601_axes_0 = const()[name = string("input_601_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138085248)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138087360)))];
+            tensor<fp16, [1, 188, 1024]> input_601_cast_fp16 = layer_norm(axes = input_601_axes_0, beta = encoder_module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_599_cast_fp16)[name = string("input_601_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138089472))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140186688))))[name = string("encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140186816)))];
+            tensor<fp16, [1, 188, 4096]> linear_100_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16_palettized, x = input_601_cast_fp16)[name = string("linear_100_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_605_cast_fp16 = silu(x = linear_100_cast_fp16)[name = string("input_605_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140195072))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142292288))))[name = string("encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142292416)))];
+            tensor<fp16, [1, 188, 1024]> linear_101_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16_palettized, x = input_605_cast_fp16)[name = string("linear_101_cast_fp16")];
+            fp16 var_2421_to_fp16 = const()[name = string("op_2421_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2422_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2421_to_fp16)[name = string("op_2422_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_611_cast_fp16 = add(x = input_599_cast_fp16, y = var_2422_cast_fp16)[name = string("input_611_cast_fp16")];
+            tensor<int32, [1]> query_23_axes_0 = const()[name = string("query_23_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142294528)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142296640)))];
+            tensor<fp16, [1, 188, 1024]> query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = encoder_module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_self_att_weight_to_fp16, x = input_611_cast_fp16)[name = string("query_23_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142298752))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142823104))))[name = string("encoder_module_layers_11_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142823232)))];
+            tensor<fp16, [1, 188, 1024]> linear_102_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_q_weight_to_fp16_palettized, x = query_23_cast_fp16)[name = string("linear_102_cast_fp16")];
+            tensor<int32, [4]> var_2439 = const()[name = string("op_2439"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_67_cast_fp16 = reshape(shape = var_2439, x = linear_102_cast_fp16)[name = string("q_67_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142825344))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143349696))))[name = string("encoder_module_layers_11_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143349824)))];
+            tensor<fp16, [1, 188, 1024]> linear_103_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_k_weight_to_fp16_palettized, x = query_23_cast_fp16)[name = string("linear_103_cast_fp16")];
+            tensor<int32, [4]> var_2444 = const()[name = string("op_2444"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_45_cast_fp16 = reshape(shape = var_2444, x = linear_103_cast_fp16)[name = string("k_45_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143351936))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143876288))))[name = string("encoder_module_layers_11_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143876416)))];
+            tensor<fp16, [1, 188, 1024]> linear_104_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_v_weight_to_fp16_palettized, x = query_23_cast_fp16)[name = string("linear_104_cast_fp16")];
+            tensor<int32, [4]> var_2449 = const()[name = string("op_2449"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_23_cast_fp16 = reshape(shape = var_2449, x = linear_104_cast_fp16)[name = string("v_23_cast_fp16")];
+            tensor<int32, [4]> value_27_perm_0 = const()[name = string("value_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143878528)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2461_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_module_layers_11_self_attn_pos_bias_u_to_fp16)[name = string("op_2461_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143880640)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2463_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_module_layers_11_self_attn_pos_bias_v_to_fp16)[name = string("op_2463_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_23_perm_0 = const()[name = string("q_with_bias_v_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_269_transpose_x_0 = const()[name = string("x_269_transpose_x_0"), val = bool(false)];
+            bool x_269_transpose_y_0 = const()[name = string("x_269_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2465_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143882752))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144074816))))[name = string("op_2465_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2463_cast_fp16)[name = string("transpose_235")];
+            tensor<fp16, [1, 8, 188, 375]> x_269_cast_fp16 = matmul(transpose_x = x_269_transpose_x_0, transpose_y = x_269_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = op_2465_to_fp16_palettized)[name = string("x_269_cast_fp16")];
+            tensor<int32, [8]> x_271_pad_0 = const()[name = string("x_271_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_271_mode_0 = const()[name = string("x_271_mode_0"), val = string("constant")];
+            fp16 const_198_to_fp16 = const()[name = string("const_198_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_271_cast_fp16 = pad(constant_val = const_198_to_fp16, mode = x_271_mode_0, pad = x_271_pad_0, x = x_269_cast_fp16)[name = string("x_271_cast_fp16")];
+            tensor<int32, [4]> var_2473 = const()[name = string("op_2473"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_273_cast_fp16 = reshape(shape = var_2473, x = x_271_cast_fp16)[name = string("x_273_cast_fp16")];
+            tensor<int32, [4]> var_2477_begin_0 = const()[name = string("op_2477_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2477_end_0 = const()[name = string("op_2477_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2477_end_mask_0 = const()[name = string("op_2477_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2477_cast_fp16 = slice_by_index(begin = var_2477_begin_0, end = var_2477_end_0, end_mask = var_2477_end_mask_0, x = x_273_cast_fp16)[name = string("op_2477_cast_fp16")];
+            tensor<int32, [4]> var_2478 = const()[name = string("op_2478"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_45_cast_fp16 = reshape(shape = var_2478, x = var_2477_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<int32, [4]> transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = string("transpose_233")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2461_cast_fp16)[name = string("transpose_234")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_47_begin_0 = const()[name = string("matrix_bd_47_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_47_end_0 = const()[name = string("matrix_bd_47_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_47_end_mask_0 = const()[name = string("matrix_bd_47_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2487_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = string("op_2487_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<fp16, [1, 8, 188, 188]> _inversed_scores_45_cast_fp16 = mul(x = var_2487_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = string("_inversed_scores_45_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_47_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_15)[name = string("scores_47_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2493_cast_fp16 = softmax(axis = var_152, x = scores_47_cast_fp16)[name = string("op_2493_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_613_cast_fp16 = select(a = var_164_to_fp16, b = var_2493_cast_fp16, cond = mask_15)[name = string("input_613_cast_fp16")];
+            bool x_275_transpose_x_0 = const()[name = string("x_275_transpose_x_0"), val = bool(false)];
+            bool x_275_transpose_y_0 = const()[name = string("x_275_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_23_cast_fp16)[name = string("transpose_232")];
+            tensor<fp16, [1, 8, 188, 128]> x_275_cast_fp16 = matmul(transpose_x = x_275_transpose_x_0, transpose_y = x_275_transpose_y_0, x = input_613_cast_fp16, y = value_27_cast_fp16)[name = string("x_275_cast_fp16")];
+            tensor<int32, [4]> var_2497_perm_0 = const()[name = string("op_2497_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2498 = const()[name = string("op_2498"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2497_cast_fp16 = transpose(perm = var_2497_perm_0, x = x_275_cast_fp16)[name = string("transpose_231")];
+            tensor<fp16, [1, 188, 1024]> input_615_cast_fp16 = reshape(shape = var_2498, x = var_2497_cast_fp16)[name = string("input_615_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144074944))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144599296))))[name = string("encoder_module_layers_11_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144599424)))];
+            tensor<fp16, [1, 188, 1024]> linear_106_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_out_weight_to_fp16_palettized, x = input_615_cast_fp16)[name = string("linear_106_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_619_cast_fp16 = add(x = input_611_cast_fp16, y = linear_106_cast_fp16)[name = string("input_619_cast_fp16")];
+            tensor<int32, [1]> x_279_axes_0 = const()[name = string("x_279_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144601536)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144603648)))];
+            tensor<fp16, [1, 188, 1024]> x_279_cast_fp16 = layer_norm(axes = x_279_axes_0, beta = encoder_module_layers_11_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_conv_weight_to_fp16, x = input_619_cast_fp16)[name = string("x_279_cast_fp16")];
+            tensor<int32, [3]> input_621_perm_0 = const()[name = string("input_621_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_623_pad_type_0 = const()[name = string("input_623_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_623_strides_0 = const()[name = string("input_623_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_623_pad_0 = const()[name = string("input_623_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_623_dilations_0 = const()[name = string("input_623_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_623_groups_0 = const()[name = string("input_623_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_11_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144605760))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145654400))))[name = string("encoder_module_layers_11_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145654528)))];
+            tensor<fp16, [1, 1024, 188]> input_621_cast_fp16 = transpose(perm = input_621_perm_0, x = x_279_cast_fp16)[name = string("transpose_230")];
+            tensor<fp16, [1, 2048, 188]> input_623_cast_fp16 = conv(bias = encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_11_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_621_cast_fp16)[name = string("input_623_cast_fp16")];
+            int32 x_281_split_num_splits_0 = const()[name = string("x_281_split_num_splits_0"), val = int32(2)];
+            int32 x_281_split_axis_0 = const()[name = string("x_281_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_281_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_281_split_cast_fp16_1 = split(axis = x_281_split_axis_0, num_splits = x_281_split_num_splits_0, x = input_623_cast_fp16)[name = string("x_281_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_281_split_1_sigmoid_cast_fp16 = sigmoid(x = x_281_split_cast_fp16_1)[name = string("x_281_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_281_cast_fp16 = mul(x = x_281_split_cast_fp16_0, y = x_281_split_1_sigmoid_cast_fp16)[name = string("x_281_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_625_cast_fp16 = select(a = var_164_to_fp16, b = x_281_cast_fp16, cond = var_608)[name = string("input_625_cast_fp16")];
+            tensor<int32, [6]> input_627_pad_0 = const()[name = string("input_627_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_627_mode_0 = const()[name = string("input_627_mode_0"), val = string("constant")];
+            fp16 const_201_to_fp16 = const()[name = string("const_201_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_627_cast_fp16 = pad(constant_val = const_201_to_fp16, mode = input_627_mode_0, pad = input_627_pad_0, x = input_625_cast_fp16)[name = string("input_627_cast_fp16")];
+            string input_629_pad_type_0 = const()[name = string("input_629_pad_type_0"), val = string("valid")];
+            int32 input_629_groups_0 = const()[name = string("input_629_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_629_strides_0 = const()[name = string("input_629_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_629_pad_0 = const()[name = string("input_629_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_629_dilations_0 = const()[name = string("input_629_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_344_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145658688))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145663360))))[name = string("const_344_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_345_to_fp16 = const()[name = string("const_345_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145663488)))];
+            tensor<fp16, [1, 1024, 188]> input_631_cast_fp16 = conv(bias = const_345_to_fp16, dilations = input_629_dilations_0, groups = input_629_groups_0, pad = input_629_pad_0, pad_type = input_629_pad_type_0, strides = input_629_strides_0, weight = const_344_to_fp16_palettized, x = input_627_cast_fp16)[name = string("input_631_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_633_cast_fp16 = silu(x = input_631_cast_fp16)[name = string("input_633_cast_fp16")];
+            string x_283_pad_type_0 = const()[name = string("x_283_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_283_strides_0 = const()[name = string("x_283_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_283_pad_0 = const()[name = string("x_283_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_283_dilations_0 = const()[name = string("x_283_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_283_groups_0 = const()[name = string("x_283_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145665600))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146189952))))[name = string("encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146190080)))];
+            tensor<fp16, [1, 1024, 188]> x_283_cast_fp16 = conv(bias = encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16, dilations = x_283_dilations_0, groups = x_283_groups_0, pad = x_283_pad_0, pad_type = x_283_pad_type_0, strides = x_283_strides_0, weight = encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_633_cast_fp16)[name = string("x_283_cast_fp16")];
+            tensor<int32, [3]> input_635_perm_0 = const()[name = string("input_635_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_635_cast_fp16 = transpose(perm = input_635_perm_0, x = x_283_cast_fp16)[name = string("transpose_229")];
+            tensor<fp16, [1, 188, 1024]> input_637_cast_fp16 = add(x = input_619_cast_fp16, y = input_635_cast_fp16)[name = string("input_637_cast_fp16")];
+            tensor<int32, [1]> input_639_axes_0 = const()[name = string("input_639_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146192192)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146194304)))];
+            tensor<fp16, [1, 188, 1024]> input_639_cast_fp16 = layer_norm(axes = input_639_axes_0, beta = encoder_module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_637_cast_fp16)[name = string("input_639_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146196416))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148293632))))[name = string("encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148293760)))];
+            tensor<fp16, [1, 188, 4096]> linear_107_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16_palettized, x = input_639_cast_fp16)[name = string("linear_107_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_643_cast_fp16 = silu(x = linear_107_cast_fp16)[name = string("input_643_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148302016))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150399232))))[name = string("encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150399360)))];
+            tensor<fp16, [1, 188, 1024]> linear_108_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16_palettized, x = input_643_cast_fp16)[name = string("linear_108_cast_fp16")];
+            fp16 var_2564_to_fp16 = const()[name = string("op_2564_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2565_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2564_to_fp16)[name = string("op_2565_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_649_cast_fp16 = add(x = input_637_cast_fp16, y = var_2565_cast_fp16)[name = string("input_649_cast_fp16")];
+            tensor<int32, [1]> input_651_axes_0 = const()[name = string("input_651_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150401472)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150403584)))];
+            tensor<fp16, [1, 188, 1024]> input_651_cast_fp16 = layer_norm(axes = input_651_axes_0, beta = encoder_module_layers_11_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_out_weight_to_fp16, x = input_649_cast_fp16)[name = string("input_651_cast_fp16")];
+            tensor<int32, [1]> input_653_axes_0 = const()[name = string("input_653_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150405696)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150407808)))];
+            tensor<fp16, [1, 188, 1024]> input_653_cast_fp16 = layer_norm(axes = input_653_axes_0, beta = encoder_module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_651_cast_fp16)[name = string("input_653_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150409920))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152507136))))[name = string("encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152507264)))];
+            tensor<fp16, [1, 188, 4096]> linear_109_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16_palettized, x = input_653_cast_fp16)[name = string("linear_109_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_657_cast_fp16 = silu(x = linear_109_cast_fp16)[name = string("input_657_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152515520))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154612736))))[name = string("encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154612864)))];
+            tensor<fp16, [1, 188, 1024]> linear_110_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16_palettized, x = input_657_cast_fp16)[name = string("linear_110_cast_fp16")];
+            fp16 var_2595_to_fp16 = const()[name = string("op_2595_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2596_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2595_to_fp16)[name = string("op_2596_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_663_cast_fp16 = add(x = input_651_cast_fp16, y = var_2596_cast_fp16)[name = string("input_663_cast_fp16")];
+            tensor<int32, [1]> query_25_axes_0 = const()[name = string("query_25_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154614976)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154617088)))];
+            tensor<fp16, [1, 188, 1024]> query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = encoder_module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_self_att_weight_to_fp16, x = input_663_cast_fp16)[name = string("query_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154619200))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155143552))))[name = string("encoder_module_layers_12_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155143680)))];
+            tensor<fp16, [1, 188, 1024]> linear_111_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_q_weight_to_fp16_palettized, x = query_25_cast_fp16)[name = string("linear_111_cast_fp16")];
+            tensor<int32, [4]> var_2613 = const()[name = string("op_2613"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_73_cast_fp16 = reshape(shape = var_2613, x = linear_111_cast_fp16)[name = string("q_73_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155145792))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155670144))))[name = string("encoder_module_layers_12_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155670272)))];
+            tensor<fp16, [1, 188, 1024]> linear_112_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_k_weight_to_fp16_palettized, x = query_25_cast_fp16)[name = string("linear_112_cast_fp16")];
+            tensor<int32, [4]> var_2618 = const()[name = string("op_2618"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_49_cast_fp16 = reshape(shape = var_2618, x = linear_112_cast_fp16)[name = string("k_49_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155672384))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156196736))))[name = string("encoder_module_layers_12_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156196864)))];
+            tensor<fp16, [1, 188, 1024]> linear_113_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_v_weight_to_fp16_palettized, x = query_25_cast_fp16)[name = string("linear_113_cast_fp16")];
+            tensor<int32, [4]> var_2623 = const()[name = string("op_2623"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_25_cast_fp16 = reshape(shape = var_2623, x = linear_113_cast_fp16)[name = string("v_25_cast_fp16")];
+            tensor<int32, [4]> value_29_perm_0 = const()[name = string("value_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156198976)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2635_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_module_layers_12_self_attn_pos_bias_u_to_fp16)[name = string("op_2635_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156201088)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2637_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_module_layers_12_self_attn_pos_bias_v_to_fp16)[name = string("op_2637_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_25_perm_0 = const()[name = string("q_with_bias_v_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_291_transpose_x_0 = const()[name = string("x_291_transpose_x_0"), val = bool(false)];
+            bool x_291_transpose_y_0 = const()[name = string("x_291_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2639_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156203200))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156395264))))[name = string("op_2639_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2637_cast_fp16)[name = string("transpose_228")];
+            tensor<fp16, [1, 8, 188, 375]> x_291_cast_fp16 = matmul(transpose_x = x_291_transpose_x_0, transpose_y = x_291_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = op_2639_to_fp16_palettized)[name = string("x_291_cast_fp16")];
+            tensor<int32, [8]> x_293_pad_0 = const()[name = string("x_293_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_293_mode_0 = const()[name = string("x_293_mode_0"), val = string("constant")];
+            fp16 const_208_to_fp16 = const()[name = string("const_208_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_293_cast_fp16 = pad(constant_val = const_208_to_fp16, mode = x_293_mode_0, pad = x_293_pad_0, x = x_291_cast_fp16)[name = string("x_293_cast_fp16")];
+            tensor<int32, [4]> var_2647 = const()[name = string("op_2647"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_295_cast_fp16 = reshape(shape = var_2647, x = x_293_cast_fp16)[name = string("x_295_cast_fp16")];
+            tensor<int32, [4]> var_2651_begin_0 = const()[name = string("op_2651_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2651_end_0 = const()[name = string("op_2651_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2651_end_mask_0 = const()[name = string("op_2651_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2651_cast_fp16 = slice_by_index(begin = var_2651_begin_0, end = var_2651_end_0, end_mask = var_2651_end_mask_0, x = x_295_cast_fp16)[name = string("op_2651_cast_fp16")];
+            tensor<int32, [4]> var_2652 = const()[name = string("op_2652"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_49_cast_fp16 = reshape(shape = var_2652, x = var_2651_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<int32, [4]> transpose_120_perm_0 = const()[name = string("transpose_120_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_121_perm_0 = const()[name = string("transpose_121_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = string("transpose_226")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_120 = transpose(perm = transpose_120_perm_0, x = var_2635_cast_fp16)[name = string("transpose_227")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_51_begin_0 = const()[name = string("matrix_bd_51_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_51_end_0 = const()[name = string("matrix_bd_51_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_51_end_mask_0 = const()[name = string("matrix_bd_51_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2661_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = string("op_2661_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<fp16, [1, 8, 188, 188]> _inversed_scores_49_cast_fp16 = mul(x = var_2661_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = string("_inversed_scores_49_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_51_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_15)[name = string("scores_51_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2667_cast_fp16 = softmax(axis = var_152, x = scores_51_cast_fp16)[name = string("op_2667_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_665_cast_fp16 = select(a = var_164_to_fp16, b = var_2667_cast_fp16, cond = mask_15)[name = string("input_665_cast_fp16")];
+            bool x_297_transpose_x_0 = const()[name = string("x_297_transpose_x_0"), val = bool(false)];
+            bool x_297_transpose_y_0 = const()[name = string("x_297_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_25_cast_fp16)[name = string("transpose_225")];
+            tensor<fp16, [1, 8, 188, 128]> x_297_cast_fp16 = matmul(transpose_x = x_297_transpose_x_0, transpose_y = x_297_transpose_y_0, x = input_665_cast_fp16, y = value_29_cast_fp16)[name = string("x_297_cast_fp16")];
+            tensor<int32, [4]> var_2671_perm_0 = const()[name = string("op_2671_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2672 = const()[name = string("op_2672"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2671_cast_fp16 = transpose(perm = var_2671_perm_0, x = x_297_cast_fp16)[name = string("transpose_224")];
+            tensor<fp16, [1, 188, 1024]> input_667_cast_fp16 = reshape(shape = var_2672, x = var_2671_cast_fp16)[name = string("input_667_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156395392))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156919744))))[name = string("encoder_module_layers_12_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156919872)))];
+            tensor<fp16, [1, 188, 1024]> linear_115_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_out_weight_to_fp16_palettized, x = input_667_cast_fp16)[name = string("linear_115_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_671_cast_fp16 = add(x = input_663_cast_fp16, y = linear_115_cast_fp16)[name = string("input_671_cast_fp16")];
+            tensor<int32, [1]> x_301_axes_0 = const()[name = string("x_301_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156921984)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156924096)))];
+            tensor<fp16, [1, 188, 1024]> x_301_cast_fp16 = layer_norm(axes = x_301_axes_0, beta = encoder_module_layers_12_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_conv_weight_to_fp16, x = input_671_cast_fp16)[name = string("x_301_cast_fp16")];
+            tensor<int32, [3]> input_673_perm_0 = const()[name = string("input_673_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_675_pad_type_0 = const()[name = string("input_675_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_675_strides_0 = const()[name = string("input_675_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_675_pad_0 = const()[name = string("input_675_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_675_dilations_0 = const()[name = string("input_675_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_675_groups_0 = const()[name = string("input_675_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_12_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156926208))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157974848))))[name = string("encoder_module_layers_12_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157974976)))];
+            tensor<fp16, [1, 1024, 188]> input_673_cast_fp16 = transpose(perm = input_673_perm_0, x = x_301_cast_fp16)[name = string("transpose_223")];
+            tensor<fp16, [1, 2048, 188]> input_675_cast_fp16 = conv(bias = encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_12_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_673_cast_fp16)[name = string("input_675_cast_fp16")];
+            int32 x_303_split_num_splits_0 = const()[name = string("x_303_split_num_splits_0"), val = int32(2)];
+            int32 x_303_split_axis_0 = const()[name = string("x_303_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_303_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_303_split_cast_fp16_1 = split(axis = x_303_split_axis_0, num_splits = x_303_split_num_splits_0, x = input_675_cast_fp16)[name = string("x_303_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_303_split_1_sigmoid_cast_fp16 = sigmoid(x = x_303_split_cast_fp16_1)[name = string("x_303_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_303_cast_fp16 = mul(x = x_303_split_cast_fp16_0, y = x_303_split_1_sigmoid_cast_fp16)[name = string("x_303_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_677_cast_fp16 = select(a = var_164_to_fp16, b = x_303_cast_fp16, cond = var_608)[name = string("input_677_cast_fp16")];
+            tensor<int32, [6]> input_679_pad_0 = const()[name = string("input_679_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_679_mode_0 = const()[name = string("input_679_mode_0"), val = string("constant")];
+            fp16 const_211_to_fp16 = const()[name = string("const_211_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_679_cast_fp16 = pad(constant_val = const_211_to_fp16, mode = input_679_mode_0, pad = input_679_pad_0, x = input_677_cast_fp16)[name = string("input_679_cast_fp16")];
+            string input_681_pad_type_0 = const()[name = string("input_681_pad_type_0"), val = string("valid")];
+            int32 input_681_groups_0 = const()[name = string("input_681_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_681_strides_0 = const()[name = string("input_681_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_681_pad_0 = const()[name = string("input_681_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_681_dilations_0 = const()[name = string("input_681_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_346_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157979136))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157983808))))[name = string("const_346_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_347_to_fp16 = const()[name = string("const_347_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157983936)))];
+            tensor<fp16, [1, 1024, 188]> input_683_cast_fp16 = conv(bias = const_347_to_fp16, dilations = input_681_dilations_0, groups = input_681_groups_0, pad = input_681_pad_0, pad_type = input_681_pad_type_0, strides = input_681_strides_0, weight = const_346_to_fp16_palettized, x = input_679_cast_fp16)[name = string("input_683_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_685_cast_fp16 = silu(x = input_683_cast_fp16)[name = string("input_685_cast_fp16")];
+            string x_305_pad_type_0 = const()[name = string("x_305_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_305_strides_0 = const()[name = string("x_305_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_305_pad_0 = const()[name = string("x_305_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_305_dilations_0 = const()[name = string("x_305_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_305_groups_0 = const()[name = string("x_305_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157986048))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158510400))))[name = string("encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158510528)))];
+            tensor<fp16, [1, 1024, 188]> x_305_cast_fp16 = conv(bias = encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16, dilations = x_305_dilations_0, groups = x_305_groups_0, pad = x_305_pad_0, pad_type = x_305_pad_type_0, strides = x_305_strides_0, weight = encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_685_cast_fp16)[name = string("x_305_cast_fp16")];
+            tensor<int32, [3]> input_687_perm_0 = const()[name = string("input_687_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_687_cast_fp16 = transpose(perm = input_687_perm_0, x = x_305_cast_fp16)[name = string("transpose_222")];
+            tensor<fp16, [1, 188, 1024]> input_689_cast_fp16 = add(x = input_671_cast_fp16, y = input_687_cast_fp16)[name = string("input_689_cast_fp16")];
+            tensor<int32, [1]> input_691_axes_0 = const()[name = string("input_691_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158512640)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158514752)))];
+            tensor<fp16, [1, 188, 1024]> input_691_cast_fp16 = layer_norm(axes = input_691_axes_0, beta = encoder_module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_689_cast_fp16)[name = string("input_691_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158516864))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160614080))))[name = string("encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160614208)))];
+            tensor<fp16, [1, 188, 4096]> linear_116_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16_palettized, x = input_691_cast_fp16)[name = string("linear_116_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_695_cast_fp16 = silu(x = linear_116_cast_fp16)[name = string("input_695_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160622464))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162719680))))[name = string("encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162719808)))];
+            tensor<fp16, [1, 188, 1024]> linear_117_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16_palettized, x = input_695_cast_fp16)[name = string("linear_117_cast_fp16")];
+            fp16 var_2738_to_fp16 = const()[name = string("op_2738_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2739_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2738_to_fp16)[name = string("op_2739_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_701_cast_fp16 = add(x = input_689_cast_fp16, y = var_2739_cast_fp16)[name = string("input_701_cast_fp16")];
+            tensor<int32, [1]> input_703_axes_0 = const()[name = string("input_703_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162721920)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162724032)))];
+            tensor<fp16, [1, 188, 1024]> input_703_cast_fp16 = layer_norm(axes = input_703_axes_0, beta = encoder_module_layers_12_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_out_weight_to_fp16, x = input_701_cast_fp16)[name = string("input_703_cast_fp16")];
+            tensor<int32, [1]> input_705_axes_0 = const()[name = string("input_705_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162726144)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162728256)))];
+            tensor<fp16, [1, 188, 1024]> input_705_cast_fp16 = layer_norm(axes = input_705_axes_0, beta = encoder_module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_703_cast_fp16)[name = string("input_705_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162730368))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164827584))))[name = string("encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164827712)))];
+            tensor<fp16, [1, 188, 4096]> linear_118_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16_palettized, x = input_705_cast_fp16)[name = string("linear_118_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_709_cast_fp16 = silu(x = linear_118_cast_fp16)[name = string("input_709_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164835968))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166933184))))[name = string("encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166933312)))];
+            tensor<fp16, [1, 188, 1024]> linear_119_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16_palettized, x = input_709_cast_fp16)[name = string("linear_119_cast_fp16")];
+            fp16 var_2769_to_fp16 = const()[name = string("op_2769_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2770_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2769_to_fp16)[name = string("op_2770_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_715_cast_fp16 = add(x = input_703_cast_fp16, y = var_2770_cast_fp16)[name = string("input_715_cast_fp16")];
+            tensor<int32, [1]> query_27_axes_0 = const()[name = string("query_27_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166935424)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166937536)))];
+            tensor<fp16, [1, 188, 1024]> query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = encoder_module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_self_att_weight_to_fp16, x = input_715_cast_fp16)[name = string("query_27_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166939648))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167464000))))[name = string("encoder_module_layers_13_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167464128)))];
+            tensor<fp16, [1, 188, 1024]> linear_120_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_q_weight_to_fp16_palettized, x = query_27_cast_fp16)[name = string("linear_120_cast_fp16")];
+            tensor<int32, [4]> var_2787 = const()[name = string("op_2787"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_79_cast_fp16 = reshape(shape = var_2787, x = linear_120_cast_fp16)[name = string("q_79_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167466240))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167990592))))[name = string("encoder_module_layers_13_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167990720)))];
+            tensor<fp16, [1, 188, 1024]> linear_121_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_k_weight_to_fp16_palettized, x = query_27_cast_fp16)[name = string("linear_121_cast_fp16")];
+            tensor<int32, [4]> var_2792 = const()[name = string("op_2792"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_53_cast_fp16 = reshape(shape = var_2792, x = linear_121_cast_fp16)[name = string("k_53_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167992832))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168517184))))[name = string("encoder_module_layers_13_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168517312)))];
+            tensor<fp16, [1, 188, 1024]> linear_122_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_v_weight_to_fp16_palettized, x = query_27_cast_fp16)[name = string("linear_122_cast_fp16")];
+            tensor<int32, [4]> var_2797 = const()[name = string("op_2797"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_27_cast_fp16 = reshape(shape = var_2797, x = linear_122_cast_fp16)[name = string("v_27_cast_fp16")];
+            tensor<int32, [4]> value_31_perm_0 = const()[name = string("value_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168519424)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2809_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_module_layers_13_self_attn_pos_bias_u_to_fp16)[name = string("op_2809_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168521536)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2811_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_module_layers_13_self_attn_pos_bias_v_to_fp16)[name = string("op_2811_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_27_perm_0 = const()[name = string("q_with_bias_v_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_313_transpose_x_0 = const()[name = string("x_313_transpose_x_0"), val = bool(false)];
+            bool x_313_transpose_y_0 = const()[name = string("x_313_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2813_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168523648))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168715712))))[name = string("op_2813_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2811_cast_fp16)[name = string("transpose_221")];
+            tensor<fp16, [1, 8, 188, 375]> x_313_cast_fp16 = matmul(transpose_x = x_313_transpose_x_0, transpose_y = x_313_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = op_2813_to_fp16_palettized)[name = string("x_313_cast_fp16")];
+            tensor<int32, [8]> x_315_pad_0 = const()[name = string("x_315_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_315_mode_0 = const()[name = string("x_315_mode_0"), val = string("constant")];
+            fp16 const_218_to_fp16 = const()[name = string("const_218_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_315_cast_fp16 = pad(constant_val = const_218_to_fp16, mode = x_315_mode_0, pad = x_315_pad_0, x = x_313_cast_fp16)[name = string("x_315_cast_fp16")];
+            tensor<int32, [4]> var_2821 = const()[name = string("op_2821"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_317_cast_fp16 = reshape(shape = var_2821, x = x_315_cast_fp16)[name = string("x_317_cast_fp16")];
+            tensor<int32, [4]> var_2825_begin_0 = const()[name = string("op_2825_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2825_end_0 = const()[name = string("op_2825_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2825_end_mask_0 = const()[name = string("op_2825_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2825_cast_fp16 = slice_by_index(begin = var_2825_begin_0, end = var_2825_end_0, end_mask = var_2825_end_mask_0, x = x_317_cast_fp16)[name = string("op_2825_cast_fp16")];
+            tensor<int32, [4]> var_2826 = const()[name = string("op_2826"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_53_cast_fp16 = reshape(shape = var_2826, x = var_2825_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<int32, [4]> transpose_122_perm_0 = const()[name = string("transpose_122_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_123_perm_0 = const()[name = string("transpose_123_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = string("transpose_219")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_122 = transpose(perm = transpose_122_perm_0, x = var_2809_cast_fp16)[name = string("transpose_220")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_55_begin_0 = const()[name = string("matrix_bd_55_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_55_end_0 = const()[name = string("matrix_bd_55_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_55_end_mask_0 = const()[name = string("matrix_bd_55_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2835_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = string("op_2835_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<fp16, [1, 8, 188, 188]> _inversed_scores_53_cast_fp16 = mul(x = var_2835_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = string("_inversed_scores_53_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_55_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_15)[name = string("scores_55_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2841_cast_fp16 = softmax(axis = var_152, x = scores_55_cast_fp16)[name = string("op_2841_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_717_cast_fp16 = select(a = var_164_to_fp16, b = var_2841_cast_fp16, cond = mask_15)[name = string("input_717_cast_fp16")];
+            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<fp16, [1, 8, 188, 128]> value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_27_cast_fp16)[name = string("transpose_218")];
+            tensor<fp16, [1, 8, 188, 128]> x_319_cast_fp16 = matmul(transpose_x = x_319_transpose_x_0, transpose_y = x_319_transpose_y_0, x = input_717_cast_fp16, y = value_31_cast_fp16)[name = string("x_319_cast_fp16")];
+            tensor<int32, [4]> var_2845_perm_0 = const()[name = string("op_2845_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2846 = const()[name = string("op_2846"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2845_cast_fp16 = transpose(perm = var_2845_perm_0, x = x_319_cast_fp16)[name = string("transpose_217")];
+            tensor<fp16, [1, 188, 1024]> input_719_cast_fp16 = reshape(shape = var_2846, x = var_2845_cast_fp16)[name = string("input_719_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168715840))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169240192))))[name = string("encoder_module_layers_13_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169240320)))];
+            tensor<fp16, [1, 188, 1024]> linear_124_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_out_weight_to_fp16_palettized, x = input_719_cast_fp16)[name = string("linear_124_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_723_cast_fp16 = add(x = input_715_cast_fp16, y = linear_124_cast_fp16)[name = string("input_723_cast_fp16")];
+            tensor<int32, [1]> x_323_axes_0 = const()[name = string("x_323_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169242432)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169244544)))];
+            tensor<fp16, [1, 188, 1024]> x_323_cast_fp16 = layer_norm(axes = x_323_axes_0, beta = encoder_module_layers_13_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_conv_weight_to_fp16, x = input_723_cast_fp16)[name = string("x_323_cast_fp16")];
+            tensor<int32, [3]> input_725_perm_0 = const()[name = string("input_725_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_727_pad_type_0 = const()[name = string("input_727_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_727_strides_0 = const()[name = string("input_727_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_727_pad_0 = const()[name = string("input_727_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_727_dilations_0 = const()[name = string("input_727_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_727_groups_0 = const()[name = string("input_727_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_13_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169246656))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170295296))))[name = string("encoder_module_layers_13_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170295424)))];
+            tensor<fp16, [1, 1024, 188]> input_725_cast_fp16 = transpose(perm = input_725_perm_0, x = x_323_cast_fp16)[name = string("transpose_216")];
+            tensor<fp16, [1, 2048, 188]> input_727_cast_fp16 = conv(bias = encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_13_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_725_cast_fp16)[name = string("input_727_cast_fp16")];
+            int32 x_325_split_num_splits_0 = const()[name = string("x_325_split_num_splits_0"), val = int32(2)];
+            int32 x_325_split_axis_0 = const()[name = string("x_325_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_325_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_325_split_cast_fp16_1 = split(axis = x_325_split_axis_0, num_splits = x_325_split_num_splits_0, x = input_727_cast_fp16)[name = string("x_325_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_325_split_1_sigmoid_cast_fp16 = sigmoid(x = x_325_split_cast_fp16_1)[name = string("x_325_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_325_cast_fp16 = mul(x = x_325_split_cast_fp16_0, y = x_325_split_1_sigmoid_cast_fp16)[name = string("x_325_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_729_cast_fp16 = select(a = var_164_to_fp16, b = x_325_cast_fp16, cond = var_608)[name = string("input_729_cast_fp16")];
+            tensor<int32, [6]> input_731_pad_0 = const()[name = string("input_731_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_731_mode_0 = const()[name = string("input_731_mode_0"), val = string("constant")];
+            fp16 const_221_to_fp16 = const()[name = string("const_221_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_731_cast_fp16 = pad(constant_val = const_221_to_fp16, mode = input_731_mode_0, pad = input_731_pad_0, x = input_729_cast_fp16)[name = string("input_731_cast_fp16")];
+            string input_733_pad_type_0 = const()[name = string("input_733_pad_type_0"), val = string("valid")];
+            int32 input_733_groups_0 = const()[name = string("input_733_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_733_strides_0 = const()[name = string("input_733_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_733_pad_0 = const()[name = string("input_733_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_733_dilations_0 = const()[name = string("input_733_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_348_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170299584))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170304256))))[name = string("const_348_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_349_to_fp16 = const()[name = string("const_349_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170304384)))];
+            tensor<fp16, [1, 1024, 188]> input_735_cast_fp16 = conv(bias = const_349_to_fp16, dilations = input_733_dilations_0, groups = input_733_groups_0, pad = input_733_pad_0, pad_type = input_733_pad_type_0, strides = input_733_strides_0, weight = const_348_to_fp16_palettized, x = input_731_cast_fp16)[name = string("input_735_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_737_cast_fp16 = silu(x = input_735_cast_fp16)[name = string("input_737_cast_fp16")];
+            string x_327_pad_type_0 = const()[name = string("x_327_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_327_strides_0 = const()[name = string("x_327_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_327_pad_0 = const()[name = string("x_327_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_327_dilations_0 = const()[name = string("x_327_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_327_groups_0 = const()[name = string("x_327_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170306496))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170830848))))[name = string("encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170830976)))];
+            tensor<fp16, [1, 1024, 188]> x_327_cast_fp16 = conv(bias = encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16, dilations = x_327_dilations_0, groups = x_327_groups_0, pad = x_327_pad_0, pad_type = x_327_pad_type_0, strides = x_327_strides_0, weight = encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_737_cast_fp16)[name = string("x_327_cast_fp16")];
+            tensor<int32, [3]> input_739_perm_0 = const()[name = string("input_739_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_739_cast_fp16 = transpose(perm = input_739_perm_0, x = x_327_cast_fp16)[name = string("transpose_215")];
+            tensor<fp16, [1, 188, 1024]> input_741_cast_fp16 = add(x = input_723_cast_fp16, y = input_739_cast_fp16)[name = string("input_741_cast_fp16")];
+            tensor<int32, [1]> input_743_axes_0 = const()[name = string("input_743_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170833088)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170835200)))];
+            tensor<fp16, [1, 188, 1024]> input_743_cast_fp16 = layer_norm(axes = input_743_axes_0, beta = encoder_module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_741_cast_fp16)[name = string("input_743_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170837312))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172934528))))[name = string("encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172934656)))];
+            tensor<fp16, [1, 188, 4096]> linear_125_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16_palettized, x = input_743_cast_fp16)[name = string("linear_125_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_747_cast_fp16 = silu(x = linear_125_cast_fp16)[name = string("input_747_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172942912))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175040128))))[name = string("encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175040256)))];
+            tensor<fp16, [1, 188, 1024]> linear_126_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16_palettized, x = input_747_cast_fp16)[name = string("linear_126_cast_fp16")];
+            fp16 var_2912_to_fp16 = const()[name = string("op_2912_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2913_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2912_to_fp16)[name = string("op_2913_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_753_cast_fp16 = add(x = input_741_cast_fp16, y = var_2913_cast_fp16)[name = string("input_753_cast_fp16")];
+            tensor<int32, [1]> input_755_axes_0 = const()[name = string("input_755_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175042368)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175044480)))];
+            tensor<fp16, [1, 188, 1024]> input_755_cast_fp16 = layer_norm(axes = input_755_axes_0, beta = encoder_module_layers_13_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_out_weight_to_fp16, x = input_753_cast_fp16)[name = string("input_755_cast_fp16")];
+            tensor<int32, [1]> input_757_axes_0 = const()[name = string("input_757_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175046592)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175048704)))];
+            tensor<fp16, [1, 188, 1024]> input_757_cast_fp16 = layer_norm(axes = input_757_axes_0, beta = encoder_module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_755_cast_fp16)[name = string("input_757_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175050816))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177148032))))[name = string("encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177148160)))];
+            tensor<fp16, [1, 188, 4096]> linear_127_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16_palettized, x = input_757_cast_fp16)[name = string("linear_127_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_761_cast_fp16 = silu(x = linear_127_cast_fp16)[name = string("input_761_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177156416))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179253632))))[name = string("encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179253760)))];
+            tensor<fp16, [1, 188, 1024]> linear_128_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16_palettized, x = input_761_cast_fp16)[name = string("linear_128_cast_fp16")];
+            fp16 var_2943_to_fp16 = const()[name = string("op_2943_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2944_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2943_to_fp16)[name = string("op_2944_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_767_cast_fp16 = add(x = input_755_cast_fp16, y = var_2944_cast_fp16)[name = string("input_767_cast_fp16")];
+            tensor<int32, [1]> query_29_axes_0 = const()[name = string("query_29_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179255872)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179257984)))];
+            tensor<fp16, [1, 188, 1024]> query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = encoder_module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_self_att_weight_to_fp16, x = input_767_cast_fp16)[name = string("query_29_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179260096))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179784448))))[name = string("encoder_module_layers_14_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179784576)))];
+            tensor<fp16, [1, 188, 1024]> linear_129_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_q_weight_to_fp16_palettized, x = query_29_cast_fp16)[name = string("linear_129_cast_fp16")];
+            tensor<int32, [4]> var_2961 = const()[name = string("op_2961"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_85_cast_fp16 = reshape(shape = var_2961, x = linear_129_cast_fp16)[name = string("q_85_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179786688))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180311040))))[name = string("encoder_module_layers_14_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180311168)))];
+            tensor<fp16, [1, 188, 1024]> linear_130_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_k_weight_to_fp16_palettized, x = query_29_cast_fp16)[name = string("linear_130_cast_fp16")];
+            tensor<int32, [4]> var_2966 = const()[name = string("op_2966"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_57_cast_fp16 = reshape(shape = var_2966, x = linear_130_cast_fp16)[name = string("k_57_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180313280))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180837632))))[name = string("encoder_module_layers_14_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180837760)))];
+            tensor<fp16, [1, 188, 1024]> linear_131_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_v_weight_to_fp16_palettized, x = query_29_cast_fp16)[name = string("linear_131_cast_fp16")];
+            tensor<int32, [4]> var_2971 = const()[name = string("op_2971"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_29_cast_fp16 = reshape(shape = var_2971, x = linear_131_cast_fp16)[name = string("v_29_cast_fp16")];
+            tensor<int32, [4]> value_33_perm_0 = const()[name = string("value_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180839872)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2983_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_module_layers_14_self_attn_pos_bias_u_to_fp16)[name = string("op_2983_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180841984)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2985_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_module_layers_14_self_attn_pos_bias_v_to_fp16)[name = string("op_2985_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_29_perm_0 = const()[name = string("q_with_bias_v_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_335_transpose_x_0 = const()[name = string("x_335_transpose_x_0"), val = bool(false)];
+            bool x_335_transpose_y_0 = const()[name = string("x_335_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2987_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180844096))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181036160))))[name = string("op_2987_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2985_cast_fp16)[name = string("transpose_214")];
+            tensor<fp16, [1, 8, 188, 375]> x_335_cast_fp16 = matmul(transpose_x = x_335_transpose_x_0, transpose_y = x_335_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = op_2987_to_fp16_palettized)[name = string("x_335_cast_fp16")];
+            tensor<int32, [8]> x_337_pad_0 = const()[name = string("x_337_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_337_mode_0 = const()[name = string("x_337_mode_0"), val = string("constant")];
+            fp16 const_228_to_fp16 = const()[name = string("const_228_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_337_cast_fp16 = pad(constant_val = const_228_to_fp16, mode = x_337_mode_0, pad = x_337_pad_0, x = x_335_cast_fp16)[name = string("x_337_cast_fp16")];
+            tensor<int32, [4]> var_2995 = const()[name = string("op_2995"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_339_cast_fp16 = reshape(shape = var_2995, x = x_337_cast_fp16)[name = string("x_339_cast_fp16")];
+            tensor<int32, [4]> var_2999_begin_0 = const()[name = string("op_2999_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2999_end_0 = const()[name = string("op_2999_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2999_end_mask_0 = const()[name = string("op_2999_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2999_cast_fp16 = slice_by_index(begin = var_2999_begin_0, end = var_2999_end_0, end_mask = var_2999_end_mask_0, x = x_339_cast_fp16)[name = string("op_2999_cast_fp16")];
+            tensor<int32, [4]> var_3000 = const()[name = string("op_3000"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_57_cast_fp16 = reshape(shape = var_3000, x = var_2999_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<int32, [4]> transpose_124_perm_0 = const()[name = string("transpose_124_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_125_perm_0 = const()[name = string("transpose_125_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = string("transpose_212")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_124 = transpose(perm = transpose_124_perm_0, x = var_2983_cast_fp16)[name = string("transpose_213")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_59_begin_0 = const()[name = string("matrix_bd_59_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_59_end_0 = const()[name = string("matrix_bd_59_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_59_end_mask_0 = const()[name = string("matrix_bd_59_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3009_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = string("op_3009_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<fp16, [1, 8, 188, 188]> _inversed_scores_57_cast_fp16 = mul(x = var_3009_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = string("_inversed_scores_57_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_59_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_15)[name = string("scores_59_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3015_cast_fp16 = softmax(axis = var_152, x = scores_59_cast_fp16)[name = string("op_3015_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_769_cast_fp16 = select(a = var_164_to_fp16, b = var_3015_cast_fp16, cond = mask_15)[name = string("input_769_cast_fp16")];
+            bool x_341_transpose_x_0 = const()[name = string("x_341_transpose_x_0"), val = bool(false)];
+            bool x_341_transpose_y_0 = const()[name = string("x_341_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_29_cast_fp16)[name = string("transpose_211")];
+            tensor<fp16, [1, 8, 188, 128]> x_341_cast_fp16 = matmul(transpose_x = x_341_transpose_x_0, transpose_y = x_341_transpose_y_0, x = input_769_cast_fp16, y = value_33_cast_fp16)[name = string("x_341_cast_fp16")];
+            tensor<int32, [4]> var_3019_perm_0 = const()[name = string("op_3019_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3020 = const()[name = string("op_3020"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3019_cast_fp16 = transpose(perm = var_3019_perm_0, x = x_341_cast_fp16)[name = string("transpose_210")];
+            tensor<fp16, [1, 188, 1024]> input_771_cast_fp16 = reshape(shape = var_3020, x = var_3019_cast_fp16)[name = string("input_771_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181036288))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181560640))))[name = string("encoder_module_layers_14_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181560768)))];
+            tensor<fp16, [1, 188, 1024]> linear_133_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_out_weight_to_fp16_palettized, x = input_771_cast_fp16)[name = string("linear_133_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_775_cast_fp16 = add(x = input_767_cast_fp16, y = linear_133_cast_fp16)[name = string("input_775_cast_fp16")];
+            tensor<int32, [1]> x_345_axes_0 = const()[name = string("x_345_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181562880)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181564992)))];
+            tensor<fp16, [1, 188, 1024]> x_345_cast_fp16 = layer_norm(axes = x_345_axes_0, beta = encoder_module_layers_14_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_conv_weight_to_fp16, x = input_775_cast_fp16)[name = string("x_345_cast_fp16")];
+            tensor<int32, [3]> input_777_perm_0 = const()[name = string("input_777_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_779_pad_type_0 = const()[name = string("input_779_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_779_strides_0 = const()[name = string("input_779_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_779_pad_0 = const()[name = string("input_779_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_779_dilations_0 = const()[name = string("input_779_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_779_groups_0 = const()[name = string("input_779_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_14_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181567104))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182615744))))[name = string("encoder_module_layers_14_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182615872)))];
+            tensor<fp16, [1, 1024, 188]> input_777_cast_fp16 = transpose(perm = input_777_perm_0, x = x_345_cast_fp16)[name = string("transpose_209")];
+            tensor<fp16, [1, 2048, 188]> input_779_cast_fp16 = conv(bias = encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_14_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_777_cast_fp16)[name = string("input_779_cast_fp16")];
+            int32 x_347_split_num_splits_0 = const()[name = string("x_347_split_num_splits_0"), val = int32(2)];
+            int32 x_347_split_axis_0 = const()[name = string("x_347_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_347_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_347_split_cast_fp16_1 = split(axis = x_347_split_axis_0, num_splits = x_347_split_num_splits_0, x = input_779_cast_fp16)[name = string("x_347_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_347_split_1_sigmoid_cast_fp16 = sigmoid(x = x_347_split_cast_fp16_1)[name = string("x_347_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_347_cast_fp16 = mul(x = x_347_split_cast_fp16_0, y = x_347_split_1_sigmoid_cast_fp16)[name = string("x_347_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_781_cast_fp16 = select(a = var_164_to_fp16, b = x_347_cast_fp16, cond = var_608)[name = string("input_781_cast_fp16")];
+            tensor<int32, [6]> input_783_pad_0 = const()[name = string("input_783_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_783_mode_0 = const()[name = string("input_783_mode_0"), val = string("constant")];
+            fp16 const_231_to_fp16 = const()[name = string("const_231_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_783_cast_fp16 = pad(constant_val = const_231_to_fp16, mode = input_783_mode_0, pad = input_783_pad_0, x = input_781_cast_fp16)[name = string("input_783_cast_fp16")];
+            string input_785_pad_type_0 = const()[name = string("input_785_pad_type_0"), val = string("valid")];
+            int32 input_785_groups_0 = const()[name = string("input_785_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_785_strides_0 = const()[name = string("input_785_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_785_pad_0 = const()[name = string("input_785_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_785_dilations_0 = const()[name = string("input_785_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_350_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182620032))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182624704))))[name = string("const_350_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_351_to_fp16 = const()[name = string("const_351_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182624832)))];
+            tensor<fp16, [1, 1024, 188]> input_787_cast_fp16 = conv(bias = const_351_to_fp16, dilations = input_785_dilations_0, groups = input_785_groups_0, pad = input_785_pad_0, pad_type = input_785_pad_type_0, strides = input_785_strides_0, weight = const_350_to_fp16_palettized, x = input_783_cast_fp16)[name = string("input_787_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_789_cast_fp16 = silu(x = input_787_cast_fp16)[name = string("input_789_cast_fp16")];
+            string x_349_pad_type_0 = const()[name = string("x_349_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_349_strides_0 = const()[name = string("x_349_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_349_pad_0 = const()[name = string("x_349_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_349_dilations_0 = const()[name = string("x_349_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_349_groups_0 = const()[name = string("x_349_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182626944))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183151296))))[name = string("encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183151424)))];
+            tensor<fp16, [1, 1024, 188]> x_349_cast_fp16 = conv(bias = encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16, dilations = x_349_dilations_0, groups = x_349_groups_0, pad = x_349_pad_0, pad_type = x_349_pad_type_0, strides = x_349_strides_0, weight = encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_789_cast_fp16)[name = string("x_349_cast_fp16")];
+            tensor<int32, [3]> input_791_perm_0 = const()[name = string("input_791_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_791_cast_fp16 = transpose(perm = input_791_perm_0, x = x_349_cast_fp16)[name = string("transpose_208")];
+            tensor<fp16, [1, 188, 1024]> input_793_cast_fp16 = add(x = input_775_cast_fp16, y = input_791_cast_fp16)[name = string("input_793_cast_fp16")];
+            tensor<int32, [1]> input_795_axes_0 = const()[name = string("input_795_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183153536)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183155648)))];
+            tensor<fp16, [1, 188, 1024]> input_795_cast_fp16 = layer_norm(axes = input_795_axes_0, beta = encoder_module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_793_cast_fp16)[name = string("input_795_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183157760))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185254976))))[name = string("encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185255104)))];
+            tensor<fp16, [1, 188, 4096]> linear_134_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16_palettized, x = input_795_cast_fp16)[name = string("linear_134_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_799_cast_fp16 = silu(x = linear_134_cast_fp16)[name = string("input_799_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185263360))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187360576))))[name = string("encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187360704)))];
+            tensor<fp16, [1, 188, 1024]> linear_135_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16_palettized, x = input_799_cast_fp16)[name = string("linear_135_cast_fp16")];
+            fp16 var_3086_to_fp16 = const()[name = string("op_3086_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3087_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_3086_to_fp16)[name = string("op_3087_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_805_cast_fp16 = add(x = input_793_cast_fp16, y = var_3087_cast_fp16)[name = string("input_805_cast_fp16")];
+            tensor<int32, [1]> input_807_axes_0 = const()[name = string("input_807_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187362816)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187364928)))];
+            tensor<fp16, [1, 188, 1024]> input_807_cast_fp16 = layer_norm(axes = input_807_axes_0, beta = encoder_module_layers_14_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_out_weight_to_fp16, x = input_805_cast_fp16)[name = string("input_807_cast_fp16")];
+            tensor<int32, [1]> input_809_axes_0 = const()[name = string("input_809_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187367040)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187369152)))];
+            tensor<fp16, [1, 188, 1024]> input_809_cast_fp16 = layer_norm(axes = input_809_axes_0, beta = encoder_module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_807_cast_fp16)[name = string("input_809_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187371264))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189468480))))[name = string("encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189468608)))];
+            tensor<fp16, [1, 188, 4096]> linear_136_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16_palettized, x = input_809_cast_fp16)[name = string("linear_136_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_813_cast_fp16 = silu(x = linear_136_cast_fp16)[name = string("input_813_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189476864))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191574080))))[name = string("encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191574208)))];
+            tensor<fp16, [1, 188, 1024]> linear_137_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16_palettized, x = input_813_cast_fp16)[name = string("linear_137_cast_fp16")];
+            fp16 var_3117_to_fp16 = const()[name = string("op_3117_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3118_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_3117_to_fp16)[name = string("op_3118_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_819_cast_fp16 = add(x = input_807_cast_fp16, y = var_3118_cast_fp16)[name = string("input_819_cast_fp16")];
+            tensor<int32, [1]> query_31_axes_0 = const()[name = string("query_31_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191576320)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191578432)))];
+            tensor<fp16, [1, 188, 1024]> query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = encoder_module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_self_att_weight_to_fp16, x = input_819_cast_fp16)[name = string("query_31_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191580544))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192104896))))[name = string("encoder_module_layers_15_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192105024)))];
+            tensor<fp16, [1, 188, 1024]> linear_138_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_q_weight_to_fp16_palettized, x = query_31_cast_fp16)[name = string("linear_138_cast_fp16")];
+            tensor<int32, [4]> var_3135 = const()[name = string("op_3135"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_91_cast_fp16 = reshape(shape = var_3135, x = linear_138_cast_fp16)[name = string("q_91_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192107136))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192631488))))[name = string("encoder_module_layers_15_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192631616)))];
+            tensor<fp16, [1, 188, 1024]> linear_139_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_k_weight_to_fp16_palettized, x = query_31_cast_fp16)[name = string("linear_139_cast_fp16")];
+            tensor<int32, [4]> var_3140 = const()[name = string("op_3140"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_61_cast_fp16 = reshape(shape = var_3140, x = linear_139_cast_fp16)[name = string("k_61_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192633728))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193158080))))[name = string("encoder_module_layers_15_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193158208)))];
+            tensor<fp16, [1, 188, 1024]> linear_140_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_v_weight_to_fp16_palettized, x = query_31_cast_fp16)[name = string("linear_140_cast_fp16")];
+            tensor<int32, [4]> var_3145 = const()[name = string("op_3145"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_31_cast_fp16 = reshape(shape = var_3145, x = linear_140_cast_fp16)[name = string("v_31_cast_fp16")];
+            tensor<int32, [4]> value_35_perm_0 = const()[name = string("value_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193160320)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3157_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_module_layers_15_self_attn_pos_bias_u_to_fp16)[name = string("op_3157_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193162432)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3159_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_module_layers_15_self_attn_pos_bias_v_to_fp16)[name = string("op_3159_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_31_perm_0 = const()[name = string("q_with_bias_v_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_357_transpose_x_0 = const()[name = string("x_357_transpose_x_0"), val = bool(false)];
+            bool x_357_transpose_y_0 = const()[name = string("x_357_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3161_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193164544))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193356608))))[name = string("op_3161_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_3159_cast_fp16)[name = string("transpose_207")];
+            tensor<fp16, [1, 8, 188, 375]> x_357_cast_fp16 = matmul(transpose_x = x_357_transpose_x_0, transpose_y = x_357_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = op_3161_to_fp16_palettized)[name = string("x_357_cast_fp16")];
+            tensor<int32, [8]> x_359_pad_0 = const()[name = string("x_359_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_359_mode_0 = const()[name = string("x_359_mode_0"), val = string("constant")];
+            fp16 const_238_to_fp16 = const()[name = string("const_238_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_359_cast_fp16 = pad(constant_val = const_238_to_fp16, mode = x_359_mode_0, pad = x_359_pad_0, x = x_357_cast_fp16)[name = string("x_359_cast_fp16")];
+            tensor<int32, [4]> var_3169 = const()[name = string("op_3169"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_361_cast_fp16 = reshape(shape = var_3169, x = x_359_cast_fp16)[name = string("x_361_cast_fp16")];
+            tensor<int32, [4]> var_3173_begin_0 = const()[name = string("op_3173_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3173_end_0 = const()[name = string("op_3173_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3173_end_mask_0 = const()[name = string("op_3173_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3173_cast_fp16 = slice_by_index(begin = var_3173_begin_0, end = var_3173_end_0, end_mask = var_3173_end_mask_0, x = x_361_cast_fp16)[name = string("op_3173_cast_fp16")];
+            tensor<int32, [4]> var_3174 = const()[name = string("op_3174"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_61_cast_fp16 = reshape(shape = var_3174, x = var_3173_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<int32, [4]> transpose_126_perm_0 = const()[name = string("transpose_126_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_127_perm_0 = const()[name = string("transpose_127_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = string("transpose_205")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_126 = transpose(perm = transpose_126_perm_0, x = var_3157_cast_fp16)[name = string("transpose_206")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_63_begin_0 = const()[name = string("matrix_bd_63_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_63_end_0 = const()[name = string("matrix_bd_63_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_63_end_mask_0 = const()[name = string("matrix_bd_63_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3183_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = string("op_3183_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<fp16, [1, 8, 188, 188]> _inversed_scores_61_cast_fp16 = mul(x = var_3183_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = string("_inversed_scores_61_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_63_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_15)[name = string("scores_63_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3189_cast_fp16 = softmax(axis = var_152, x = scores_63_cast_fp16)[name = string("op_3189_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_821_cast_fp16 = select(a = var_164_to_fp16, b = var_3189_cast_fp16, cond = mask_15)[name = string("input_821_cast_fp16")];
+            bool x_363_transpose_x_0 = const()[name = string("x_363_transpose_x_0"), val = bool(false)];
+            bool x_363_transpose_y_0 = const()[name = string("x_363_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_31_cast_fp16)[name = string("transpose_204")];
+            tensor<fp16, [1, 8, 188, 128]> x_363_cast_fp16 = matmul(transpose_x = x_363_transpose_x_0, transpose_y = x_363_transpose_y_0, x = input_821_cast_fp16, y = value_35_cast_fp16)[name = string("x_363_cast_fp16")];
+            tensor<int32, [4]> var_3193_perm_0 = const()[name = string("op_3193_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3194 = const()[name = string("op_3194"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3193_cast_fp16 = transpose(perm = var_3193_perm_0, x = x_363_cast_fp16)[name = string("transpose_203")];
+            tensor<fp16, [1, 188, 1024]> input_823_cast_fp16 = reshape(shape = var_3194, x = var_3193_cast_fp16)[name = string("input_823_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193356736))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193881088))))[name = string("encoder_module_layers_15_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193881216)))];
+            tensor<fp16, [1, 188, 1024]> linear_142_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_out_weight_to_fp16_palettized, x = input_823_cast_fp16)[name = string("linear_142_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_827_cast_fp16 = add(x = input_819_cast_fp16, y = linear_142_cast_fp16)[name = string("input_827_cast_fp16")];
+            tensor<int32, [1]> x_367_axes_0 = const()[name = string("x_367_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193883328)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193885440)))];
+            tensor<fp16, [1, 188, 1024]> x_367_cast_fp16 = layer_norm(axes = x_367_axes_0, beta = encoder_module_layers_15_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_conv_weight_to_fp16, x = input_827_cast_fp16)[name = string("x_367_cast_fp16")];
+            tensor<int32, [3]> input_829_perm_0 = const()[name = string("input_829_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_831_pad_type_0 = const()[name = string("input_831_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_831_strides_0 = const()[name = string("input_831_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_831_pad_0 = const()[name = string("input_831_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_831_dilations_0 = const()[name = string("input_831_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_831_groups_0 = const()[name = string("input_831_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_15_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193887552))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194936192))))[name = string("encoder_module_layers_15_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194936320)))];
+            tensor<fp16, [1, 1024, 188]> input_829_cast_fp16 = transpose(perm = input_829_perm_0, x = x_367_cast_fp16)[name = string("transpose_202")];
+            tensor<fp16, [1, 2048, 188]> input_831_cast_fp16 = conv(bias = encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_15_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_829_cast_fp16)[name = string("input_831_cast_fp16")];
+            int32 x_369_split_num_splits_0 = const()[name = string("x_369_split_num_splits_0"), val = int32(2)];
+            int32 x_369_split_axis_0 = const()[name = string("x_369_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_369_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_369_split_cast_fp16_1 = split(axis = x_369_split_axis_0, num_splits = x_369_split_num_splits_0, x = input_831_cast_fp16)[name = string("x_369_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_369_split_1_sigmoid_cast_fp16 = sigmoid(x = x_369_split_cast_fp16_1)[name = string("x_369_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_369_cast_fp16 = mul(x = x_369_split_cast_fp16_0, y = x_369_split_1_sigmoid_cast_fp16)[name = string("x_369_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_833_cast_fp16 = select(a = var_164_to_fp16, b = x_369_cast_fp16, cond = var_608)[name = string("input_833_cast_fp16")];
+            tensor<int32, [6]> input_835_pad_0 = const()[name = string("input_835_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_835_mode_0 = const()[name = string("input_835_mode_0"), val = string("constant")];
+            fp16 const_241_to_fp16 = const()[name = string("const_241_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_835_cast_fp16 = pad(constant_val = const_241_to_fp16, mode = input_835_mode_0, pad = input_835_pad_0, x = input_833_cast_fp16)[name = string("input_835_cast_fp16")];
+            string input_837_pad_type_0 = const()[name = string("input_837_pad_type_0"), val = string("valid")];
+            int32 input_837_groups_0 = const()[name = string("input_837_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_837_strides_0 = const()[name = string("input_837_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_837_pad_0 = const()[name = string("input_837_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_837_dilations_0 = const()[name = string("input_837_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_352_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194940480))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194945152))))[name = string("const_352_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_353_to_fp16 = const()[name = string("const_353_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194945280)))];
+            tensor<fp16, [1, 1024, 188]> input_839_cast_fp16 = conv(bias = const_353_to_fp16, dilations = input_837_dilations_0, groups = input_837_groups_0, pad = input_837_pad_0, pad_type = input_837_pad_type_0, strides = input_837_strides_0, weight = const_352_to_fp16_palettized, x = input_835_cast_fp16)[name = string("input_839_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_841_cast_fp16 = silu(x = input_839_cast_fp16)[name = string("input_841_cast_fp16")];
+            string x_371_pad_type_0 = const()[name = string("x_371_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_371_strides_0 = const()[name = string("x_371_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_371_pad_0 = const()[name = string("x_371_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_371_dilations_0 = const()[name = string("x_371_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_371_groups_0 = const()[name = string("x_371_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194947392))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195471744))))[name = string("encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195471872)))];
+            tensor<fp16, [1, 1024, 188]> x_371_cast_fp16 = conv(bias = encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16, dilations = x_371_dilations_0, groups = x_371_groups_0, pad = x_371_pad_0, pad_type = x_371_pad_type_0, strides = x_371_strides_0, weight = encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_841_cast_fp16)[name = string("x_371_cast_fp16")];
+            tensor<int32, [3]> input_843_perm_0 = const()[name = string("input_843_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_843_cast_fp16 = transpose(perm = input_843_perm_0, x = x_371_cast_fp16)[name = string("transpose_201")];
+            tensor<fp16, [1, 188, 1024]> input_845_cast_fp16 = add(x = input_827_cast_fp16, y = input_843_cast_fp16)[name = string("input_845_cast_fp16")];
+            tensor<int32, [1]> input_847_axes_0 = const()[name = string("input_847_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195473984)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195476096)))];
+            tensor<fp16, [1, 188, 1024]> input_847_cast_fp16 = layer_norm(axes = input_847_axes_0, beta = encoder_module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_845_cast_fp16)[name = string("input_847_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195478208))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197575424))))[name = string("encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197575552)))];
+            tensor<fp16, [1, 188, 4096]> linear_143_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16_palettized, x = input_847_cast_fp16)[name = string("linear_143_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_851_cast_fp16 = silu(x = linear_143_cast_fp16)[name = string("input_851_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197583808))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199681024))))[name = string("encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199681152)))];
+            tensor<fp16, [1, 188, 1024]> linear_144_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16_palettized, x = input_851_cast_fp16)[name = string("linear_144_cast_fp16")];
+            fp16 var_3260_to_fp16 = const()[name = string("op_3260_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3261_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_3260_to_fp16)[name = string("op_3261_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_857_cast_fp16 = add(x = input_845_cast_fp16, y = var_3261_cast_fp16)[name = string("input_857_cast_fp16")];
+            tensor<int32, [1]> input_859_axes_0 = const()[name = string("input_859_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199683264)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199685376)))];
+            tensor<fp16, [1, 188, 1024]> input_859_cast_fp16 = layer_norm(axes = input_859_axes_0, beta = encoder_module_layers_15_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_out_weight_to_fp16, x = input_857_cast_fp16)[name = string("input_859_cast_fp16")];
+            tensor<int32, [1]> input_861_axes_0 = const()[name = string("input_861_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199687488)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199689600)))];
+            tensor<fp16, [1, 188, 1024]> input_861_cast_fp16 = layer_norm(axes = input_861_axes_0, beta = encoder_module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_859_cast_fp16)[name = string("input_861_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199691712))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201788928))))[name = string("encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201789056)))];
+            tensor<fp16, [1, 188, 4096]> linear_145_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16_palettized, x = input_861_cast_fp16)[name = string("linear_145_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_865_cast_fp16 = silu(x = linear_145_cast_fp16)[name = string("input_865_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201797312))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203894528))))[name = string("encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203894656)))];
+            tensor<fp16, [1, 188, 1024]> linear_146_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16_palettized, x = input_865_cast_fp16)[name = string("linear_146_cast_fp16")];
+            fp16 var_3291_to_fp16 = const()[name = string("op_3291_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3292_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3291_to_fp16)[name = string("op_3292_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_871_cast_fp16 = add(x = input_859_cast_fp16, y = var_3292_cast_fp16)[name = string("input_871_cast_fp16")];
+            tensor<int32, [1]> query_33_axes_0 = const()[name = string("query_33_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203896768)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203898880)))];
+            tensor<fp16, [1, 188, 1024]> query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = encoder_module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_self_att_weight_to_fp16, x = input_871_cast_fp16)[name = string("query_33_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203900992))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204425344))))[name = string("encoder_module_layers_16_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204425472)))];
+            tensor<fp16, [1, 188, 1024]> linear_147_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_q_weight_to_fp16_palettized, x = query_33_cast_fp16)[name = string("linear_147_cast_fp16")];
+            tensor<int32, [4]> var_3309 = const()[name = string("op_3309"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_97_cast_fp16 = reshape(shape = var_3309, x = linear_147_cast_fp16)[name = string("q_97_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204427584))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204951936))))[name = string("encoder_module_layers_16_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204952064)))];
+            tensor<fp16, [1, 188, 1024]> linear_148_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_k_weight_to_fp16_palettized, x = query_33_cast_fp16)[name = string("linear_148_cast_fp16")];
+            tensor<int32, [4]> var_3314 = const()[name = string("op_3314"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_65_cast_fp16 = reshape(shape = var_3314, x = linear_148_cast_fp16)[name = string("k_65_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204954176))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205478528))))[name = string("encoder_module_layers_16_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205478656)))];
+            tensor<fp16, [1, 188, 1024]> linear_149_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_v_weight_to_fp16_palettized, x = query_33_cast_fp16)[name = string("linear_149_cast_fp16")];
+            tensor<int32, [4]> var_3319 = const()[name = string("op_3319"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_33_cast_fp16 = reshape(shape = var_3319, x = linear_149_cast_fp16)[name = string("v_33_cast_fp16")];
+            tensor<int32, [4]> value_37_perm_0 = const()[name = string("value_37_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205480768)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3331_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_module_layers_16_self_attn_pos_bias_u_to_fp16)[name = string("op_3331_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205482880)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3333_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_module_layers_16_self_attn_pos_bias_v_to_fp16)[name = string("op_3333_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_33_perm_0 = const()[name = string("q_with_bias_v_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_379_transpose_x_0 = const()[name = string("x_379_transpose_x_0"), val = bool(false)];
+            bool x_379_transpose_y_0 = const()[name = string("x_379_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3335_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205484992))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205677056))))[name = string("op_3335_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_3333_cast_fp16)[name = string("transpose_200")];
+            tensor<fp16, [1, 8, 188, 375]> x_379_cast_fp16 = matmul(transpose_x = x_379_transpose_x_0, transpose_y = x_379_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = op_3335_to_fp16_palettized)[name = string("x_379_cast_fp16")];
+            tensor<int32, [8]> x_381_pad_0 = const()[name = string("x_381_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_381_mode_0 = const()[name = string("x_381_mode_0"), val = string("constant")];
+            fp16 const_248_to_fp16 = const()[name = string("const_248_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_381_cast_fp16 = pad(constant_val = const_248_to_fp16, mode = x_381_mode_0, pad = x_381_pad_0, x = x_379_cast_fp16)[name = string("x_381_cast_fp16")];
+            tensor<int32, [4]> var_3343 = const()[name = string("op_3343"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_383_cast_fp16 = reshape(shape = var_3343, x = x_381_cast_fp16)[name = string("x_383_cast_fp16")];
+            tensor<int32, [4]> var_3347_begin_0 = const()[name = string("op_3347_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3347_end_0 = const()[name = string("op_3347_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3347_end_mask_0 = const()[name = string("op_3347_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3347_cast_fp16 = slice_by_index(begin = var_3347_begin_0, end = var_3347_end_0, end_mask = var_3347_end_mask_0, x = x_383_cast_fp16)[name = string("op_3347_cast_fp16")];
+            tensor<int32, [4]> var_3348 = const()[name = string("op_3348"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_65_cast_fp16 = reshape(shape = var_3348, x = var_3347_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<int32, [4]> transpose_128_perm_0 = const()[name = string("transpose_128_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_129_perm_0 = const()[name = string("transpose_129_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = string("transpose_198")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_128 = transpose(perm = transpose_128_perm_0, x = var_3331_cast_fp16)[name = string("transpose_199")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_67_begin_0 = const()[name = string("matrix_bd_67_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_67_end_0 = const()[name = string("matrix_bd_67_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_67_end_mask_0 = const()[name = string("matrix_bd_67_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3357_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = string("op_3357_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<fp16, [1, 8, 188, 188]> _inversed_scores_65_cast_fp16 = mul(x = var_3357_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = string("_inversed_scores_65_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_67_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_15)[name = string("scores_67_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3363_cast_fp16 = softmax(axis = var_152, x = scores_67_cast_fp16)[name = string("op_3363_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_873_cast_fp16 = select(a = var_164_to_fp16, b = var_3363_cast_fp16, cond = mask_15)[name = string("input_873_cast_fp16")];
+            bool x_385_transpose_x_0 = const()[name = string("x_385_transpose_x_0"), val = bool(false)];
+            bool x_385_transpose_y_0 = const()[name = string("x_385_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_33_cast_fp16)[name = string("transpose_197")];
+            tensor<fp16, [1, 8, 188, 128]> x_385_cast_fp16 = matmul(transpose_x = x_385_transpose_x_0, transpose_y = x_385_transpose_y_0, x = input_873_cast_fp16, y = value_37_cast_fp16)[name = string("x_385_cast_fp16")];
+            tensor<int32, [4]> var_3367_perm_0 = const()[name = string("op_3367_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3368 = const()[name = string("op_3368"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3367_cast_fp16 = transpose(perm = var_3367_perm_0, x = x_385_cast_fp16)[name = string("transpose_196")];
+            tensor<fp16, [1, 188, 1024]> input_875_cast_fp16 = reshape(shape = var_3368, x = var_3367_cast_fp16)[name = string("input_875_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205677184))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206201536))))[name = string("encoder_module_layers_16_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206201664)))];
+            tensor<fp16, [1, 188, 1024]> linear_151_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_out_weight_to_fp16_palettized, x = input_875_cast_fp16)[name = string("linear_151_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_879_cast_fp16 = add(x = input_871_cast_fp16, y = linear_151_cast_fp16)[name = string("input_879_cast_fp16")];
+            tensor<int32, [1]> x_389_axes_0 = const()[name = string("x_389_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206203776)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206205888)))];
+            tensor<fp16, [1, 188, 1024]> x_389_cast_fp16 = layer_norm(axes = x_389_axes_0, beta = encoder_module_layers_16_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_conv_weight_to_fp16, x = input_879_cast_fp16)[name = string("x_389_cast_fp16")];
+            tensor<int32, [3]> input_881_perm_0 = const()[name = string("input_881_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_883_pad_type_0 = const()[name = string("input_883_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_883_strides_0 = const()[name = string("input_883_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_883_pad_0 = const()[name = string("input_883_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_883_dilations_0 = const()[name = string("input_883_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_883_groups_0 = const()[name = string("input_883_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_16_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206208000))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207256640))))[name = string("encoder_module_layers_16_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207256768)))];
+            tensor<fp16, [1, 1024, 188]> input_881_cast_fp16 = transpose(perm = input_881_perm_0, x = x_389_cast_fp16)[name = string("transpose_195")];
+            tensor<fp16, [1, 2048, 188]> input_883_cast_fp16 = conv(bias = encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_16_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_881_cast_fp16)[name = string("input_883_cast_fp16")];
+            int32 x_391_split_num_splits_0 = const()[name = string("x_391_split_num_splits_0"), val = int32(2)];
+            int32 x_391_split_axis_0 = const()[name = string("x_391_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_391_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_391_split_cast_fp16_1 = split(axis = x_391_split_axis_0, num_splits = x_391_split_num_splits_0, x = input_883_cast_fp16)[name = string("x_391_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_391_split_1_sigmoid_cast_fp16 = sigmoid(x = x_391_split_cast_fp16_1)[name = string("x_391_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_391_cast_fp16 = mul(x = x_391_split_cast_fp16_0, y = x_391_split_1_sigmoid_cast_fp16)[name = string("x_391_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_885_cast_fp16 = select(a = var_164_to_fp16, b = x_391_cast_fp16, cond = var_608)[name = string("input_885_cast_fp16")];
+            tensor<int32, [6]> input_887_pad_0 = const()[name = string("input_887_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_887_mode_0 = const()[name = string("input_887_mode_0"), val = string("constant")];
+            fp16 const_251_to_fp16 = const()[name = string("const_251_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_887_cast_fp16 = pad(constant_val = const_251_to_fp16, mode = input_887_mode_0, pad = input_887_pad_0, x = input_885_cast_fp16)[name = string("input_887_cast_fp16")];
+            string input_889_pad_type_0 = const()[name = string("input_889_pad_type_0"), val = string("valid")];
+            int32 input_889_groups_0 = const()[name = string("input_889_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_889_strides_0 = const()[name = string("input_889_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_889_pad_0 = const()[name = string("input_889_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_889_dilations_0 = const()[name = string("input_889_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_354_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207260928))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207265600))))[name = string("const_354_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_355_to_fp16 = const()[name = string("const_355_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207265728)))];
+            tensor<fp16, [1, 1024, 188]> input_891_cast_fp16 = conv(bias = const_355_to_fp16, dilations = input_889_dilations_0, groups = input_889_groups_0, pad = input_889_pad_0, pad_type = input_889_pad_type_0, strides = input_889_strides_0, weight = const_354_to_fp16_palettized, x = input_887_cast_fp16)[name = string("input_891_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_893_cast_fp16 = silu(x = input_891_cast_fp16)[name = string("input_893_cast_fp16")];
+            string x_393_pad_type_0 = const()[name = string("x_393_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_393_strides_0 = const()[name = string("x_393_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_393_pad_0 = const()[name = string("x_393_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_393_dilations_0 = const()[name = string("x_393_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_393_groups_0 = const()[name = string("x_393_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207267840))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207792192))))[name = string("encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207792320)))];
+            tensor<fp16, [1, 1024, 188]> x_393_cast_fp16 = conv(bias = encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16, dilations = x_393_dilations_0, groups = x_393_groups_0, pad = x_393_pad_0, pad_type = x_393_pad_type_0, strides = x_393_strides_0, weight = encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_893_cast_fp16)[name = string("x_393_cast_fp16")];
+            tensor<int32, [3]> input_895_perm_0 = const()[name = string("input_895_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_895_cast_fp16 = transpose(perm = input_895_perm_0, x = x_393_cast_fp16)[name = string("transpose_194")];
+            tensor<fp16, [1, 188, 1024]> input_897_cast_fp16 = add(x = input_879_cast_fp16, y = input_895_cast_fp16)[name = string("input_897_cast_fp16")];
+            tensor<int32, [1]> input_899_axes_0 = const()[name = string("input_899_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207794432)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207796544)))];
+            tensor<fp16, [1, 188, 1024]> input_899_cast_fp16 = layer_norm(axes = input_899_axes_0, beta = encoder_module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_897_cast_fp16)[name = string("input_899_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207798656))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209895872))))[name = string("encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209896000)))];
+            tensor<fp16, [1, 188, 4096]> linear_152_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16_palettized, x = input_899_cast_fp16)[name = string("linear_152_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_903_cast_fp16 = silu(x = linear_152_cast_fp16)[name = string("input_903_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209904256))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212001472))))[name = string("encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212001600)))];
+            tensor<fp16, [1, 188, 1024]> linear_153_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16_palettized, x = input_903_cast_fp16)[name = string("linear_153_cast_fp16")];
+            fp16 var_3434_to_fp16 = const()[name = string("op_3434_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3435_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_3434_to_fp16)[name = string("op_3435_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_909_cast_fp16 = add(x = input_897_cast_fp16, y = var_3435_cast_fp16)[name = string("input_909_cast_fp16")];
+            tensor<int32, [1]> input_911_axes_0 = const()[name = string("input_911_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212003712)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212005824)))];
+            tensor<fp16, [1, 188, 1024]> input_911_cast_fp16 = layer_norm(axes = input_911_axes_0, beta = encoder_module_layers_16_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_out_weight_to_fp16, x = input_909_cast_fp16)[name = string("input_911_cast_fp16")];
+            tensor<int32, [1]> input_913_axes_0 = const()[name = string("input_913_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212007936)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212010048)))];
+            tensor<fp16, [1, 188, 1024]> input_913_cast_fp16 = layer_norm(axes = input_913_axes_0, beta = encoder_module_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_feed_forward1_weight_to_fp16, x = input_911_cast_fp16)[name = string("input_913_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212012160))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214109376))))[name = string("encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214109504)))];
+            tensor<fp16, [1, 188, 4096]> linear_154_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16_palettized, x = input_913_cast_fp16)[name = string("linear_154_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_917_cast_fp16 = silu(x = linear_154_cast_fp16)[name = string("input_917_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214117760))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216214976))))[name = string("encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216215104)))];
+            tensor<fp16, [1, 188, 1024]> linear_155_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16_palettized, x = input_917_cast_fp16)[name = string("linear_155_cast_fp16")];
+            fp16 var_3465_to_fp16 = const()[name = string("op_3465_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3466_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_3465_to_fp16)[name = string("op_3466_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_923_cast_fp16 = add(x = input_911_cast_fp16, y = var_3466_cast_fp16)[name = string("input_923_cast_fp16")];
+            tensor<int32, [1]> query_35_axes_0 = const()[name = string("query_35_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216217216)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216219328)))];
+            tensor<fp16, [1, 188, 1024]> query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = encoder_module_layers_17_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_self_att_weight_to_fp16, x = input_923_cast_fp16)[name = string("query_35_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216221440))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216745792))))[name = string("encoder_module_layers_17_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216745920)))];
+            tensor<fp16, [1, 188, 1024]> linear_156_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_q_weight_to_fp16_palettized, x = query_35_cast_fp16)[name = string("linear_156_cast_fp16")];
+            tensor<int32, [4]> var_3483 = const()[name = string("op_3483"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_103_cast_fp16 = reshape(shape = var_3483, x = linear_156_cast_fp16)[name = string("q_103_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216748032))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217272384))))[name = string("encoder_module_layers_17_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217272512)))];
+            tensor<fp16, [1, 188, 1024]> linear_157_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_k_weight_to_fp16_palettized, x = query_35_cast_fp16)[name = string("linear_157_cast_fp16")];
+            tensor<int32, [4]> var_3488 = const()[name = string("op_3488"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_69_cast_fp16 = reshape(shape = var_3488, x = linear_157_cast_fp16)[name = string("k_69_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217274624))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217798976))))[name = string("encoder_module_layers_17_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217799104)))];
+            tensor<fp16, [1, 188, 1024]> linear_158_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_v_weight_to_fp16_palettized, x = query_35_cast_fp16)[name = string("linear_158_cast_fp16")];
+            tensor<int32, [4]> var_3493 = const()[name = string("op_3493"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_35_cast_fp16 = reshape(shape = var_3493, x = linear_158_cast_fp16)[name = string("v_35_cast_fp16")];
+            tensor<int32, [4]> value_39_perm_0 = const()[name = string("value_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217801216)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3505_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_module_layers_17_self_attn_pos_bias_u_to_fp16)[name = string("op_3505_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217803328)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3507_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_module_layers_17_self_attn_pos_bias_v_to_fp16)[name = string("op_3507_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_35_perm_0 = const()[name = string("q_with_bias_v_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_401_transpose_x_0 = const()[name = string("x_401_transpose_x_0"), val = bool(false)];
+            bool x_401_transpose_y_0 = const()[name = string("x_401_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3509_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217805440))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217997504))))[name = string("op_3509_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3507_cast_fp16)[name = string("transpose_193")];
+            tensor<fp16, [1, 8, 188, 375]> x_401_cast_fp16 = matmul(transpose_x = x_401_transpose_x_0, transpose_y = x_401_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = op_3509_to_fp16_palettized)[name = string("x_401_cast_fp16")];
+            tensor<int32, [8]> x_403_pad_0 = const()[name = string("x_403_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_403_mode_0 = const()[name = string("x_403_mode_0"), val = string("constant")];
+            fp16 const_258_to_fp16 = const()[name = string("const_258_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_403_cast_fp16 = pad(constant_val = const_258_to_fp16, mode = x_403_mode_0, pad = x_403_pad_0, x = x_401_cast_fp16)[name = string("x_403_cast_fp16")];
+            tensor<int32, [4]> var_3517 = const()[name = string("op_3517"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_405_cast_fp16 = reshape(shape = var_3517, x = x_403_cast_fp16)[name = string("x_405_cast_fp16")];
+            tensor<int32, [4]> var_3521_begin_0 = const()[name = string("op_3521_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3521_end_0 = const()[name = string("op_3521_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3521_end_mask_0 = const()[name = string("op_3521_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3521_cast_fp16 = slice_by_index(begin = var_3521_begin_0, end = var_3521_end_0, end_mask = var_3521_end_mask_0, x = x_405_cast_fp16)[name = string("op_3521_cast_fp16")];
+            tensor<int32, [4]> var_3522 = const()[name = string("op_3522"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_69_cast_fp16 = reshape(shape = var_3522, x = var_3521_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<int32, [4]> transpose_130_perm_0 = const()[name = string("transpose_130_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_131_perm_0 = const()[name = string("transpose_131_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = string("transpose_191")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_130 = transpose(perm = transpose_130_perm_0, x = var_3505_cast_fp16)[name = string("transpose_192")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_71_begin_0 = const()[name = string("matrix_bd_71_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_71_end_0 = const()[name = string("matrix_bd_71_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_71_end_mask_0 = const()[name = string("matrix_bd_71_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3531_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = string("op_3531_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<fp16, [1, 8, 188, 188]> _inversed_scores_69_cast_fp16 = mul(x = var_3531_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = string("_inversed_scores_69_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_71_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_15)[name = string("scores_71_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3537_cast_fp16 = softmax(axis = var_152, x = scores_71_cast_fp16)[name = string("op_3537_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_925_cast_fp16 = select(a = var_164_to_fp16, b = var_3537_cast_fp16, cond = mask_15)[name = string("input_925_cast_fp16")];
+            bool x_407_transpose_x_0 = const()[name = string("x_407_transpose_x_0"), val = bool(false)];
+            bool x_407_transpose_y_0 = const()[name = string("x_407_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_35_cast_fp16)[name = string("transpose_190")];
+            tensor<fp16, [1, 8, 188, 128]> x_407_cast_fp16 = matmul(transpose_x = x_407_transpose_x_0, transpose_y = x_407_transpose_y_0, x = input_925_cast_fp16, y = value_39_cast_fp16)[name = string("x_407_cast_fp16")];
+            tensor<int32, [4]> var_3541_perm_0 = const()[name = string("op_3541_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3542 = const()[name = string("op_3542"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3541_cast_fp16 = transpose(perm = var_3541_perm_0, x = x_407_cast_fp16)[name = string("transpose_189")];
+            tensor<fp16, [1, 188, 1024]> input_927_cast_fp16 = reshape(shape = var_3542, x = var_3541_cast_fp16)[name = string("input_927_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217997632))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218521984))))[name = string("encoder_module_layers_17_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218522112)))];
+            tensor<fp16, [1, 188, 1024]> linear_160_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_out_weight_to_fp16_palettized, x = input_927_cast_fp16)[name = string("linear_160_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_931_cast_fp16 = add(x = input_923_cast_fp16, y = linear_160_cast_fp16)[name = string("input_931_cast_fp16")];
+            tensor<int32, [1]> x_411_axes_0 = const()[name = string("x_411_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218524224)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218526336)))];
+            tensor<fp16, [1, 188, 1024]> x_411_cast_fp16 = layer_norm(axes = x_411_axes_0, beta = encoder_module_layers_17_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_conv_weight_to_fp16, x = input_931_cast_fp16)[name = string("x_411_cast_fp16")];
+            tensor<int32, [3]> input_933_perm_0 = const()[name = string("input_933_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_935_pad_type_0 = const()[name = string("input_935_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_935_strides_0 = const()[name = string("input_935_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_935_pad_0 = const()[name = string("input_935_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_935_dilations_0 = const()[name = string("input_935_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_935_groups_0 = const()[name = string("input_935_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_17_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218528448))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219577088))))[name = string("encoder_module_layers_17_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219577216)))];
+            tensor<fp16, [1, 1024, 188]> input_933_cast_fp16 = transpose(perm = input_933_perm_0, x = x_411_cast_fp16)[name = string("transpose_188")];
+            tensor<fp16, [1, 2048, 188]> input_935_cast_fp16 = conv(bias = encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_17_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_933_cast_fp16)[name = string("input_935_cast_fp16")];
+            int32 x_413_split_num_splits_0 = const()[name = string("x_413_split_num_splits_0"), val = int32(2)];
+            int32 x_413_split_axis_0 = const()[name = string("x_413_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_413_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_413_split_cast_fp16_1 = split(axis = x_413_split_axis_0, num_splits = x_413_split_num_splits_0, x = input_935_cast_fp16)[name = string("x_413_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_413_split_1_sigmoid_cast_fp16 = sigmoid(x = x_413_split_cast_fp16_1)[name = string("x_413_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_413_cast_fp16 = mul(x = x_413_split_cast_fp16_0, y = x_413_split_1_sigmoid_cast_fp16)[name = string("x_413_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_937_cast_fp16 = select(a = var_164_to_fp16, b = x_413_cast_fp16, cond = var_608)[name = string("input_937_cast_fp16")];
+            tensor<int32, [6]> input_939_pad_0 = const()[name = string("input_939_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_939_mode_0 = const()[name = string("input_939_mode_0"), val = string("constant")];
+            fp16 const_261_to_fp16 = const()[name = string("const_261_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_939_cast_fp16 = pad(constant_val = const_261_to_fp16, mode = input_939_mode_0, pad = input_939_pad_0, x = input_937_cast_fp16)[name = string("input_939_cast_fp16")];
+            string input_941_pad_type_0 = const()[name = string("input_941_pad_type_0"), val = string("valid")];
+            int32 input_941_groups_0 = const()[name = string("input_941_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_941_strides_0 = const()[name = string("input_941_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_941_pad_0 = const()[name = string("input_941_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_941_dilations_0 = const()[name = string("input_941_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_356_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219581376))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219586048))))[name = string("const_356_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_357_to_fp16 = const()[name = string("const_357_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219586176)))];
+            tensor<fp16, [1, 1024, 188]> input_943_cast_fp16 = conv(bias = const_357_to_fp16, dilations = input_941_dilations_0, groups = input_941_groups_0, pad = input_941_pad_0, pad_type = input_941_pad_type_0, strides = input_941_strides_0, weight = const_356_to_fp16_palettized, x = input_939_cast_fp16)[name = string("input_943_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_945_cast_fp16 = silu(x = input_943_cast_fp16)[name = string("input_945_cast_fp16")];
+            string x_415_pad_type_0 = const()[name = string("x_415_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_415_strides_0 = const()[name = string("x_415_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_415_pad_0 = const()[name = string("x_415_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_415_dilations_0 = const()[name = string("x_415_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_415_groups_0 = const()[name = string("x_415_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_17_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219588288))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220112640))))[name = string("encoder_module_layers_17_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220112768)))];
+            tensor<fp16, [1, 1024, 188]> x_415_cast_fp16 = conv(bias = encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_17_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_945_cast_fp16)[name = string("x_415_cast_fp16")];
+            tensor<int32, [3]> input_947_perm_0 = const()[name = string("input_947_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_947_cast_fp16 = transpose(perm = input_947_perm_0, x = x_415_cast_fp16)[name = string("transpose_187")];
+            tensor<fp16, [1, 188, 1024]> input_949_cast_fp16 = add(x = input_931_cast_fp16, y = input_947_cast_fp16)[name = string("input_949_cast_fp16")];
+            tensor<int32, [1]> input_951_axes_0 = const()[name = string("input_951_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220114880)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220116992)))];
+            tensor<fp16, [1, 188, 1024]> input_951_cast_fp16 = layer_norm(axes = input_951_axes_0, beta = encoder_module_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_feed_forward2_weight_to_fp16, x = input_949_cast_fp16)[name = string("input_951_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220119104))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222216320))))[name = string("encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222216448)))];
+            tensor<fp16, [1, 188, 4096]> linear_161_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16_palettized, x = input_951_cast_fp16)[name = string("linear_161_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_955_cast_fp16 = silu(x = linear_161_cast_fp16)[name = string("input_955_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222224704))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224321920))))[name = string("encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224322048)))];
+            tensor<fp16, [1, 188, 1024]> linear_162_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16_palettized, x = input_955_cast_fp16)[name = string("linear_162_cast_fp16")];
+            fp16 var_3608_to_fp16 = const()[name = string("op_3608_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3609_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_3608_to_fp16)[name = string("op_3609_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_961_cast_fp16 = add(x = input_949_cast_fp16, y = var_3609_cast_fp16)[name = string("input_961_cast_fp16")];
+            tensor<int32, [1]> input_963_axes_0 = const()[name = string("input_963_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224324160)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224326272)))];
+            tensor<fp16, [1, 188, 1024]> input_963_cast_fp16 = layer_norm(axes = input_963_axes_0, beta = encoder_module_layers_17_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_out_weight_to_fp16, x = input_961_cast_fp16)[name = string("input_963_cast_fp16")];
+            tensor<int32, [1]> input_965_axes_0 = const()[name = string("input_965_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224328384)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224330496)))];
+            tensor<fp16, [1, 188, 1024]> input_965_cast_fp16 = layer_norm(axes = input_965_axes_0, beta = encoder_module_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_feed_forward1_weight_to_fp16, x = input_963_cast_fp16)[name = string("input_965_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224332608))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226429824))))[name = string("encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226429952)))];
+            tensor<fp16, [1, 188, 4096]> linear_163_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16_palettized, x = input_965_cast_fp16)[name = string("linear_163_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_969_cast_fp16 = silu(x = linear_163_cast_fp16)[name = string("input_969_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226438208))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228535424))))[name = string("encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228535552)))];
+            tensor<fp16, [1, 188, 1024]> linear_164_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16_palettized, x = input_969_cast_fp16)[name = string("linear_164_cast_fp16")];
+            fp16 var_3639_to_fp16 = const()[name = string("op_3639_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3640_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3639_to_fp16)[name = string("op_3640_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_975_cast_fp16 = add(x = input_963_cast_fp16, y = var_3640_cast_fp16)[name = string("input_975_cast_fp16")];
+            tensor<int32, [1]> query_37_axes_0 = const()[name = string("query_37_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228537664)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228539776)))];
+            tensor<fp16, [1, 188, 1024]> query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = encoder_module_layers_18_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_self_att_weight_to_fp16, x = input_975_cast_fp16)[name = string("query_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(228541888))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229066240))))[name = string("encoder_module_layers_18_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229066368)))];
+            tensor<fp16, [1, 188, 1024]> linear_165_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_q_weight_to_fp16_palettized, x = query_37_cast_fp16)[name = string("linear_165_cast_fp16")];
+            tensor<int32, [4]> var_3657 = const()[name = string("op_3657"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_109_cast_fp16 = reshape(shape = var_3657, x = linear_165_cast_fp16)[name = string("q_109_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229068480))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229592832))))[name = string("encoder_module_layers_18_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229592960)))];
+            tensor<fp16, [1, 188, 1024]> linear_166_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_k_weight_to_fp16_palettized, x = query_37_cast_fp16)[name = string("linear_166_cast_fp16")];
+            tensor<int32, [4]> var_3662 = const()[name = string("op_3662"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_73_cast_fp16 = reshape(shape = var_3662, x = linear_166_cast_fp16)[name = string("k_73_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229595072))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230119424))))[name = string("encoder_module_layers_18_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230119552)))];
+            tensor<fp16, [1, 188, 1024]> linear_167_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_v_weight_to_fp16_palettized, x = query_37_cast_fp16)[name = string("linear_167_cast_fp16")];
+            tensor<int32, [4]> var_3667 = const()[name = string("op_3667"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_37_cast_fp16 = reshape(shape = var_3667, x = linear_167_cast_fp16)[name = string("v_37_cast_fp16")];
+            tensor<int32, [4]> value_41_perm_0 = const()[name = string("value_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230121664)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3679_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_module_layers_18_self_attn_pos_bias_u_to_fp16)[name = string("op_3679_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230123776)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3681_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_module_layers_18_self_attn_pos_bias_v_to_fp16)[name = string("op_3681_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_37_perm_0 = const()[name = string("q_with_bias_v_37_perm_0"), val = tensor<int32, [4]>([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<fp16, [1, 8, 128, 375]> op_3683_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230125888))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230317952))))[name = string("op_3683_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3681_cast_fp16)[name = string("transpose_186")];
+            tensor<fp16, [1, 8, 188, 375]> x_423_cast_fp16 = matmul(transpose_x = x_423_transpose_x_0, transpose_y = x_423_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = op_3683_to_fp16_palettized)[name = string("x_423_cast_fp16")];
+            tensor<int32, [8]> x_425_pad_0 = const()[name = string("x_425_pad_0"), val = tensor<int32, [8]>([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_268_to_fp16 = const()[name = string("const_268_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_425_cast_fp16 = pad(constant_val = const_268_to_fp16, mode = x_425_mode_0, pad = x_425_pad_0, x = x_423_cast_fp16)[name = string("x_425_cast_fp16")];
+            tensor<int32, [4]> var_3691 = const()[name = string("op_3691"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_427_cast_fp16 = reshape(shape = var_3691, x = x_425_cast_fp16)[name = string("x_427_cast_fp16")];
+            tensor<int32, [4]> var_3695_begin_0 = const()[name = string("op_3695_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3695_end_0 = const()[name = string("op_3695_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3695_end_mask_0 = const()[name = string("op_3695_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3695_cast_fp16 = slice_by_index(begin = var_3695_begin_0, end = var_3695_end_0, end_mask = var_3695_end_mask_0, x = x_427_cast_fp16)[name = string("op_3695_cast_fp16")];
+            tensor<int32, [4]> var_3696 = const()[name = string("op_3696"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_73_cast_fp16 = reshape(shape = var_3696, x = var_3695_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<int32, [4]> transpose_132_perm_0 = const()[name = string("transpose_132_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_133_perm_0 = const()[name = string("transpose_133_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = string("transpose_184")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_132 = transpose(perm = transpose_132_perm_0, x = var_3679_cast_fp16)[name = string("transpose_185")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_75_begin_0 = const()[name = string("matrix_bd_75_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_75_end_0 = const()[name = string("matrix_bd_75_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_75_end_mask_0 = const()[name = string("matrix_bd_75_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3705_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = string("op_3705_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<fp16, [1, 8, 188, 188]> _inversed_scores_73_cast_fp16 = mul(x = var_3705_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = string("_inversed_scores_73_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_75_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_15)[name = string("scores_75_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3711_cast_fp16 = softmax(axis = var_152, x = scores_75_cast_fp16)[name = string("op_3711_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_977_cast_fp16 = select(a = var_164_to_fp16, b = var_3711_cast_fp16, cond = mask_15)[name = string("input_977_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<fp16, [1, 8, 188, 128]> value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_37_cast_fp16)[name = string("transpose_183")];
+            tensor<fp16, [1, 8, 188, 128]> x_429_cast_fp16 = matmul(transpose_x = x_429_transpose_x_0, transpose_y = x_429_transpose_y_0, x = input_977_cast_fp16, y = value_41_cast_fp16)[name = string("x_429_cast_fp16")];
+            tensor<int32, [4]> var_3715_perm_0 = const()[name = string("op_3715_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3716 = const()[name = string("op_3716"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3715_cast_fp16 = transpose(perm = var_3715_perm_0, x = x_429_cast_fp16)[name = string("transpose_182")];
+            tensor<fp16, [1, 188, 1024]> input_979_cast_fp16 = reshape(shape = var_3716, x = var_3715_cast_fp16)[name = string("input_979_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230318080))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230842432))))[name = string("encoder_module_layers_18_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230842560)))];
+            tensor<fp16, [1, 188, 1024]> linear_169_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_out_weight_to_fp16_palettized, x = input_979_cast_fp16)[name = string("linear_169_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_983_cast_fp16 = add(x = input_975_cast_fp16, y = linear_169_cast_fp16)[name = string("input_983_cast_fp16")];
+            tensor<int32, [1]> x_433_axes_0 = const()[name = string("x_433_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230844672)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230846784)))];
+            tensor<fp16, [1, 188, 1024]> x_433_cast_fp16 = layer_norm(axes = x_433_axes_0, beta = encoder_module_layers_18_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_conv_weight_to_fp16, x = input_983_cast_fp16)[name = string("x_433_cast_fp16")];
+            tensor<int32, [3]> input_985_perm_0 = const()[name = string("input_985_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_987_pad_type_0 = const()[name = string("input_987_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_987_strides_0 = const()[name = string("input_987_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_987_pad_0 = const()[name = string("input_987_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_987_dilations_0 = const()[name = string("input_987_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_987_groups_0 = const()[name = string("input_987_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_18_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230848896))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231897536))))[name = string("encoder_module_layers_18_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231897664)))];
+            tensor<fp16, [1, 1024, 188]> input_985_cast_fp16 = transpose(perm = input_985_perm_0, x = x_433_cast_fp16)[name = string("transpose_181")];
+            tensor<fp16, [1, 2048, 188]> input_987_cast_fp16 = conv(bias = encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_18_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_985_cast_fp16)[name = string("input_987_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<fp16, [1, 1024, 188]> x_435_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_435_split_cast_fp16_1 = split(axis = x_435_split_axis_0, num_splits = x_435_split_num_splits_0, x = input_987_cast_fp16)[name = string("x_435_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> input_989_cast_fp16 = select(a = var_164_to_fp16, b = x_435_cast_fp16, cond = var_608)[name = string("input_989_cast_fp16")];
+            tensor<int32, [6]> input_991_pad_0 = const()[name = string("input_991_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_991_mode_0 = const()[name = string("input_991_mode_0"), val = string("constant")];
+            fp16 const_271_to_fp16 = const()[name = string("const_271_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_991_cast_fp16 = pad(constant_val = const_271_to_fp16, mode = input_991_mode_0, pad = input_991_pad_0, x = input_989_cast_fp16)[name = string("input_991_cast_fp16")];
+            string input_993_pad_type_0 = const()[name = string("input_993_pad_type_0"), val = string("valid")];
+            int32 input_993_groups_0 = const()[name = string("input_993_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_993_strides_0 = const()[name = string("input_993_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_993_pad_0 = const()[name = string("input_993_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_993_dilations_0 = const()[name = string("input_993_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_358_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231901824))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231906496))))[name = string("const_358_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_359_to_fp16 = const()[name = string("const_359_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231906624)))];
+            tensor<fp16, [1, 1024, 188]> input_995_cast_fp16 = conv(bias = const_359_to_fp16, dilations = input_993_dilations_0, groups = input_993_groups_0, pad = input_993_pad_0, pad_type = input_993_pad_type_0, strides = input_993_strides_0, weight = const_358_to_fp16_palettized, x = input_991_cast_fp16)[name = string("input_995_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_997_cast_fp16 = silu(x = input_995_cast_fp16)[name = string("input_997_cast_fp16")];
+            string x_437_pad_type_0 = const()[name = string("x_437_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_437_strides_0 = const()[name = string("x_437_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_437_pad_0 = const()[name = string("x_437_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_437_dilations_0 = const()[name = string("x_437_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_437_groups_0 = const()[name = string("x_437_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_18_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231908736))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232433088))))[name = string("encoder_module_layers_18_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232433216)))];
+            tensor<fp16, [1, 1024, 188]> x_437_cast_fp16 = conv(bias = encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_18_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_997_cast_fp16)[name = string("x_437_cast_fp16")];
+            tensor<int32, [3]> input_999_perm_0 = const()[name = string("input_999_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_999_cast_fp16 = transpose(perm = input_999_perm_0, x = x_437_cast_fp16)[name = string("transpose_180")];
+            tensor<fp16, [1, 188, 1024]> input_1001_cast_fp16 = add(x = input_983_cast_fp16, y = input_999_cast_fp16)[name = string("input_1001_cast_fp16")];
+            tensor<int32, [1]> input_1003_axes_0 = const()[name = string("input_1003_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232435328)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232437440)))];
+            tensor<fp16, [1, 188, 1024]> input_1003_cast_fp16 = layer_norm(axes = input_1003_axes_0, beta = encoder_module_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_feed_forward2_weight_to_fp16, x = input_1001_cast_fp16)[name = string("input_1003_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232439552))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234536768))))[name = string("encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234536896)))];
+            tensor<fp16, [1, 188, 4096]> linear_170_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1003_cast_fp16)[name = string("linear_170_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1007_cast_fp16 = silu(x = linear_170_cast_fp16)[name = string("input_1007_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234545152))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236642368))))[name = string("encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236642496)))];
+            tensor<fp16, [1, 188, 1024]> linear_171_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1007_cast_fp16)[name = string("linear_171_cast_fp16")];
+            fp16 var_3782_to_fp16 = const()[name = string("op_3782_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3783_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_3782_to_fp16)[name = string("op_3783_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1013_cast_fp16 = add(x = input_1001_cast_fp16, y = var_3783_cast_fp16)[name = string("input_1013_cast_fp16")];
+            tensor<int32, [1]> input_1015_axes_0 = const()[name = string("input_1015_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236644608)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236646720)))];
+            tensor<fp16, [1, 188, 1024]> input_1015_cast_fp16 = layer_norm(axes = input_1015_axes_0, beta = encoder_module_layers_18_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_out_weight_to_fp16, x = input_1013_cast_fp16)[name = string("input_1015_cast_fp16")];
+            tensor<int32, [1]> input_1017_axes_0 = const()[name = string("input_1017_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236648832)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236650944)))];
+            tensor<fp16, [1, 188, 1024]> input_1017_cast_fp16 = layer_norm(axes = input_1017_axes_0, beta = encoder_module_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1015_cast_fp16)[name = string("input_1017_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236653056))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238750272))))[name = string("encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238750400)))];
+            tensor<fp16, [1, 188, 4096]> linear_172_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1017_cast_fp16)[name = string("linear_172_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1021_cast_fp16 = silu(x = linear_172_cast_fp16)[name = string("input_1021_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238758656))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240855872))))[name = string("encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240856000)))];
+            tensor<fp16, [1, 188, 1024]> linear_173_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1021_cast_fp16)[name = string("linear_173_cast_fp16")];
+            fp16 var_3813_to_fp16 = const()[name = string("op_3813_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3814_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3813_to_fp16)[name = string("op_3814_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1027_cast_fp16 = add(x = input_1015_cast_fp16, y = var_3814_cast_fp16)[name = string("input_1027_cast_fp16")];
+            tensor<int32, [1]> query_39_axes_0 = const()[name = string("query_39_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240858112)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240860224)))];
+            tensor<fp16, [1, 188, 1024]> query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = encoder_module_layers_19_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_self_att_weight_to_fp16, x = input_1027_cast_fp16)[name = string("query_39_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240862336))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241386688))))[name = string("encoder_module_layers_19_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241386816)))];
+            tensor<fp16, [1, 188, 1024]> linear_174_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_q_weight_to_fp16_palettized, x = query_39_cast_fp16)[name = string("linear_174_cast_fp16")];
+            tensor<int32, [4]> var_3831 = const()[name = string("op_3831"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_115_cast_fp16 = reshape(shape = var_3831, x = linear_174_cast_fp16)[name = string("q_115_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241388928))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241913280))))[name = string("encoder_module_layers_19_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241913408)))];
+            tensor<fp16, [1, 188, 1024]> linear_175_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_k_weight_to_fp16_palettized, x = query_39_cast_fp16)[name = string("linear_175_cast_fp16")];
+            tensor<int32, [4]> var_3836 = const()[name = string("op_3836"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_77_cast_fp16 = reshape(shape = var_3836, x = linear_175_cast_fp16)[name = string("k_77_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241915520))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242439872))))[name = string("encoder_module_layers_19_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242440000)))];
+            tensor<fp16, [1, 188, 1024]> linear_176_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_v_weight_to_fp16_palettized, x = query_39_cast_fp16)[name = string("linear_176_cast_fp16")];
+            tensor<int32, [4]> var_3841 = const()[name = string("op_3841"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_39_cast_fp16 = reshape(shape = var_3841, x = linear_176_cast_fp16)[name = string("v_39_cast_fp16")];
+            tensor<int32, [4]> value_43_perm_0 = const()[name = string("value_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242442112)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3853_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_module_layers_19_self_attn_pos_bias_u_to_fp16)[name = string("op_3853_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242444224)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3855_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_module_layers_19_self_attn_pos_bias_v_to_fp16)[name = string("op_3855_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_39_perm_0 = const()[name = string("q_with_bias_v_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_445_transpose_x_0 = const()[name = string("x_445_transpose_x_0"), val = bool(false)];
+            bool x_445_transpose_y_0 = const()[name = string("x_445_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3857_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242446336))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242638400))))[name = string("op_3857_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3855_cast_fp16)[name = string("transpose_179")];
+            tensor<fp16, [1, 8, 188, 375]> x_445_cast_fp16 = matmul(transpose_x = x_445_transpose_x_0, transpose_y = x_445_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = op_3857_to_fp16_palettized)[name = string("x_445_cast_fp16")];
+            tensor<int32, [8]> x_447_pad_0 = const()[name = string("x_447_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_447_mode_0 = const()[name = string("x_447_mode_0"), val = string("constant")];
+            fp16 const_278_to_fp16 = const()[name = string("const_278_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_447_cast_fp16 = pad(constant_val = const_278_to_fp16, mode = x_447_mode_0, pad = x_447_pad_0, x = x_445_cast_fp16)[name = string("x_447_cast_fp16")];
+            tensor<int32, [4]> var_3865 = const()[name = string("op_3865"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_449_cast_fp16 = reshape(shape = var_3865, x = x_447_cast_fp16)[name = string("x_449_cast_fp16")];
+            tensor<int32, [4]> var_3869_begin_0 = const()[name = string("op_3869_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3869_end_0 = const()[name = string("op_3869_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3869_end_mask_0 = const()[name = string("op_3869_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3869_cast_fp16 = slice_by_index(begin = var_3869_begin_0, end = var_3869_end_0, end_mask = var_3869_end_mask_0, x = x_449_cast_fp16)[name = string("op_3869_cast_fp16")];
+            tensor<int32, [4]> var_3870 = const()[name = string("op_3870"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_77_cast_fp16 = reshape(shape = var_3870, x = var_3869_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<int32, [4]> transpose_134_perm_0 = const()[name = string("transpose_134_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_135_perm_0 = const()[name = string("transpose_135_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = string("transpose_177")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_134 = transpose(perm = transpose_134_perm_0, x = var_3853_cast_fp16)[name = string("transpose_178")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_79_begin_0 = const()[name = string("matrix_bd_79_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_79_end_0 = const()[name = string("matrix_bd_79_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_79_end_mask_0 = const()[name = string("matrix_bd_79_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3879_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = string("op_3879_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<fp16, [1, 8, 188, 188]> _inversed_scores_77_cast_fp16 = mul(x = var_3879_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = string("_inversed_scores_77_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_79_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_15)[name = string("scores_79_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3885_cast_fp16 = softmax(axis = var_152, x = scores_79_cast_fp16)[name = string("op_3885_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1029_cast_fp16 = select(a = var_164_to_fp16, b = var_3885_cast_fp16, cond = mask_15)[name = string("input_1029_cast_fp16")];
+            bool x_451_transpose_x_0 = const()[name = string("x_451_transpose_x_0"), val = bool(false)];
+            bool x_451_transpose_y_0 = const()[name = string("x_451_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_39_cast_fp16)[name = string("transpose_176")];
+            tensor<fp16, [1, 8, 188, 128]> x_451_cast_fp16 = matmul(transpose_x = x_451_transpose_x_0, transpose_y = x_451_transpose_y_0, x = input_1029_cast_fp16, y = value_43_cast_fp16)[name = string("x_451_cast_fp16")];
+            tensor<int32, [4]> var_3889_perm_0 = const()[name = string("op_3889_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3890 = const()[name = string("op_3890"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3889_cast_fp16 = transpose(perm = var_3889_perm_0, x = x_451_cast_fp16)[name = string("transpose_175")];
+            tensor<fp16, [1, 188, 1024]> input_1031_cast_fp16 = reshape(shape = var_3890, x = var_3889_cast_fp16)[name = string("input_1031_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242638528))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243162880))))[name = string("encoder_module_layers_19_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243163008)))];
+            tensor<fp16, [1, 188, 1024]> linear_178_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_out_weight_to_fp16_palettized, x = input_1031_cast_fp16)[name = string("linear_178_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1035_cast_fp16 = add(x = input_1027_cast_fp16, y = linear_178_cast_fp16)[name = string("input_1035_cast_fp16")];
+            tensor<int32, [1]> x_455_axes_0 = const()[name = string("x_455_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243165120)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243167232)))];
+            tensor<fp16, [1, 188, 1024]> x_455_cast_fp16 = layer_norm(axes = x_455_axes_0, beta = encoder_module_layers_19_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_conv_weight_to_fp16, x = input_1035_cast_fp16)[name = string("x_455_cast_fp16")];
+            tensor<int32, [3]> input_1037_perm_0 = const()[name = string("input_1037_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1039_pad_type_0 = const()[name = string("input_1039_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1039_strides_0 = const()[name = string("input_1039_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1039_pad_0 = const()[name = string("input_1039_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1039_dilations_0 = const()[name = string("input_1039_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1039_groups_0 = const()[name = string("input_1039_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_19_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243169344))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244217984))))[name = string("encoder_module_layers_19_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244218112)))];
+            tensor<fp16, [1, 1024, 188]> input_1037_cast_fp16 = transpose(perm = input_1037_perm_0, x = x_455_cast_fp16)[name = string("transpose_174")];
+            tensor<fp16, [1, 2048, 188]> input_1039_cast_fp16 = conv(bias = encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_19_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1037_cast_fp16)[name = string("input_1039_cast_fp16")];
+            int32 x_457_split_num_splits_0 = const()[name = string("x_457_split_num_splits_0"), val = int32(2)];
+            int32 x_457_split_axis_0 = const()[name = string("x_457_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_457_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_457_split_cast_fp16_1 = split(axis = x_457_split_axis_0, num_splits = x_457_split_num_splits_0, x = input_1039_cast_fp16)[name = string("x_457_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_457_split_1_sigmoid_cast_fp16 = sigmoid(x = x_457_split_cast_fp16_1)[name = string("x_457_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_457_cast_fp16 = mul(x = x_457_split_cast_fp16_0, y = x_457_split_1_sigmoid_cast_fp16)[name = string("x_457_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1041_cast_fp16 = select(a = var_164_to_fp16, b = x_457_cast_fp16, cond = var_608)[name = string("input_1041_cast_fp16")];
+            tensor<int32, [6]> input_1043_pad_0 = const()[name = string("input_1043_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1043_mode_0 = const()[name = string("input_1043_mode_0"), val = string("constant")];
+            fp16 const_281_to_fp16 = const()[name = string("const_281_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1043_cast_fp16 = pad(constant_val = const_281_to_fp16, mode = input_1043_mode_0, pad = input_1043_pad_0, x = input_1041_cast_fp16)[name = string("input_1043_cast_fp16")];
+            string input_1045_pad_type_0 = const()[name = string("input_1045_pad_type_0"), val = string("valid")];
+            int32 input_1045_groups_0 = const()[name = string("input_1045_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1045_strides_0 = const()[name = string("input_1045_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1045_pad_0 = const()[name = string("input_1045_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1045_dilations_0 = const()[name = string("input_1045_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_360_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244222272))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244226944))))[name = string("const_360_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_361_to_fp16 = const()[name = string("const_361_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244227072)))];
+            tensor<fp16, [1, 1024, 188]> input_1047_cast_fp16 = conv(bias = const_361_to_fp16, dilations = input_1045_dilations_0, groups = input_1045_groups_0, pad = input_1045_pad_0, pad_type = input_1045_pad_type_0, strides = input_1045_strides_0, weight = const_360_to_fp16_palettized, x = input_1043_cast_fp16)[name = string("input_1047_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1049_cast_fp16 = silu(x = input_1047_cast_fp16)[name = string("input_1049_cast_fp16")];
+            string x_459_pad_type_0 = const()[name = string("x_459_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_459_strides_0 = const()[name = string("x_459_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_459_pad_0 = const()[name = string("x_459_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_459_dilations_0 = const()[name = string("x_459_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_459_groups_0 = const()[name = string("x_459_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244229184))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244753536))))[name = string("encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244753664)))];
+            tensor<fp16, [1, 1024, 188]> x_459_cast_fp16 = conv(bias = encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16, dilations = x_459_dilations_0, groups = x_459_groups_0, pad = x_459_pad_0, pad_type = x_459_pad_type_0, strides = x_459_strides_0, weight = encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1049_cast_fp16)[name = string("x_459_cast_fp16")];
+            tensor<int32, [3]> input_1051_perm_0 = const()[name = string("input_1051_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1051_cast_fp16 = transpose(perm = input_1051_perm_0, x = x_459_cast_fp16)[name = string("transpose_173")];
+            tensor<fp16, [1, 188, 1024]> input_1053_cast_fp16 = add(x = input_1035_cast_fp16, y = input_1051_cast_fp16)[name = string("input_1053_cast_fp16")];
+            tensor<int32, [1]> input_1055_axes_0 = const()[name = string("input_1055_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244755776)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244757888)))];
+            tensor<fp16, [1, 188, 1024]> input_1055_cast_fp16 = layer_norm(axes = input_1055_axes_0, beta = encoder_module_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1053_cast_fp16)[name = string("input_1055_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244760000))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246857216))))[name = string("encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246857344)))];
+            tensor<fp16, [1, 188, 4096]> linear_179_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1055_cast_fp16)[name = string("linear_179_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1059_cast_fp16 = silu(x = linear_179_cast_fp16)[name = string("input_1059_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246865600))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248962816))))[name = string("encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248962944)))];
+            tensor<fp16, [1, 188, 1024]> linear_180_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1059_cast_fp16)[name = string("linear_180_cast_fp16")];
+            fp16 var_3956_to_fp16 = const()[name = string("op_3956_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3957_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_3956_to_fp16)[name = string("op_3957_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1065_cast_fp16 = add(x = input_1053_cast_fp16, y = var_3957_cast_fp16)[name = string("input_1065_cast_fp16")];
+            tensor<int32, [1]> input_1067_axes_0 = const()[name = string("input_1067_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248965056)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248967168)))];
+            tensor<fp16, [1, 188, 1024]> input_1067_cast_fp16 = layer_norm(axes = input_1067_axes_0, beta = encoder_module_layers_19_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_out_weight_to_fp16, x = input_1065_cast_fp16)[name = string("input_1067_cast_fp16")];
+            tensor<int32, [1]> input_1069_axes_0 = const()[name = string("input_1069_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248969280)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248971392)))];
+            tensor<fp16, [1, 188, 1024]> input_1069_cast_fp16 = layer_norm(axes = input_1069_axes_0, beta = encoder_module_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1067_cast_fp16)[name = string("input_1069_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248973504))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251070720))))[name = string("encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251070848)))];
+            tensor<fp16, [1, 188, 4096]> linear_181_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1069_cast_fp16)[name = string("linear_181_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1073_cast_fp16 = silu(x = linear_181_cast_fp16)[name = string("input_1073_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251079104))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253176320))))[name = string("encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253176448)))];
+            tensor<fp16, [1, 188, 1024]> linear_182_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1073_cast_fp16)[name = string("linear_182_cast_fp16")];
+            fp16 var_3987_to_fp16 = const()[name = string("op_3987_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3988_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3987_to_fp16)[name = string("op_3988_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1079_cast_fp16 = add(x = input_1067_cast_fp16, y = var_3988_cast_fp16)[name = string("input_1079_cast_fp16")];
+            tensor<int32, [1]> query_41_axes_0 = const()[name = string("query_41_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253178560)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253180672)))];
+            tensor<fp16, [1, 188, 1024]> query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = encoder_module_layers_20_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_self_att_weight_to_fp16, x = input_1079_cast_fp16)[name = string("query_41_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253182784))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253707136))))[name = string("encoder_module_layers_20_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253707264)))];
+            tensor<fp16, [1, 188, 1024]> linear_183_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_q_weight_to_fp16_palettized, x = query_41_cast_fp16)[name = string("linear_183_cast_fp16")];
+            tensor<int32, [4]> var_4005 = const()[name = string("op_4005"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_121_cast_fp16 = reshape(shape = var_4005, x = linear_183_cast_fp16)[name = string("q_121_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(253709376))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254233728))))[name = string("encoder_module_layers_20_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254233856)))];
+            tensor<fp16, [1, 188, 1024]> linear_184_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_k_weight_to_fp16_palettized, x = query_41_cast_fp16)[name = string("linear_184_cast_fp16")];
+            tensor<int32, [4]> var_4010 = const()[name = string("op_4010"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_81_cast_fp16 = reshape(shape = var_4010, x = linear_184_cast_fp16)[name = string("k_81_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254235968))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254760320))))[name = string("encoder_module_layers_20_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254760448)))];
+            tensor<fp16, [1, 188, 1024]> linear_185_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_v_weight_to_fp16_palettized, x = query_41_cast_fp16)[name = string("linear_185_cast_fp16")];
+            tensor<int32, [4]> var_4015 = const()[name = string("op_4015"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_41_cast_fp16 = reshape(shape = var_4015, x = linear_185_cast_fp16)[name = string("v_41_cast_fp16")];
+            tensor<int32, [4]> value_45_perm_0 = const()[name = string("value_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254762560)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4027_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_module_layers_20_self_attn_pos_bias_u_to_fp16)[name = string("op_4027_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254764672)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4029_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_module_layers_20_self_attn_pos_bias_v_to_fp16)[name = string("op_4029_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_41_perm_0 = const()[name = string("q_with_bias_v_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_467_transpose_x_0 = const()[name = string("x_467_transpose_x_0"), val = bool(false)];
+            bool x_467_transpose_y_0 = const()[name = string("x_467_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_4031_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254766784))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254958848))))[name = string("op_4031_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_4029_cast_fp16)[name = string("transpose_172")];
+            tensor<fp16, [1, 8, 188, 375]> x_467_cast_fp16 = matmul(transpose_x = x_467_transpose_x_0, transpose_y = x_467_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = op_4031_to_fp16_palettized)[name = string("x_467_cast_fp16")];
+            tensor<int32, [8]> x_469_pad_0 = const()[name = string("x_469_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_469_mode_0 = const()[name = string("x_469_mode_0"), val = string("constant")];
+            fp16 const_288_to_fp16 = const()[name = string("const_288_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_469_cast_fp16 = pad(constant_val = const_288_to_fp16, mode = x_469_mode_0, pad = x_469_pad_0, x = x_467_cast_fp16)[name = string("x_469_cast_fp16")];
+            tensor<int32, [4]> var_4039 = const()[name = string("op_4039"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_471_cast_fp16 = reshape(shape = var_4039, x = x_469_cast_fp16)[name = string("x_471_cast_fp16")];
+            tensor<int32, [4]> var_4043_begin_0 = const()[name = string("op_4043_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4043_end_0 = const()[name = string("op_4043_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4043_end_mask_0 = const()[name = string("op_4043_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4043_cast_fp16 = slice_by_index(begin = var_4043_begin_0, end = var_4043_end_0, end_mask = var_4043_end_mask_0, x = x_471_cast_fp16)[name = string("op_4043_cast_fp16")];
+            tensor<int32, [4]> var_4044 = const()[name = string("op_4044"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_81_cast_fp16 = reshape(shape = var_4044, x = var_4043_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<int32, [4]> transpose_136_perm_0 = const()[name = string("transpose_136_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_137_perm_0 = const()[name = string("transpose_137_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = string("transpose_170")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_136 = transpose(perm = transpose_136_perm_0, x = var_4027_cast_fp16)[name = string("transpose_171")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_83_begin_0 = const()[name = string("matrix_bd_83_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_83_end_0 = const()[name = string("matrix_bd_83_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_83_end_mask_0 = const()[name = string("matrix_bd_83_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4053_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = string("op_4053_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<fp16, [1, 8, 188, 188]> _inversed_scores_81_cast_fp16 = mul(x = var_4053_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = string("_inversed_scores_81_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_83_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_15)[name = string("scores_83_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4059_cast_fp16 = softmax(axis = var_152, x = scores_83_cast_fp16)[name = string("op_4059_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1081_cast_fp16 = select(a = var_164_to_fp16, b = var_4059_cast_fp16, cond = mask_15)[name = string("input_1081_cast_fp16")];
+            bool x_473_transpose_x_0 = const()[name = string("x_473_transpose_x_0"), val = bool(false)];
+            bool x_473_transpose_y_0 = const()[name = string("x_473_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_41_cast_fp16)[name = string("transpose_169")];
+            tensor<fp16, [1, 8, 188, 128]> x_473_cast_fp16 = matmul(transpose_x = x_473_transpose_x_0, transpose_y = x_473_transpose_y_0, x = input_1081_cast_fp16, y = value_45_cast_fp16)[name = string("x_473_cast_fp16")];
+            tensor<int32, [4]> var_4063_perm_0 = const()[name = string("op_4063_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4064 = const()[name = string("op_4064"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4063_cast_fp16 = transpose(perm = var_4063_perm_0, x = x_473_cast_fp16)[name = string("transpose_168")];
+            tensor<fp16, [1, 188, 1024]> input_1083_cast_fp16 = reshape(shape = var_4064, x = var_4063_cast_fp16)[name = string("input_1083_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254958976))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255483328))))[name = string("encoder_module_layers_20_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255483456)))];
+            tensor<fp16, [1, 188, 1024]> linear_187_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_out_weight_to_fp16_palettized, x = input_1083_cast_fp16)[name = string("linear_187_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1087_cast_fp16 = add(x = input_1079_cast_fp16, y = linear_187_cast_fp16)[name = string("input_1087_cast_fp16")];
+            tensor<int32, [1]> x_477_axes_0 = const()[name = string("x_477_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255485568)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255487680)))];
+            tensor<fp16, [1, 188, 1024]> x_477_cast_fp16 = layer_norm(axes = x_477_axes_0, beta = encoder_module_layers_20_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_conv_weight_to_fp16, x = input_1087_cast_fp16)[name = string("x_477_cast_fp16")];
+            tensor<int32, [3]> input_1089_perm_0 = const()[name = string("input_1089_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1091_pad_type_0 = const()[name = string("input_1091_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1091_strides_0 = const()[name = string("input_1091_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1091_pad_0 = const()[name = string("input_1091_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1091_dilations_0 = const()[name = string("input_1091_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1091_groups_0 = const()[name = string("input_1091_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_20_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255489792))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256538432))))[name = string("encoder_module_layers_20_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256538560)))];
+            tensor<fp16, [1, 1024, 188]> input_1089_cast_fp16 = transpose(perm = input_1089_perm_0, x = x_477_cast_fp16)[name = string("transpose_167")];
+            tensor<fp16, [1, 2048, 188]> input_1091_cast_fp16 = conv(bias = encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_20_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1089_cast_fp16)[name = string("input_1091_cast_fp16")];
+            int32 x_479_split_num_splits_0 = const()[name = string("x_479_split_num_splits_0"), val = int32(2)];
+            int32 x_479_split_axis_0 = const()[name = string("x_479_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_479_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_479_split_cast_fp16_1 = split(axis = x_479_split_axis_0, num_splits = x_479_split_num_splits_0, x = input_1091_cast_fp16)[name = string("x_479_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_479_split_1_sigmoid_cast_fp16 = sigmoid(x = x_479_split_cast_fp16_1)[name = string("x_479_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_479_cast_fp16 = mul(x = x_479_split_cast_fp16_0, y = x_479_split_1_sigmoid_cast_fp16)[name = string("x_479_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1093_cast_fp16 = select(a = var_164_to_fp16, b = x_479_cast_fp16, cond = var_608)[name = string("input_1093_cast_fp16")];
+            tensor<int32, [6]> input_1095_pad_0 = const()[name = string("input_1095_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1095_mode_0 = const()[name = string("input_1095_mode_0"), val = string("constant")];
+            fp16 const_291_to_fp16 = const()[name = string("const_291_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1095_cast_fp16 = pad(constant_val = const_291_to_fp16, mode = input_1095_mode_0, pad = input_1095_pad_0, x = input_1093_cast_fp16)[name = string("input_1095_cast_fp16")];
+            string input_1097_pad_type_0 = const()[name = string("input_1097_pad_type_0"), val = string("valid")];
+            int32 input_1097_groups_0 = const()[name = string("input_1097_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1097_strides_0 = const()[name = string("input_1097_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1097_pad_0 = const()[name = string("input_1097_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1097_dilations_0 = const()[name = string("input_1097_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_362_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256542720))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256547392))))[name = string("const_362_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_363_to_fp16 = const()[name = string("const_363_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256547520)))];
+            tensor<fp16, [1, 1024, 188]> input_1099_cast_fp16 = conv(bias = const_363_to_fp16, dilations = input_1097_dilations_0, groups = input_1097_groups_0, pad = input_1097_pad_0, pad_type = input_1097_pad_type_0, strides = input_1097_strides_0, weight = const_362_to_fp16_palettized, x = input_1095_cast_fp16)[name = string("input_1099_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1101_cast_fp16 = silu(x = input_1099_cast_fp16)[name = string("input_1101_cast_fp16")];
+            string x_481_pad_type_0 = const()[name = string("x_481_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_481_strides_0 = const()[name = string("x_481_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_481_pad_0 = const()[name = string("x_481_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_481_dilations_0 = const()[name = string("x_481_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_481_groups_0 = const()[name = string("x_481_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256549632))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257073984))))[name = string("encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257074112)))];
+            tensor<fp16, [1, 1024, 188]> x_481_cast_fp16 = conv(bias = encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16, dilations = x_481_dilations_0, groups = x_481_groups_0, pad = x_481_pad_0, pad_type = x_481_pad_type_0, strides = x_481_strides_0, weight = encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1101_cast_fp16)[name = string("x_481_cast_fp16")];
+            tensor<int32, [3]> input_1103_perm_0 = const()[name = string("input_1103_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1103_cast_fp16 = transpose(perm = input_1103_perm_0, x = x_481_cast_fp16)[name = string("transpose_166")];
+            tensor<fp16, [1, 188, 1024]> input_1105_cast_fp16 = add(x = input_1087_cast_fp16, y = input_1103_cast_fp16)[name = string("input_1105_cast_fp16")];
+            tensor<int32, [1]> input_1107_axes_0 = const()[name = string("input_1107_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257076224)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257078336)))];
+            tensor<fp16, [1, 188, 1024]> input_1107_cast_fp16 = layer_norm(axes = input_1107_axes_0, beta = encoder_module_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1105_cast_fp16)[name = string("input_1107_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257080448))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259177664))))[name = string("encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259177792)))];
+            tensor<fp16, [1, 188, 4096]> linear_188_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1107_cast_fp16)[name = string("linear_188_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1111_cast_fp16 = silu(x = linear_188_cast_fp16)[name = string("input_1111_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259186048))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261283264))))[name = string("encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261283392)))];
+            tensor<fp16, [1, 188, 1024]> linear_189_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1111_cast_fp16)[name = string("linear_189_cast_fp16")];
+            fp16 var_4130_to_fp16 = const()[name = string("op_4130_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4131_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_4130_to_fp16)[name = string("op_4131_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1117_cast_fp16 = add(x = input_1105_cast_fp16, y = var_4131_cast_fp16)[name = string("input_1117_cast_fp16")];
+            tensor<int32, [1]> input_1119_axes_0 = const()[name = string("input_1119_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261285504)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261287616)))];
+            tensor<fp16, [1, 188, 1024]> input_1119_cast_fp16 = layer_norm(axes = input_1119_axes_0, beta = encoder_module_layers_20_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_out_weight_to_fp16, x = input_1117_cast_fp16)[name = string("input_1119_cast_fp16")];
+            tensor<int32, [1]> input_1121_axes_0 = const()[name = string("input_1121_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261289728)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261291840)))];
+            tensor<fp16, [1, 188, 1024]> input_1121_cast_fp16 = layer_norm(axes = input_1121_axes_0, beta = encoder_module_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1119_cast_fp16)[name = string("input_1121_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261293952))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263391168))))[name = string("encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263391296)))];
+            tensor<fp16, [1, 188, 4096]> linear_190_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1121_cast_fp16)[name = string("linear_190_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1125_cast_fp16 = silu(x = linear_190_cast_fp16)[name = string("input_1125_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263399552))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265496768))))[name = string("encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265496896)))];
+            tensor<fp16, [1, 188, 1024]> linear_191_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1125_cast_fp16)[name = string("linear_191_cast_fp16")];
+            fp16 var_4161_to_fp16 = const()[name = string("op_4161_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4162_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_4161_to_fp16)[name = string("op_4162_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1131_cast_fp16 = add(x = input_1119_cast_fp16, y = var_4162_cast_fp16)[name = string("input_1131_cast_fp16")];
+            tensor<int32, [1]> query_43_axes_0 = const()[name = string("query_43_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265499008)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265501120)))];
+            tensor<fp16, [1, 188, 1024]> query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = encoder_module_layers_21_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_self_att_weight_to_fp16, x = input_1131_cast_fp16)[name = string("query_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265503232))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266027584))))[name = string("encoder_module_layers_21_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266027712)))];
+            tensor<fp16, [1, 188, 1024]> linear_192_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_q_weight_to_fp16_palettized, x = query_43_cast_fp16)[name = string("linear_192_cast_fp16")];
+            tensor<int32, [4]> var_4179 = const()[name = string("op_4179"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_127_cast_fp16 = reshape(shape = var_4179, x = linear_192_cast_fp16)[name = string("q_127_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266029824))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266554176))))[name = string("encoder_module_layers_21_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266554304)))];
+            tensor<fp16, [1, 188, 1024]> linear_193_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_k_weight_to_fp16_palettized, x = query_43_cast_fp16)[name = string("linear_193_cast_fp16")];
+            tensor<int32, [4]> var_4184 = const()[name = string("op_4184"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_85_cast_fp16 = reshape(shape = var_4184, x = linear_193_cast_fp16)[name = string("k_85_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266556416))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267080768))))[name = string("encoder_module_layers_21_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267080896)))];
+            tensor<fp16, [1, 188, 1024]> linear_194_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_v_weight_to_fp16_palettized, x = query_43_cast_fp16)[name = string("linear_194_cast_fp16")];
+            tensor<int32, [4]> var_4189 = const()[name = string("op_4189"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_43_cast_fp16 = reshape(shape = var_4189, x = linear_194_cast_fp16)[name = string("v_43_cast_fp16")];
+            tensor<int32, [4]> value_47_perm_0 = const()[name = string("value_47_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267083008)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4201_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_module_layers_21_self_attn_pos_bias_u_to_fp16)[name = string("op_4201_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267085120)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4203_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_module_layers_21_self_attn_pos_bias_v_to_fp16)[name = string("op_4203_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_43_perm_0 = const()[name = string("q_with_bias_v_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_489_transpose_x_0 = const()[name = string("x_489_transpose_x_0"), val = bool(false)];
+            bool x_489_transpose_y_0 = const()[name = string("x_489_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_4205_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267087232))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267279296))))[name = string("op_4205_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_4203_cast_fp16)[name = string("transpose_165")];
+            tensor<fp16, [1, 8, 188, 375]> x_489_cast_fp16 = matmul(transpose_x = x_489_transpose_x_0, transpose_y = x_489_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = op_4205_to_fp16_palettized)[name = string("x_489_cast_fp16")];
+            tensor<int32, [8]> x_491_pad_0 = const()[name = string("x_491_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_491_mode_0 = const()[name = string("x_491_mode_0"), val = string("constant")];
+            fp16 const_298_to_fp16 = const()[name = string("const_298_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_491_cast_fp16 = pad(constant_val = const_298_to_fp16, mode = x_491_mode_0, pad = x_491_pad_0, x = x_489_cast_fp16)[name = string("x_491_cast_fp16")];
+            tensor<int32, [4]> var_4213 = const()[name = string("op_4213"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_493_cast_fp16 = reshape(shape = var_4213, x = x_491_cast_fp16)[name = string("x_493_cast_fp16")];
+            tensor<int32, [4]> var_4217_begin_0 = const()[name = string("op_4217_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4217_end_0 = const()[name = string("op_4217_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4217_end_mask_0 = const()[name = string("op_4217_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4217_cast_fp16 = slice_by_index(begin = var_4217_begin_0, end = var_4217_end_0, end_mask = var_4217_end_mask_0, x = x_493_cast_fp16)[name = string("op_4217_cast_fp16")];
+            tensor<int32, [4]> var_4218 = const()[name = string("op_4218"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_85_cast_fp16 = reshape(shape = var_4218, x = var_4217_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<int32, [4]> transpose_138_perm_0 = const()[name = string("transpose_138_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_139_perm_0 = const()[name = string("transpose_139_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = string("transpose_163")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_138 = transpose(perm = transpose_138_perm_0, x = var_4201_cast_fp16)[name = string("transpose_164")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_87_begin_0 = const()[name = string("matrix_bd_87_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_87_end_0 = const()[name = string("matrix_bd_87_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_87_end_mask_0 = const()[name = string("matrix_bd_87_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4227_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = string("op_4227_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<fp16, [1, 8, 188, 188]> _inversed_scores_85_cast_fp16 = mul(x = var_4227_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = string("_inversed_scores_85_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_87_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_15)[name = string("scores_87_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4233_cast_fp16 = softmax(axis = var_152, x = scores_87_cast_fp16)[name = string("op_4233_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1133_cast_fp16 = select(a = var_164_to_fp16, b = var_4233_cast_fp16, cond = mask_15)[name = string("input_1133_cast_fp16")];
+            bool x_495_transpose_x_0 = const()[name = string("x_495_transpose_x_0"), val = bool(false)];
+            bool x_495_transpose_y_0 = const()[name = string("x_495_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_43_cast_fp16)[name = string("transpose_162")];
+            tensor<fp16, [1, 8, 188, 128]> x_495_cast_fp16 = matmul(transpose_x = x_495_transpose_x_0, transpose_y = x_495_transpose_y_0, x = input_1133_cast_fp16, y = value_47_cast_fp16)[name = string("x_495_cast_fp16")];
+            tensor<int32, [4]> var_4237_perm_0 = const()[name = string("op_4237_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4238 = const()[name = string("op_4238"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4237_cast_fp16 = transpose(perm = var_4237_perm_0, x = x_495_cast_fp16)[name = string("transpose_161")];
+            tensor<fp16, [1, 188, 1024]> input_1135_cast_fp16 = reshape(shape = var_4238, x = var_4237_cast_fp16)[name = string("input_1135_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267279424))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267803776))))[name = string("encoder_module_layers_21_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267803904)))];
+            tensor<fp16, [1, 188, 1024]> linear_196_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_out_weight_to_fp16_palettized, x = input_1135_cast_fp16)[name = string("linear_196_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1139_cast_fp16 = add(x = input_1131_cast_fp16, y = linear_196_cast_fp16)[name = string("input_1139_cast_fp16")];
+            tensor<int32, [1]> x_499_axes_0 = const()[name = string("x_499_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267806016)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267808128)))];
+            tensor<fp16, [1, 188, 1024]> x_499_cast_fp16 = layer_norm(axes = x_499_axes_0, beta = encoder_module_layers_21_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_conv_weight_to_fp16, x = input_1139_cast_fp16)[name = string("x_499_cast_fp16")];
+            tensor<int32, [3]> input_1141_perm_0 = const()[name = string("input_1141_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1143_pad_type_0 = const()[name = string("input_1143_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1143_strides_0 = const()[name = string("input_1143_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1143_pad_0 = const()[name = string("input_1143_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1143_dilations_0 = const()[name = string("input_1143_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1143_groups_0 = const()[name = string("input_1143_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_21_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267810240))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268858880))))[name = string("encoder_module_layers_21_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268859008)))];
+            tensor<fp16, [1, 1024, 188]> input_1141_cast_fp16 = transpose(perm = input_1141_perm_0, x = x_499_cast_fp16)[name = string("transpose_160")];
+            tensor<fp16, [1, 2048, 188]> input_1143_cast_fp16 = conv(bias = encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_21_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1141_cast_fp16)[name = string("input_1143_cast_fp16")];
+            int32 x_501_split_num_splits_0 = const()[name = string("x_501_split_num_splits_0"), val = int32(2)];
+            int32 x_501_split_axis_0 = const()[name = string("x_501_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_501_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_501_split_cast_fp16_1 = split(axis = x_501_split_axis_0, num_splits = x_501_split_num_splits_0, x = input_1143_cast_fp16)[name = string("x_501_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_501_split_1_sigmoid_cast_fp16 = sigmoid(x = x_501_split_cast_fp16_1)[name = string("x_501_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_501_cast_fp16 = mul(x = x_501_split_cast_fp16_0, y = x_501_split_1_sigmoid_cast_fp16)[name = string("x_501_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1145_cast_fp16 = select(a = var_164_to_fp16, b = x_501_cast_fp16, cond = var_608)[name = string("input_1145_cast_fp16")];
+            tensor<int32, [6]> input_1147_pad_0 = const()[name = string("input_1147_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1147_mode_0 = const()[name = string("input_1147_mode_0"), val = string("constant")];
+            fp16 const_301_to_fp16 = const()[name = string("const_301_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1147_cast_fp16 = pad(constant_val = const_301_to_fp16, mode = input_1147_mode_0, pad = input_1147_pad_0, x = input_1145_cast_fp16)[name = string("input_1147_cast_fp16")];
+            string input_1149_pad_type_0 = const()[name = string("input_1149_pad_type_0"), val = string("valid")];
+            int32 input_1149_groups_0 = const()[name = string("input_1149_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1149_strides_0 = const()[name = string("input_1149_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1149_pad_0 = const()[name = string("input_1149_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1149_dilations_0 = const()[name = string("input_1149_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_364_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268863168))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268867840))))[name = string("const_364_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_365_to_fp16 = const()[name = string("const_365_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268867968)))];
+            tensor<fp16, [1, 1024, 188]> input_1151_cast_fp16 = conv(bias = const_365_to_fp16, dilations = input_1149_dilations_0, groups = input_1149_groups_0, pad = input_1149_pad_0, pad_type = input_1149_pad_type_0, strides = input_1149_strides_0, weight = const_364_to_fp16_palettized, x = input_1147_cast_fp16)[name = string("input_1151_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1153_cast_fp16 = silu(x = input_1151_cast_fp16)[name = string("input_1153_cast_fp16")];
+            string x_503_pad_type_0 = const()[name = string("x_503_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_503_strides_0 = const()[name = string("x_503_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_503_pad_0 = const()[name = string("x_503_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_503_dilations_0 = const()[name = string("x_503_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_503_groups_0 = const()[name = string("x_503_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268870080))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269394432))))[name = string("encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269394560)))];
+            tensor<fp16, [1, 1024, 188]> x_503_cast_fp16 = conv(bias = encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16, dilations = x_503_dilations_0, groups = x_503_groups_0, pad = x_503_pad_0, pad_type = x_503_pad_type_0, strides = x_503_strides_0, weight = encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1153_cast_fp16)[name = string("x_503_cast_fp16")];
+            tensor<int32, [3]> input_1155_perm_0 = const()[name = string("input_1155_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1155_cast_fp16 = transpose(perm = input_1155_perm_0, x = x_503_cast_fp16)[name = string("transpose_159")];
+            tensor<fp16, [1, 188, 1024]> input_1157_cast_fp16 = add(x = input_1139_cast_fp16, y = input_1155_cast_fp16)[name = string("input_1157_cast_fp16")];
+            tensor<int32, [1]> input_1159_axes_0 = const()[name = string("input_1159_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269396672)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269398784)))];
+            tensor<fp16, [1, 188, 1024]> input_1159_cast_fp16 = layer_norm(axes = input_1159_axes_0, beta = encoder_module_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1157_cast_fp16)[name = string("input_1159_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269400896))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271498112))))[name = string("encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271498240)))];
+            tensor<fp16, [1, 188, 4096]> linear_197_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1159_cast_fp16)[name = string("linear_197_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1163_cast_fp16 = silu(x = linear_197_cast_fp16)[name = string("input_1163_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271506496))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273603712))))[name = string("encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273603840)))];
+            tensor<fp16, [1, 188, 1024]> linear_198_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1163_cast_fp16)[name = string("linear_198_cast_fp16")];
+            fp16 var_4304_to_fp16 = const()[name = string("op_4304_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4305_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_4304_to_fp16)[name = string("op_4305_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1169_cast_fp16 = add(x = input_1157_cast_fp16, y = var_4305_cast_fp16)[name = string("input_1169_cast_fp16")];
+            tensor<int32, [1]> input_1171_axes_0 = const()[name = string("input_1171_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273605952)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273608064)))];
+            tensor<fp16, [1, 188, 1024]> input_1171_cast_fp16 = layer_norm(axes = input_1171_axes_0, beta = encoder_module_layers_21_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_out_weight_to_fp16, x = input_1169_cast_fp16)[name = string("input_1171_cast_fp16")];
+            tensor<int32, [1]> input_1173_axes_0 = const()[name = string("input_1173_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273610176)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273612288)))];
+            tensor<fp16, [1, 188, 1024]> input_1173_cast_fp16 = layer_norm(axes = input_1173_axes_0, beta = encoder_module_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1171_cast_fp16)[name = string("input_1173_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273614400))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275711616))))[name = string("encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275711744)))];
+            tensor<fp16, [1, 188, 4096]> linear_199_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1173_cast_fp16)[name = string("linear_199_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1177_cast_fp16 = silu(x = linear_199_cast_fp16)[name = string("input_1177_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275720000))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277817216))))[name = string("encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277817344)))];
+            tensor<fp16, [1, 188, 1024]> linear_200_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1177_cast_fp16)[name = string("linear_200_cast_fp16")];
+            fp16 var_4335_to_fp16 = const()[name = string("op_4335_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4336_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_4335_to_fp16)[name = string("op_4336_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1183_cast_fp16 = add(x = input_1171_cast_fp16, y = var_4336_cast_fp16)[name = string("input_1183_cast_fp16")];
+            tensor<int32, [1]> query_45_axes_0 = const()[name = string("query_45_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277819456)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277821568)))];
+            tensor<fp16, [1, 188, 1024]> query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = encoder_module_layers_22_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_self_att_weight_to_fp16, x = input_1183_cast_fp16)[name = string("query_45_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277823680))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278348032))))[name = string("encoder_module_layers_22_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278348160)))];
+            tensor<fp16, [1, 188, 1024]> linear_201_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_q_weight_to_fp16_palettized, x = query_45_cast_fp16)[name = string("linear_201_cast_fp16")];
+            tensor<int32, [4]> var_4353 = const()[name = string("op_4353"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_133_cast_fp16 = reshape(shape = var_4353, x = linear_201_cast_fp16)[name = string("q_133_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278350272))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278874624))))[name = string("encoder_module_layers_22_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278874752)))];
+            tensor<fp16, [1, 188, 1024]> linear_202_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_k_weight_to_fp16_palettized, x = query_45_cast_fp16)[name = string("linear_202_cast_fp16")];
+            tensor<int32, [4]> var_4358 = const()[name = string("op_4358"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_89_cast_fp16 = reshape(shape = var_4358, x = linear_202_cast_fp16)[name = string("k_89_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278876864))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279401216))))[name = string("encoder_module_layers_22_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279401344)))];
+            tensor<fp16, [1, 188, 1024]> linear_203_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_v_weight_to_fp16_palettized, x = query_45_cast_fp16)[name = string("linear_203_cast_fp16")];
+            tensor<int32, [4]> var_4363 = const()[name = string("op_4363"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_45_cast_fp16 = reshape(shape = var_4363, x = linear_203_cast_fp16)[name = string("v_45_cast_fp16")];
+            tensor<int32, [4]> value_49_perm_0 = const()[name = string("value_49_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279403456)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4375_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_module_layers_22_self_attn_pos_bias_u_to_fp16)[name = string("op_4375_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279405568)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4377_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_module_layers_22_self_attn_pos_bias_v_to_fp16)[name = string("op_4377_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_45_perm_0 = const()[name = string("q_with_bias_v_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_511_transpose_x_0 = const()[name = string("x_511_transpose_x_0"), val = bool(false)];
+            bool x_511_transpose_y_0 = const()[name = string("x_511_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_4379_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279407680))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279599744))))[name = string("op_4379_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_4377_cast_fp16)[name = string("transpose_158")];
+            tensor<fp16, [1, 8, 188, 375]> x_511_cast_fp16 = matmul(transpose_x = x_511_transpose_x_0, transpose_y = x_511_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = op_4379_to_fp16_palettized)[name = string("x_511_cast_fp16")];
+            tensor<int32, [8]> x_513_pad_0 = const()[name = string("x_513_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_513_mode_0 = const()[name = string("x_513_mode_0"), val = string("constant")];
+            fp16 const_308_to_fp16 = const()[name = string("const_308_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_513_cast_fp16 = pad(constant_val = const_308_to_fp16, mode = x_513_mode_0, pad = x_513_pad_0, x = x_511_cast_fp16)[name = string("x_513_cast_fp16")];
+            tensor<int32, [4]> var_4387 = const()[name = string("op_4387"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_515_cast_fp16 = reshape(shape = var_4387, x = x_513_cast_fp16)[name = string("x_515_cast_fp16")];
+            tensor<int32, [4]> var_4391_begin_0 = const()[name = string("op_4391_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4391_end_0 = const()[name = string("op_4391_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4391_end_mask_0 = const()[name = string("op_4391_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4391_cast_fp16 = slice_by_index(begin = var_4391_begin_0, end = var_4391_end_0, end_mask = var_4391_end_mask_0, x = x_515_cast_fp16)[name = string("op_4391_cast_fp16")];
+            tensor<int32, [4]> var_4392 = const()[name = string("op_4392"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_89_cast_fp16 = reshape(shape = var_4392, x = var_4391_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<int32, [4]> transpose_140_perm_0 = const()[name = string("transpose_140_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_141_perm_0 = const()[name = string("transpose_141_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = string("transpose_156")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_140 = transpose(perm = transpose_140_perm_0, x = var_4375_cast_fp16)[name = string("transpose_157")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_91_begin_0 = const()[name = string("matrix_bd_91_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_91_end_0 = const()[name = string("matrix_bd_91_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_91_end_mask_0 = const()[name = string("matrix_bd_91_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4401_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = string("op_4401_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<fp16, [1, 8, 188, 188]> _inversed_scores_89_cast_fp16 = mul(x = var_4401_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = string("_inversed_scores_89_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_91_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_15)[name = string("scores_91_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4407_cast_fp16 = softmax(axis = var_152, x = scores_91_cast_fp16)[name = string("op_4407_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1185_cast_fp16 = select(a = var_164_to_fp16, b = var_4407_cast_fp16, cond = mask_15)[name = string("input_1185_cast_fp16")];
+            bool x_517_transpose_x_0 = const()[name = string("x_517_transpose_x_0"), val = bool(false)];
+            bool x_517_transpose_y_0 = const()[name = string("x_517_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_49_cast_fp16 = transpose(perm = value_49_perm_0, x = v_45_cast_fp16)[name = string("transpose_155")];
+            tensor<fp16, [1, 8, 188, 128]> x_517_cast_fp16 = matmul(transpose_x = x_517_transpose_x_0, transpose_y = x_517_transpose_y_0, x = input_1185_cast_fp16, y = value_49_cast_fp16)[name = string("x_517_cast_fp16")];
+            tensor<int32, [4]> var_4411_perm_0 = const()[name = string("op_4411_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4412 = const()[name = string("op_4412"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4411_cast_fp16 = transpose(perm = var_4411_perm_0, x = x_517_cast_fp16)[name = string("transpose_154")];
+            tensor<fp16, [1, 188, 1024]> input_1187_cast_fp16 = reshape(shape = var_4412, x = var_4411_cast_fp16)[name = string("input_1187_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279599872))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280124224))))[name = string("encoder_module_layers_22_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280124352)))];
+            tensor<fp16, [1, 188, 1024]> linear_205_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_out_weight_to_fp16_palettized, x = input_1187_cast_fp16)[name = string("linear_205_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1191_cast_fp16 = add(x = input_1183_cast_fp16, y = linear_205_cast_fp16)[name = string("input_1191_cast_fp16")];
+            tensor<int32, [1]> x_521_axes_0 = const()[name = string("x_521_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280126464)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280128576)))];
+            tensor<fp16, [1, 188, 1024]> x_521_cast_fp16 = layer_norm(axes = x_521_axes_0, beta = encoder_module_layers_22_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_conv_weight_to_fp16, x = input_1191_cast_fp16)[name = string("x_521_cast_fp16")];
+            tensor<int32, [3]> input_1193_perm_0 = const()[name = string("input_1193_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1195_pad_type_0 = const()[name = string("input_1195_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1195_strides_0 = const()[name = string("input_1195_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1195_pad_0 = const()[name = string("input_1195_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1195_dilations_0 = const()[name = string("input_1195_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1195_groups_0 = const()[name = string("input_1195_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_22_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280130688))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281179328))))[name = string("encoder_module_layers_22_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281179456)))];
+            tensor<fp16, [1, 1024, 188]> input_1193_cast_fp16 = transpose(perm = input_1193_perm_0, x = x_521_cast_fp16)[name = string("transpose_153")];
+            tensor<fp16, [1, 2048, 188]> input_1195_cast_fp16 = conv(bias = encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_22_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1193_cast_fp16)[name = string("input_1195_cast_fp16")];
+            int32 x_523_split_num_splits_0 = const()[name = string("x_523_split_num_splits_0"), val = int32(2)];
+            int32 x_523_split_axis_0 = const()[name = string("x_523_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_523_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_523_split_cast_fp16_1 = split(axis = x_523_split_axis_0, num_splits = x_523_split_num_splits_0, x = input_1195_cast_fp16)[name = string("x_523_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_523_split_1_sigmoid_cast_fp16 = sigmoid(x = x_523_split_cast_fp16_1)[name = string("x_523_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_523_cast_fp16 = mul(x = x_523_split_cast_fp16_0, y = x_523_split_1_sigmoid_cast_fp16)[name = string("x_523_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1197_cast_fp16 = select(a = var_164_to_fp16, b = x_523_cast_fp16, cond = var_608)[name = string("input_1197_cast_fp16")];
+            tensor<int32, [6]> input_1199_pad_0 = const()[name = string("input_1199_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1199_mode_0 = const()[name = string("input_1199_mode_0"), val = string("constant")];
+            fp16 const_311_to_fp16 = const()[name = string("const_311_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1199_cast_fp16 = pad(constant_val = const_311_to_fp16, mode = input_1199_mode_0, pad = input_1199_pad_0, x = input_1197_cast_fp16)[name = string("input_1199_cast_fp16")];
+            string input_1201_pad_type_0 = const()[name = string("input_1201_pad_type_0"), val = string("valid")];
+            int32 input_1201_groups_0 = const()[name = string("input_1201_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1201_strides_0 = const()[name = string("input_1201_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1201_pad_0 = const()[name = string("input_1201_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1201_dilations_0 = const()[name = string("input_1201_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_366_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281183616))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281188288))))[name = string("const_366_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_367_to_fp16 = const()[name = string("const_367_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281188416)))];
+            tensor<fp16, [1, 1024, 188]> input_1203_cast_fp16 = conv(bias = const_367_to_fp16, dilations = input_1201_dilations_0, groups = input_1201_groups_0, pad = input_1201_pad_0, pad_type = input_1201_pad_type_0, strides = input_1201_strides_0, weight = const_366_to_fp16_palettized, x = input_1199_cast_fp16)[name = string("input_1203_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1205_cast_fp16 = silu(x = input_1203_cast_fp16)[name = string("input_1205_cast_fp16")];
+            string x_525_pad_type_0 = const()[name = string("x_525_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_525_strides_0 = const()[name = string("x_525_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_525_pad_0 = const()[name = string("x_525_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_525_dilations_0 = const()[name = string("x_525_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_525_groups_0 = const()[name = string("x_525_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281190528))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281714880))))[name = string("encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281715008)))];
+            tensor<fp16, [1, 1024, 188]> x_525_cast_fp16 = conv(bias = encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16, dilations = x_525_dilations_0, groups = x_525_groups_0, pad = x_525_pad_0, pad_type = x_525_pad_type_0, strides = x_525_strides_0, weight = encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1205_cast_fp16)[name = string("x_525_cast_fp16")];
+            tensor<int32, [3]> input_1207_perm_0 = const()[name = string("input_1207_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1207_cast_fp16 = transpose(perm = input_1207_perm_0, x = x_525_cast_fp16)[name = string("transpose_152")];
+            tensor<fp16, [1, 188, 1024]> input_1209_cast_fp16 = add(x = input_1191_cast_fp16, y = input_1207_cast_fp16)[name = string("input_1209_cast_fp16")];
+            tensor<int32, [1]> input_1211_axes_0 = const()[name = string("input_1211_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281717120)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281719232)))];
+            tensor<fp16, [1, 188, 1024]> input_1211_cast_fp16 = layer_norm(axes = input_1211_axes_0, beta = encoder_module_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1209_cast_fp16)[name = string("input_1211_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(281721344))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283818560))))[name = string("encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283818688)))];
+            tensor<fp16, [1, 188, 4096]> linear_206_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1211_cast_fp16)[name = string("linear_206_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1215_cast_fp16 = silu(x = linear_206_cast_fp16)[name = string("input_1215_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283826944))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285924160))))[name = string("encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285924288)))];
+            tensor<fp16, [1, 188, 1024]> linear_207_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1215_cast_fp16)[name = string("linear_207_cast_fp16")];
+            fp16 var_4478_to_fp16 = const()[name = string("op_4478_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4479_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_4478_to_fp16)[name = string("op_4479_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1221_cast_fp16 = add(x = input_1209_cast_fp16, y = var_4479_cast_fp16)[name = string("input_1221_cast_fp16")];
+            tensor<int32, [1]> input_1223_axes_0 = const()[name = string("input_1223_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285926400)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285928512)))];
+            tensor<fp16, [1, 188, 1024]> input_1223_cast_fp16 = layer_norm(axes = input_1223_axes_0, beta = encoder_module_layers_22_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_out_weight_to_fp16, x = input_1221_cast_fp16)[name = string("input_1223_cast_fp16")];
+            tensor<int32, [1]> input_1225_axes_0 = const()[name = string("input_1225_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285930624)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285932736)))];
+            tensor<fp16, [1, 188, 1024]> input_1225_cast_fp16 = layer_norm(axes = input_1225_axes_0, beta = encoder_module_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1223_cast_fp16)[name = string("input_1225_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285934848))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288032064))))[name = string("encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288032192)))];
+            tensor<fp16, [1, 188, 4096]> linear_208_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1225_cast_fp16)[name = string("linear_208_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1229_cast_fp16 = silu(x = linear_208_cast_fp16)[name = string("input_1229_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288040448))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290137664))))[name = string("encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290137792)))];
+            tensor<fp16, [1, 188, 1024]> linear_209_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1229_cast_fp16)[name = string("linear_209_cast_fp16")];
+            fp16 var_4509_to_fp16 = const()[name = string("op_4509_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4510_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_4509_to_fp16)[name = string("op_4510_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1235_cast_fp16 = add(x = input_1223_cast_fp16, y = var_4510_cast_fp16)[name = string("input_1235_cast_fp16")];
+            tensor<int32, [1]> query_axes_0 = const()[name = string("query_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290139904)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290142016)))];
+            tensor<fp16, [1, 188, 1024]> query_cast_fp16 = layer_norm(axes = query_axes_0, beta = encoder_module_layers_23_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_self_att_weight_to_fp16, x = input_1235_cast_fp16)[name = string("query_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290144128))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290668480))))[name = string("encoder_module_layers_23_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290668608)))];
+            tensor<fp16, [1, 188, 1024]> linear_210_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_q_weight_to_fp16_palettized, x = query_cast_fp16)[name = string("linear_210_cast_fp16")];
+            tensor<int32, [4]> var_4527 = const()[name = string("op_4527"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_139_cast_fp16 = reshape(shape = var_4527, x = linear_210_cast_fp16)[name = string("q_139_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290670720))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291195072))))[name = string("encoder_module_layers_23_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291195200)))];
+            tensor<fp16, [1, 188, 1024]> linear_211_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_k_weight_to_fp16_palettized, x = query_cast_fp16)[name = string("linear_211_cast_fp16")];
+            tensor<int32, [4]> var_4532 = const()[name = string("op_4532"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_93_cast_fp16 = reshape(shape = var_4532, x = linear_211_cast_fp16)[name = string("k_93_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291197312))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291721664))))[name = string("encoder_module_layers_23_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291721792)))];
+            tensor<fp16, [1, 188, 1024]> linear_212_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_v_weight_to_fp16_palettized, x = query_cast_fp16)[name = string("linear_212_cast_fp16")];
+            tensor<int32, [4]> var_4537 = const()[name = string("op_4537"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_cast_fp16 = reshape(shape = var_4537, x = linear_212_cast_fp16)[name = string("v_cast_fp16")];
+            tensor<int32, [4]> value_perm_0 = const()[name = string("value_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291723904)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4549_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_module_layers_23_self_attn_pos_bias_u_to_fp16)[name = string("op_4549_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291726016)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4551_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_module_layers_23_self_attn_pos_bias_v_to_fp16)[name = string("op_4551_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_perm_0 = const()[name = string("q_with_bias_v_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            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<fp16, [1, 8, 128, 375]> op_4553_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291728128))), lut = tensor<fp16, [1, 1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291920192))))[name = string("op_4553_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4551_cast_fp16)[name = string("transpose_151")];
+            tensor<fp16, [1, 8, 188, 375]> x_533_cast_fp16 = matmul(transpose_x = x_533_transpose_x_0, transpose_y = x_533_transpose_y_0, x = q_with_bias_v_cast_fp16, y = op_4553_to_fp16_palettized)[name = string("x_533_cast_fp16")];
+            tensor<int32, [8]> x_535_pad_0 = const()[name = string("x_535_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_535_mode_0 = const()[name = string("x_535_mode_0"), val = string("constant")];
+            fp16 const_318_to_fp16 = const()[name = string("const_318_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_535_cast_fp16 = pad(constant_val = const_318_to_fp16, mode = x_535_mode_0, pad = x_535_pad_0, x = x_533_cast_fp16)[name = string("x_535_cast_fp16")];
+            tensor<int32, [4]> var_4561 = const()[name = string("op_4561"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_537_cast_fp16 = reshape(shape = var_4561, x = x_535_cast_fp16)[name = string("x_537_cast_fp16")];
+            tensor<int32, [4]> var_4565_begin_0 = const()[name = string("op_4565_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4565_end_0 = const()[name = string("op_4565_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4565_end_mask_0 = const()[name = string("op_4565_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4565_cast_fp16 = slice_by_index(begin = var_4565_begin_0, end = var_4565_end_0, end_mask = var_4565_end_mask_0, x = x_537_cast_fp16)[name = string("op_4565_cast_fp16")];
+            tensor<int32, [4]> var_4566 = const()[name = string("op_4566"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_93_cast_fp16 = reshape(shape = var_4566, x = var_4565_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<int32, [4]> transpose_142_perm_0 = const()[name = string("transpose_142_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_143_perm_0 = const()[name = string("transpose_143_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = string("transpose_149")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_142 = transpose(perm = transpose_142_perm_0, x = var_4549_cast_fp16)[name = string("transpose_150")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_end_0 = const()[name = string("matrix_bd_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4575_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = string("op_4575_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<fp16, [1, 8, 188, 188]> _inversed_scores_93_cast_fp16 = mul(x = var_4575_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = string("_inversed_scores_93_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_15)[name = string("scores_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4581_cast_fp16 = softmax(axis = var_152, x = scores_cast_fp16)[name = string("op_4581_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1237_cast_fp16 = select(a = var_164_to_fp16, b = var_4581_cast_fp16, cond = mask_15)[name = string("input_1237_cast_fp16")];
+            bool x_539_transpose_x_0 = const()[name = string("x_539_transpose_x_0"), val = bool(false)];
+            bool x_539_transpose_y_0 = const()[name = string("x_539_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = string("transpose_148")];
+            tensor<fp16, [1, 8, 188, 128]> x_539_cast_fp16 = matmul(transpose_x = x_539_transpose_x_0, transpose_y = x_539_transpose_y_0, x = input_1237_cast_fp16, y = value_cast_fp16)[name = string("x_539_cast_fp16")];
+            tensor<int32, [4]> var_4585_perm_0 = const()[name = string("op_4585_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4586 = const()[name = string("op_4586"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4585_cast_fp16 = transpose(perm = var_4585_perm_0, x = x_539_cast_fp16)[name = string("transpose_147")];
+            tensor<fp16, [1, 188, 1024]> input_1239_cast_fp16 = reshape(shape = var_4586, x = var_4585_cast_fp16)[name = string("input_1239_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291920320))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292444672))))[name = string("encoder_module_layers_23_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292444800)))];
+            tensor<fp16, [1, 188, 1024]> linear_214_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_out_weight_to_fp16_palettized, x = input_1239_cast_fp16)[name = string("linear_214_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1243_cast_fp16 = add(x = input_1235_cast_fp16, y = linear_214_cast_fp16)[name = string("input_1243_cast_fp16")];
+            tensor<int32, [1]> x_543_axes_0 = const()[name = string("x_543_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292446912)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292449024)))];
+            tensor<fp16, [1, 188, 1024]> x_543_cast_fp16 = layer_norm(axes = x_543_axes_0, beta = encoder_module_layers_23_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_conv_weight_to_fp16, x = input_1243_cast_fp16)[name = string("x_543_cast_fp16")];
+            tensor<int32, [3]> input_1245_perm_0 = const()[name = string("input_1245_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1247_pad_type_0 = const()[name = string("input_1247_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1247_strides_0 = const()[name = string("input_1247_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1247_pad_0 = const()[name = string("input_1247_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1247_dilations_0 = const()[name = string("input_1247_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1247_groups_0 = const()[name = string("input_1247_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_23_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292451136))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293499776))))[name = string("encoder_module_layers_23_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293499904)))];
+            tensor<fp16, [1, 1024, 188]> input_1245_cast_fp16 = transpose(perm = input_1245_perm_0, x = x_543_cast_fp16)[name = string("transpose_146")];
+            tensor<fp16, [1, 2048, 188]> input_1247_cast_fp16 = conv(bias = encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_23_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1245_cast_fp16)[name = string("input_1247_cast_fp16")];
+            int32 x_545_split_num_splits_0 = const()[name = string("x_545_split_num_splits_0"), val = int32(2)];
+            int32 x_545_split_axis_0 = const()[name = string("x_545_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_545_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_545_split_cast_fp16_1 = split(axis = x_545_split_axis_0, num_splits = x_545_split_num_splits_0, x = input_1247_cast_fp16)[name = string("x_545_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_545_split_1_sigmoid_cast_fp16 = sigmoid(x = x_545_split_cast_fp16_1)[name = string("x_545_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_545_cast_fp16 = mul(x = x_545_split_cast_fp16_0, y = x_545_split_1_sigmoid_cast_fp16)[name = string("x_545_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1249_cast_fp16 = select(a = var_164_to_fp16, b = x_545_cast_fp16, cond = var_608)[name = string("input_1249_cast_fp16")];
+            tensor<int32, [6]> input_1251_pad_0 = const()[name = string("input_1251_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1251_mode_0 = const()[name = string("input_1251_mode_0"), val = string("constant")];
+            fp16 const_321_to_fp16 = const()[name = string("const_321_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1251_cast_fp16 = pad(constant_val = const_321_to_fp16, mode = input_1251_mode_0, pad = input_1251_pad_0, x = input_1249_cast_fp16)[name = string("input_1251_cast_fp16")];
+            string input_1253_pad_type_0 = const()[name = string("input_1253_pad_type_0"), val = string("valid")];
+            int32 input_1253_groups_0 = const()[name = string("input_1253_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1253_strides_0 = const()[name = string("input_1253_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1253_pad_0 = const()[name = string("input_1253_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1253_dilations_0 = const()[name = string("input_1253_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_368_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293504064))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293508736))))[name = string("const_368_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_369_to_fp16 = const()[name = string("const_369_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293508864)))];
+            tensor<fp16, [1, 1024, 188]> input_1255_cast_fp16 = conv(bias = const_369_to_fp16, dilations = input_1253_dilations_0, groups = input_1253_groups_0, pad = input_1253_pad_0, pad_type = input_1253_pad_type_0, strides = input_1253_strides_0, weight = const_368_to_fp16_palettized, x = input_1251_cast_fp16)[name = string("input_1255_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1257_cast_fp16 = silu(x = input_1255_cast_fp16)[name = string("input_1257_cast_fp16")];
+            string x_547_pad_type_0 = const()[name = string("x_547_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_547_strides_0 = const()[name = string("x_547_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_547_pad_0 = const()[name = string("x_547_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_547_dilations_0 = const()[name = string("x_547_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_547_groups_0 = const()[name = string("x_547_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293510976))), lut = tensor<fp16, [1, 1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294035328))))[name = string("encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294035456)))];
+            tensor<fp16, [1, 1024, 188]> x_547_cast_fp16 = conv(bias = encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16, dilations = x_547_dilations_0, groups = x_547_groups_0, pad = x_547_pad_0, pad_type = x_547_pad_type_0, strides = x_547_strides_0, weight = encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1257_cast_fp16)[name = string("x_547_cast_fp16")];
+            tensor<int32, [3]> input_1259_perm_0 = const()[name = string("input_1259_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1259_cast_fp16 = transpose(perm = input_1259_perm_0, x = x_547_cast_fp16)[name = string("transpose_145")];
+            tensor<fp16, [1, 188, 1024]> input_1261_cast_fp16 = add(x = input_1243_cast_fp16, y = input_1259_cast_fp16)[name = string("input_1261_cast_fp16")];
+            tensor<int32, [1]> input_1263_axes_0 = const()[name = string("input_1263_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294037568)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294039680)))];
+            tensor<fp16, [1, 188, 1024]> input_1263_cast_fp16 = layer_norm(axes = input_1263_axes_0, beta = encoder_module_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1261_cast_fp16)[name = string("input_1263_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294041792))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296139008))))[name = string("encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296139136)))];
+            tensor<fp16, [1, 188, 4096]> linear_215_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1263_cast_fp16)[name = string("linear_215_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1267_cast_fp16 = silu(x = linear_215_cast_fp16)[name = string("input_1267_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296147392))), lut = tensor<fp16, [1, 1, 16, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298244608))))[name = string("encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298244736)))];
+            tensor<fp16, [1, 188, 1024]> linear_216_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1267_cast_fp16)[name = string("linear_216_cast_fp16")];
+            fp16 var_4652_to_fp16 = const()[name = string("op_4652_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4653_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_4652_to_fp16)[name = string("op_4653_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_cast_fp16 = add(x = input_1261_cast_fp16, y = var_4653_cast_fp16)[name = string("input_cast_fp16")];
+            tensor<int32, [1]> audio_signal_axes_0 = const()[name = string("audio_signal_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298246848)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298248960)))];
+            tensor<fp16, [1, 188, 1024]> audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = encoder_module_layers_23_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = string("audio_signal_cast_fp16")];
+            tensor<int32, [3]> obj_3_perm_0 = const()[name = string("obj_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp16, [1, 1024, 188]> obj_3_cast_fp16 = transpose(perm = obj_3_perm_0, x = audio_signal_cast_fp16)[name = string("transpose_144")];
+            tensor<fp32, [1, 1024, 188]> encoder = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_0")];
+        } -> (encoder, encoder_length);
+}
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/weights/weight.bin b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..5c3b4f23d3eb3812a77cd32605893306a45e5331
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:971bdc0f0bfc738b0d9e7fe6aca1d30fee5c2a55c15a8f8955b07767db1a76d0
+size 298251072
diff --git a/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/analytics/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/analytics/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..5faaaa3514e82f8668bc8a33e7b527b47b118185
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7000c84a4ca4b88f7c286df332b8a5b6cf98d6f5472352eea08470f38f46cdc1
+size 243
diff --git a/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..dc7d752357ad13592893f618318f4b0b73c18493
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8bb8115c6d918526b13db13e228e22dd8010269a545d922d83d6a10ab0dedc42
+size 463
diff --git a/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/metadata.json b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..9a5231e48b347c3817f231130a28ff4e206cf157
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/metadata.json
@@ -0,0 +1,70 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "shortDescription" : "Parakeet CTC decoder head (encoder -> log_probs)",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "log_probs",
+        "type" : "MultiArray"
+      }
+    ],
+    "storagePrecision" : "Mixed (Float16, Palettized (6 bits), UInt6)",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.transpose" : 1,
+      "Ios18.constexprLutToDense" : 1,
+      "Ios18.cast" : 2,
+      "Ios18.conv" : 1,
+      "Ios18.softmax" : 1,
+      "Ios18.log" : 1
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 1024 × 1...188",
+        "shapeRange" : "[[1, 1], [1024, 1024], [1, 188]]",
+        "formattedType" : "MultiArray (Float32 1 × 1024 × 1)",
+        "type" : "MultiArray",
+        "shape" : "[1, 1024, 1]",
+        "name" : "encoder",
+        "shortDescription" : ""
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-02-27",
+      "com.github.apple.coremltools.source" : "torch==2.10.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "parakeet_ctc_decoder",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/model.mil b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..2d5ed5ec2f7595046f347be7e3709c463e3091b5
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/model.mil
@@ -0,0 +1,24 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 1024, ?]> encoder) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"encoder", [1, 1024, 1]}}), ("RangeDims", {{"encoder", [[1, 1], [1024, 1024], [1, 188]]}})))] {
+            int32 var_4 = const()[name = string("op_4"), val = int32(-1)];
+            string var_18_pad_type_0 = const()[name = string("op_18_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_18_strides_0 = const()[name = string("op_18_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_18_pad_0 = const()[name = string("op_18_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_18_dilations_0 = const()[name = string("op_18_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_18_groups_0 = const()[name = string("op_18_groups_0"), val = int32(1)];
+            string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1025, 1024, 1]> module_decoder_layers_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1025, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(787328))))[name = string("module_decoder_layers_0_weight_to_fp16_palettized")];
+            tensor<fp16, [1025]> module_decoder_layers_0_bias_to_fp16 = const()[name = string("module_decoder_layers_0_bias_to_fp16"), val = tensor<fp16, [1025]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(787520)))];
+            tensor<fp16, [1, 1024, ?]> encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_1")];
+            tensor<fp16, [1, 1025, ?]> var_18_cast_fp16 = conv(bias = module_decoder_layers_0_bias_to_fp16, dilations = var_18_dilations_0, groups = var_18_groups_0, pad = var_18_pad_0, pad_type = var_18_pad_type_0, strides = var_18_strides_0, weight = module_decoder_layers_0_weight_to_fp16_palettized, x = encoder_to_fp16)[name = string("op_18_cast_fp16")];
+            tensor<int32, [3]> input_perm_0 = const()[name = string("input_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, ?, 1025]> input_cast_fp16 = transpose(perm = input_perm_0, x = var_18_cast_fp16)[name = string("transpose_0")];
+            tensor<fp16, [1, ?, 1025]> out_objects_softmax_cast_fp16 = softmax(axis = var_4, x = input_cast_fp16)[name = string("out_objects_softmax_cast_fp16")];
+            fp32 out_objects_epsilon_0 = const()[name = string("out_objects_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [1, ?, 1025]> out_objects_cast_fp16 = log(epsilon = out_objects_epsilon_0, x = out_objects_softmax_cast_fp16)[name = string("out_objects_cast_fp16")];
+            string out_objects_cast_fp16_to_fp32_dtype_0 = const()[name = string("out_objects_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [1, ?, 1025]> log_probs = cast(dtype = out_objects_cast_fp16_to_fp32_dtype_0, x = out_objects_cast_fp16)[name = string("cast_0")];
+        } -> (log_probs);
+}
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/weights/weight.bin b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..92f18dfdb78b691632218f31f2f4ce7f0ed66cdb
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_decoder.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d0a073770791d8ff2214fc89dcb29e6e42a11c0c253f0c3c0a23d3e79ad69ec
+size 789634
diff --git a/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..6a598bdefb4840d3d1e38c33ffd3ee0912a44d6f
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:124ea4cda0d5d6210203fd724f81e5284e78fea6a9517013f25d54b046a393d6
+size 243
diff --git a/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..466937376bb16eee14c8e65c42081492c0b2781c
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:85e9f93f4605ea3f79760b8005fc26ba2ff209c2f1865af09cfb76805c1c8938
+size 496
diff --git a/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/metadata.json b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..572d9275c12a00ad77a6963c3951c6c7c260b55a
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/metadata.json
@@ -0,0 +1,117 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "shortDescription" : "Parakeet CTC Mel+Encoder (15.0s window)",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 1024 × 188)",
+        "shortDescription" : "",
+        "shape" : "[1, 1024, 188]",
+        "name" : "encoder",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "encoder_length",
+        "type" : "MultiArray"
+      }
+    ],
+    "storagePrecision" : "Mixed (Float16, Int32, Palettized (6 bits), UInt6)",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Stack" : 1,
+      "Ios18.conv" : 79,
+      "Ios18.mul" : 107,
+      "Ios18.constexprLutToDense" : 298,
+      "Ios18.cast" : 12,
+      "Ios18.layerNorm" : 120,
+      "Ios18.log" : 1,
+      "Ios18.equal" : 1,
+      "Ios18.floorDiv" : 4,
+      "Ios16.reduceSum" : 4,
+      "Ios18.logicalNot" : 3,
+      "Ios18.reshape" : 147,
+      "Pad" : 49,
+      "Ios18.concat" : 1,
+      "Ios18.add" : 180,
+      "Ios18.realDiv" : 3,
+      "Ios18.greaterEqual" : 1,
+      "Ios18.notEqual" : 1,
+      "Ios18.relu" : 3,
+      "Ios18.sub" : 7,
+      "Ios18.matmul" : 73,
+      "Ios18.silu" : 72,
+      "Ios18.expandDims" : 25,
+      "Ios18.linear" : 193,
+      "Ios18.sigmoid" : 24,
+      "Ios18.sliceByIndex" : 51,
+      "Ios18.transpose" : 172,
+      "Split" : 24,
+      "Ios18.sqrt" : 1,
+      "Ios18.softmax" : 24,
+      "Ios18.pow" : 2,
+      "Select" : 78,
+      "Ios18.logicalAnd" : 2,
+      "Ios18.less" : 6,
+      "Tile" : 9
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 240000)",
+        "shortDescription" : "",
+        "shape" : "[1, 240000]",
+        "name" : "audio_signal",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "audio_length",
+        "type" : "MultiArray"
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-02-27",
+      "com.github.apple.coremltools.source" : "torch==2.10.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "parakeet_ctc_mel_encoder",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/model.mil b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..73e022a7aedd05a4adcd3f8e252391bf302c4dee
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/model.mil
@@ -0,0 +1,3838 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}})]
+{
+    func main<ios18>(tensor<int32, [1]> audio_length, tensor<fp32, [1, 240000]> audio_signal) {
+            int32 var_20 = const()[name = string("op_20"), val = int32(0)];
+            int32 var_21 = const()[name = string("op_21"), val = int32(160)];
+            int32 var_22 = const()[name = string("op_22"), val = int32(1)];
+            int32 var_32 = const()[name = string("op_32"), val = int32(512)];
+            tensor<int32, [1]> var_33 = add(x = audio_length, y = var_32)[name = string("op_33")];
+            int32 var_34 = const()[name = string("op_34"), val = int32(512)];
+            tensor<int32, [1]> var_35 = sub(x = var_33, y = var_34)[name = string("op_35")];
+            tensor<int32, [1]> floor_div_0 = floor_div(x = var_35, y = var_21)[name = string("floor_div_0")];
+            tensor<bool, [1]> var_38 = equal(x = audio_length, y = var_20)[name = string("op_38")];
+            tensor<int32, [1]> var_39 = const()[name = string("op_39"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1]> seq_len = select(a = var_39, b = floor_div_0, cond = var_38)[name = string("seq_len")];
+            tensor<int32, [1, 240000]> var_43 = const()[name = string("op_43"), val = tensor<int32, [1, 240000]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<int32, [1]> var_44_axes_0 = const()[name = string("op_44_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1]> var_44 = expand_dims(axes = var_44_axes_0, x = audio_length)[name = string("op_44")];
+            tensor<bool, [1, 240000]> timemask = less(x = var_43, y = var_44)[name = string("timemask")];
+            tensor<int32, [2]> var_47_begin_0 = const()[name = string("op_47_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_47_end_0 = const()[name = string("op_47_end_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<bool, [2]> var_47_end_mask_0 = const()[name = string("op_47_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<bool, [2]> var_47_squeeze_mask_0 = const()[name = string("op_47_squeeze_mask_0"), val = tensor<bool, [2]>([false, true])];
+            string audio_signal_to_fp16_dtype_0 = const()[name = string("audio_signal_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 240000]> audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = string("cast_11")];
+            tensor<fp16, [1]> var_47_cast_fp16 = slice_by_index(begin = var_47_begin_0, end = var_47_end_0, end_mask = var_47_end_mask_0, squeeze_mask = var_47_squeeze_mask_0, x = audio_signal_to_fp16)[name = string("op_47_cast_fp16")];
+            tensor<int32, [1]> var_48_axes_0 = const()[name = string("op_48_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_48_cast_fp16 = expand_dims(axes = var_48_axes_0, x = var_47_cast_fp16)[name = string("op_48_cast_fp16")];
+            tensor<int32, [2]> var_50_begin_0 = const()[name = string("op_50_begin_0"), val = tensor<int32, [2]>([0, 1])];
+            tensor<int32, [2]> var_50_end_0 = const()[name = string("op_50_end_0"), val = tensor<int32, [2]>([1, 240000])];
+            tensor<bool, [2]> var_50_end_mask_0 = const()[name = string("op_50_end_mask_0"), val = tensor<bool, [2]>([true, true])];
+            tensor<fp16, [1, 239999]> var_50_cast_fp16 = slice_by_index(begin = var_50_begin_0, end = var_50_end_0, end_mask = var_50_end_mask_0, x = audio_signal_to_fp16)[name = string("op_50_cast_fp16")];
+            tensor<int32, [2]> var_52_begin_0 = const()[name = string("op_52_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_52_end_0 = const()[name = string("op_52_end_0"), val = tensor<int32, [2]>([1, 239999])];
+            tensor<bool, [2]> var_52_end_mask_0 = const()[name = string("op_52_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<fp16, [1, 239999]> var_52_cast_fp16 = slice_by_index(begin = var_52_begin_0, end = var_52_end_0, end_mask = var_52_end_mask_0, x = audio_signal_to_fp16)[name = string("op_52_cast_fp16")];
+            fp16 var_53_to_fp16 = const()[name = string("op_53_to_fp16"), val = fp16(0x1.f0cp-1)];
+            tensor<fp16, [1, 239999]> var_54_cast_fp16 = mul(x = var_52_cast_fp16, y = var_53_to_fp16)[name = string("op_54_cast_fp16")];
+            tensor<fp16, [1, 239999]> var_55_cast_fp16 = sub(x = var_50_cast_fp16, y = var_54_cast_fp16)[name = string("op_55_cast_fp16")];
+            bool x_3_interleave_0 = const()[name = string("x_3_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 240000]> x_3_cast_fp16 = concat(axis = var_22, interleave = x_3_interleave_0, values = (var_48_cast_fp16, var_55_cast_fp16))[name = string("x_3_cast_fp16")];
+            tensor<bool, [1, 240000]> var_58 = logical_not(x = timemask)[name = string("op_58")];
+            fp16 var_16_to_fp16 = const()[name = string("op_16_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 240000]> input_1_cast_fp16 = select(a = var_16_to_fp16, b = x_3_cast_fp16, cond = var_58)[name = string("input_1_cast_fp16")];
+            tensor<int32, [3]> var_63 = const()[name = string("op_63"), val = tensor<int32, [3]>([1, 1, 240000])];
+            tensor<fp16, [1, 1, 240000]> input_3_cast_fp16 = reshape(shape = var_63, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")];
+            tensor<int32, [6]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 256])];
+            string input_5_mode_0 = const()[name = string("input_5_mode_0"), val = string("constant")];
+            fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1, 240512]> input_5_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")];
+            tensor<int32, [2]> var_69 = const()[name = string("op_69"), val = tensor<int32, [2]>([1, 240512])];
+            tensor<fp16, [1, 240512]> input_7_cast_fp16 = reshape(shape = var_69, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")];
+            tensor<int32, [1]> expand_dims_10 = const()[name = string("expand_dims_10"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [1]> expand_dims_11_axes_0 = const()[name = string("expand_dims_11_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 240512]> expand_dims_11_cast_fp16 = expand_dims(axes = expand_dims_11_axes_0, x = input_7_cast_fp16)[name = string("expand_dims_11_cast_fp16")];
+            string conv_0_pad_type_0 = const()[name = string("conv_0_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv_0_pad_0 = const()[name = string("conv_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_0_dilations_0 = const()[name = string("conv_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 conv_0_groups_0 = const()[name = string("conv_0_groups_0"), val = int32(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_8_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [257, 1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(960128))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1058880))))[name = string("expand_dims_8_to_fp16_palettized")];
+            tensor<fp16, [1, 257, 1501]> 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_10, weight = expand_dims_8_to_fp16_palettized, x = expand_dims_11_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<int32, [2]> conv_1_pad_0 = const()[name = string("conv_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_1_dilations_0 = const()[name = string("conv_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 conv_1_groups_0 = const()[name = string("conv_1_groups_0"), val = int32(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_9_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [257, 1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1059072))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1157824))))[name = string("expand_dims_9_to_fp16_palettized")];
+            tensor<fp16, [1, 257, 1501]> 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_10, weight = expand_dims_9_to_fp16_palettized, x = expand_dims_11_cast_fp16)[name = string("conv_1_cast_fp16")];
+            int32 stack_0_axis_0 = const()[name = string("stack_0_axis_0"), val = int32(-1)];
+            tensor<fp16, [1, 257, 1501, 2]> 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_13_promoted_to_fp16 = const()[name = string("op_13_promoted_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1, 257, 1501, 2]> var_73_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_13_promoted_to_fp16)[name = string("op_73_cast_fp16")];
+            tensor<int32, [1]> var_75_axes_0 = const()[name = string("op_75_axes_0"), val = tensor<int32, [1]>([-1])];
+            bool var_75_keep_dims_0 = const()[name = string("op_75_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 257, 1501]> var_75_cast_fp16 = reduce_sum(axes = var_75_axes_0, keep_dims = var_75_keep_dims_0, x = var_73_cast_fp16)[name = string("op_75_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<fp16, [1, 80, 257]> const_9_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 80, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1158016))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1173504))))[name = string("const_9_to_fp16_palettized")];
+            tensor<fp16, [1, 80, 1501]> x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = const_9_to_fp16_palettized, y = var_75_cast_fp16)[name = string("x_13_cast_fp16")];
+            fp16 var_82_to_fp16 = const()[name = string("op_82_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [1, 80, 1501]> var_83_cast_fp16 = add(x = x_13_cast_fp16, y = var_82_to_fp16)[name = string("op_83_cast_fp16")];
+            fp32 x_15_epsilon_0 = const()[name = string("x_15_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [1, 80, 1501]> x_15_cast_fp16 = log(epsilon = x_15_epsilon_0, x = var_83_cast_fp16)[name = string("x_15_cast_fp16")];
+            tensor<int32, [1, 1501]> var_88 = const()[name = string("op_88"), val = tensor<int32, [1, 1501]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1173696)))];
+            tensor<int32, [1]> var_91_axes_0 = const()[name = string("op_91_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1]> var_91 = expand_dims(axes = var_91_axes_0, x = seq_len)[name = string("op_91")];
+            tensor<bool, [1, 1501]> valid_mask = less(x = var_88, y = var_91)[name = string("valid_mask")];
+            tensor<int32, [1]> var_93_axes_0 = const()[name = string("op_93_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_93 = expand_dims(axes = var_93_axes_0, x = valid_mask)[name = string("op_93")];
+            tensor<int32, [3]> var_93_after_broadcast_reps_0 = const()[name = string("op_93_after_broadcast_reps_0"), val = tensor<int32, [3]>([1, 80, 1])];
+            tensor<bool, [1, 80, 1501]> var_93_after_broadcast = tile(reps = var_93_after_broadcast_reps_0, x = var_93)[name = string("op_93_after_broadcast")];
+            tensor<fp16, [1, 80, 1501]> op_16_after_broadcast_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 80, 1501]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1179776))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269952))))[name = string("op_16_after_broadcast_to_fp16_palettized")];
+            tensor<fp16, [1, 80, 1501]> var_94_cast_fp16 = select(a = x_15_cast_fp16, b = op_16_after_broadcast_to_fp16_palettized, cond = var_93_after_broadcast)[name = string("op_94_cast_fp16")];
+            tensor<int32, [1]> x_mean_numerator_axes_0 = const()[name = string("x_mean_numerator_axes_0"), val = tensor<int32, [1]>([2])];
+            bool x_mean_numerator_keep_dims_0 = const()[name = string("x_mean_numerator_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 80]> x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_94_cast_fp16)[name = string("x_mean_numerator_cast_fp16")];
+            tensor<int32, [1]> x_mean_denominator_axes_0 = const()[name = string("x_mean_denominator_axes_0"), val = tensor<int32, [1]>([1])];
+            bool x_mean_denominator_keep_dims_0 = const()[name = string("x_mean_denominator_keep_dims_0"), val = bool(false)];
+            string cast_2_to_fp16_dtype_0 = const()[name = string("cast_2_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 1501]> valid_mask_to_fp16 = cast(dtype = cast_2_to_fp16_dtype_0, x = valid_mask)[name = string("cast_10")];
+            tensor<fp16, [1]> x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = string("x_mean_denominator_cast_fp16")];
+            tensor<int32, [1]> var_99_axes_0 = const()[name = string("op_99_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_99_cast_fp16 = expand_dims(axes = var_99_axes_0, x = x_mean_denominator_cast_fp16)[name = string("op_99_cast_fp16")];
+            tensor<fp16, [1, 80]> x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_99_cast_fp16)[name = string("x_mean_cast_fp16")];
+            tensor<int32, [1]> var_102_axes_0 = const()[name = string("op_102_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 80, 1]> var_102_cast_fp16 = expand_dims(axes = var_102_axes_0, x = x_mean_cast_fp16)[name = string("op_102_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> var_103_cast_fp16 = sub(x = x_15_cast_fp16, y = var_102_cast_fp16)[name = string("op_103_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> var_104_cast_fp16 = select(a = var_103_cast_fp16, b = op_16_after_broadcast_to_fp16_palettized, cond = var_93_after_broadcast)[name = string("op_104_cast_fp16")];
+            fp16 var_13_promoted_1_to_fp16 = const()[name = string("op_13_promoted_1_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1, 80, 1501]> var_105_cast_fp16 = pow(x = var_104_cast_fp16, y = var_13_promoted_1_to_fp16)[name = string("op_105_cast_fp16")];
+            tensor<int32, [1]> var_107_axes_0 = const()[name = string("op_107_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_107_keep_dims_0 = const()[name = string("op_107_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 80]> var_107_cast_fp16 = reduce_sum(axes = var_107_axes_0, keep_dims = var_107_keep_dims_0, x = var_105_cast_fp16)[name = string("op_107_cast_fp16")];
+            fp16 var_109_to_fp16 = const()[name = string("op_109_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1]> var_110_cast_fp16 = sub(x = var_99_cast_fp16, y = var_109_to_fp16)[name = string("op_110_cast_fp16")];
+            tensor<fp16, [1, 80]> var_111_cast_fp16 = real_div(x = var_107_cast_fp16, y = var_110_cast_fp16)[name = string("op_111_cast_fp16")];
+            tensor<fp16, [1, 80]> x_std_1_cast_fp16 = sqrt(x = var_111_cast_fp16)[name = string("x_std_1_cast_fp16")];
+            tensor<bool, [1, 80]> var_113_cast_fp16 = not_equal(x = x_std_1_cast_fp16, y = x_std_1_cast_fp16)[name = string("op_113_cast_fp16")];
+            tensor<fp16, [1, 80]> x_std_3_cast_fp16 = select(a = var_16_to_fp16, b = x_std_1_cast_fp16, cond = var_113_cast_fp16)[name = string("x_std_3_cast_fp16")];
+            fp16 var_7_to_fp16 = const()[name = string("op_7_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 80]> x_std_cast_fp16 = add(x = x_std_3_cast_fp16, y = var_7_to_fp16)[name = string("x_std_cast_fp16")];
+            tensor<int32, [1]> var_118_axes_0 = const()[name = string("op_118_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 80, 1]> var_118_cast_fp16 = expand_dims(axes = var_118_axes_0, x = x_std_cast_fp16)[name = string("op_118_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> x_17_cast_fp16 = real_div(x = var_103_cast_fp16, y = var_118_cast_fp16)[name = string("x_17_cast_fp16")];
+            tensor<bool, [1, 1501]> mask_3 = greater_equal(x = var_88, y = var_91)[name = string("mask_3")];
+            tensor<int32, [1]> var_127_axes_0 = const()[name = string("op_127_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_127 = expand_dims(axes = var_127_axes_0, x = mask_3)[name = string("op_127")];
+            tensor<fp16, [1, 80, 1501]> processed_signal_cast_fp16 = select(a = var_16_to_fp16, b = x_17_cast_fp16, cond = var_127)[name = string("processed_signal_cast_fp16")];
+            int32 var_152 = const()[name = string("op_152"), val = int32(-1)];
+            tensor<int32, [3]> x_19_perm_0 = const()[name = string("x_19_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> tensor_1_axes_0 = const()[name = string("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1501, 80]> x_19_cast_fp16 = transpose(perm = x_19_perm_0, x = processed_signal_cast_fp16)[name = string("transpose_315")];
+            tensor<fp16, [1, 1, 1501, 80]> tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_19_cast_fp16)[name = string("tensor_1_cast_fp16")];
+            tensor<int32, [1]> var_242_axes_0 = const()[name = string("op_242_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 1501, 1]> var_242 = expand_dims(axes = var_242_axes_0, x = valid_mask)[name = string("op_242")];
+            tensor<int32, [3]> var_244_reps_0 = const()[name = string("op_244_reps_0"), val = tensor<int32, [3]>([1, 1, 80])];
+            tensor<bool, [1, 1501, 80]> var_244 = tile(reps = var_244_reps_0, x = var_242)[name = string("op_244")];
+            tensor<int32, [1]> var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_5_to_fp16_dtype_0 = const()[name = string("mask_5_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 1501, 80]> var_244_to_fp16 = cast(dtype = mask_5_to_fp16_dtype_0, x = var_244)[name = string("cast_9")];
+            tensor<fp16, [1, 1, 1501, 80]> var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = var_244_to_fp16)[name = string("op_250_cast_fp16")];
+            tensor<fp16, [1, 1, 1501, 80]> input_9_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_250_cast_fp16)[name = string("input_9_cast_fp16")];
+            string tensor_3_pad_type_0 = const()[name = string("tensor_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_3_pad_0 = const()[name = string("tensor_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_3_strides_0 = const()[name = string("tensor_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> tensor_3_dilations_0 = const()[name = string("tensor_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_3_groups_0 = const()[name = string("tensor_3_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_0_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1270144))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1271936))))[name = string("encoder_module_pre_encode_conv_0_weight_to_fp16_palettized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_0_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1272128)))];
+            tensor<fp16, [1, 256, 751, 40]> tensor_3_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_0_weight_to_fp16_palettized, x = input_9_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_261_promoted_to_fp16 = const()[name = string("op_261_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> seq_len_to_fp16 = cast(dtype = current_lengths_1_to_fp16_dtype_0, x = seq_len)[name = string("cast_8")];
+            tensor<fp16, [1]> var_262_cast_fp16 = add(x = seq_len_to_fp16, y = var_261_promoted_to_fp16)[name = string("op_262_cast_fp16")];
+            fp16 var_263_promoted_to_fp16 = const()[name = string("op_263_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_264_cast_fp16 = add(x = var_262_cast_fp16, y = var_263_promoted_to_fp16)[name = string("op_264_cast_fp16")];
+            fp16 var_265_promoted_to_fp16 = const()[name = string("op_265_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_266_cast_fp16 = sub(x = var_264_cast_fp16, y = var_265_promoted_to_fp16)[name = string("op_266_cast_fp16")];
+            fp16 var_154_promoted_to_fp16 = const()[name = string("op_154_promoted_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_1_cast_fp16 = floor_div(x = var_266_cast_fp16, y = var_154_promoted_to_fp16)[name = string("floor_div_1_cast_fp16")];
+            fp16 var_268_promoted_to_fp16 = const()[name = string("op_268_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_3_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_268_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<int32, [1, 751]> expand_dims_4 = const()[name = string("expand_dims_4"), val = tensor<int32, [1, 751]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1272704)))];
+            tensor<int32, [1]> var_277_axes_0 = const()[name = string("op_277_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_3_cast_fp16_to_int32 = cast(dtype = lengths_19_dtype_0, x = current_lengths_3_cast_fp16)[name = string("cast_7")];
+            tensor<int32, [1, 1]> var_277 = expand_dims(axes = var_277_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = string("op_277")];
+            tensor<bool, [1, 751]> time_mask_3 = less(x = expand_dims_4, y = var_277)[name = string("time_mask_3")];
+            tensor<int32, [1]> var_279_axes_0 = const()[name = string("op_279_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 751, 1]> var_279 = expand_dims(axes = var_279_axes_0, x = time_mask_3)[name = string("op_279")];
+            tensor<int32, [3]> var_281_reps_0 = const()[name = string("op_281_reps_0"), val = tensor<int32, [3]>([1, 1, 40])];
+            tensor<bool, [1, 751, 40]> var_281 = tile(reps = var_281_reps_0, x = var_279)[name = string("op_281")];
+            tensor<int32, [1]> var_287_axes_0 = const()[name = string("op_287_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_7_to_fp16_dtype_0 = const()[name = string("mask_7_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 751, 40]> var_281_to_fp16 = cast(dtype = mask_7_to_fp16_dtype_0, x = var_281)[name = string("cast_6")];
+            tensor<fp16, [1, 1, 751, 40]> var_287_cast_fp16 = expand_dims(axes = var_287_axes_0, x = var_281_to_fp16)[name = string("op_287_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_3_reps_0 = const()[name = string("expanded_mask_3_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 751, 40]> expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_287_cast_fp16)[name = string("expanded_mask_3_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> input_11_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_11_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> tensor_5_cast_fp16 = relu(x = input_11_cast_fp16)[name = string("tensor_5_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> input_13_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_13_cast_fp16")];
+            string tensor_7_pad_type_0 = const()[name = string("tensor_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_7_pad_0 = const()[name = string("tensor_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_7_strides_0 = const()[name = string("tensor_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 tensor_7_groups_0 = const()[name = string("tensor_7_groups_0"), val = int32(256)];
+            tensor<int32, [2]> tensor_7_dilations_0 = const()[name = string("tensor_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1275776))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1277568))))[name = string("encoder_module_pre_encode_conv_2_weight_to_fp16_palettized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_2_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1277760)))];
+            tensor<fp16, [1, 256, 376, 20]> tensor_7_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_2_weight_to_fp16_palettized, x = input_13_cast_fp16)[name = string("tensor_7_cast_fp16")];
+            fp16 var_307_promoted_to_fp16 = const()[name = string("op_307_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_308_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_307_promoted_to_fp16)[name = string("op_308_cast_fp16")];
+            fp16 var_309_promoted_to_fp16 = const()[name = string("op_309_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_310_cast_fp16 = add(x = var_308_cast_fp16, y = var_309_promoted_to_fp16)[name = string("op_310_cast_fp16")];
+            fp16 var_311_promoted_to_fp16 = const()[name = string("op_311_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_312_cast_fp16 = sub(x = var_310_cast_fp16, y = var_311_promoted_to_fp16)[name = string("op_312_cast_fp16")];
+            fp16 var_154_promoted_1_to_fp16 = const()[name = string("op_154_promoted_1_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_2_cast_fp16 = floor_div(x = var_312_cast_fp16, y = var_154_promoted_1_to_fp16)[name = string("floor_div_2_cast_fp16")];
+            fp16 var_314_promoted_to_fp16 = const()[name = string("op_314_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_5_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_314_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<int32, [1, 376]> expand_dims_5 = const()[name = string("expand_dims_5"), val = tensor<int32, [1, 376]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1278336)))];
+            tensor<int32, [1]> var_323_axes_0 = const()[name = string("op_323_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_5_cast_fp16_to_int32 = cast(dtype = lengths_21_dtype_0, x = current_lengths_5_cast_fp16)[name = string("cast_5")];
+            tensor<int32, [1, 1]> var_323 = expand_dims(axes = var_323_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = string("op_323")];
+            tensor<bool, [1, 376]> time_mask_5 = less(x = expand_dims_5, y = var_323)[name = string("time_mask_5")];
+            tensor<int32, [1]> var_325_axes_0 = const()[name = string("op_325_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 376, 1]> var_325 = expand_dims(axes = var_325_axes_0, x = time_mask_5)[name = string("op_325")];
+            tensor<int32, [3]> var_327_reps_0 = const()[name = string("op_327_reps_0"), val = tensor<int32, [3]>([1, 1, 20])];
+            tensor<bool, [1, 376, 20]> var_327 = tile(reps = var_327_reps_0, x = var_325)[name = string("op_327")];
+            tensor<int32, [1]> var_333_axes_0 = const()[name = string("op_333_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_9_to_fp16_dtype_0 = const()[name = string("mask_9_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 376, 20]> var_327_to_fp16 = cast(dtype = mask_9_to_fp16_dtype_0, x = var_327)[name = string("cast_4")];
+            tensor<fp16, [1, 1, 376, 20]> var_333_cast_fp16 = expand_dims(axes = var_333_axes_0, x = var_327_to_fp16)[name = string("op_333_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_7_reps_0 = const()[name = string("expanded_mask_7_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 376, 20]> expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_333_cast_fp16)[name = string("expanded_mask_7_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_15_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_15_cast_fp16")];
+            string tensor_9_pad_type_0 = const()[name = string("tensor_9_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> tensor_9_strides_0 = const()[name = string("tensor_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> tensor_9_pad_0 = const()[name = string("tensor_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> tensor_9_dilations_0 = const()[name = string("tensor_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_9_groups_0 = const()[name = string("tensor_9_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 1, 1]> encoder_module_pre_encode_conv_3_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1279936))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1329152))))[name = string("encoder_module_pre_encode_conv_3_weight_to_fp16_palettized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_3_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_3_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1329344)))];
+            tensor<fp16, [1, 256, 376, 20]> tensor_9_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_3_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = string("tensor_9_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_17_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_17_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> tensor_11_cast_fp16 = relu(x = input_17_cast_fp16)[name = string("tensor_11_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_19_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_19_cast_fp16")];
+            string tensor_13_pad_type_0 = const()[name = string("tensor_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_13_pad_0 = const()[name = string("tensor_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_13_strides_0 = const()[name = string("tensor_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 tensor_13_groups_0 = const()[name = string("tensor_13_groups_0"), val = int32(256)];
+            tensor<int32, [2]> tensor_13_dilations_0 = const()[name = string("tensor_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_5_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1329920))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1331712))))[name = string("encoder_module_pre_encode_conv_5_weight_to_fp16_palettized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_5_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_5_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1331904)))];
+            tensor<fp16, [1, 256, 188, 10]> tensor_13_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_5_weight_to_fp16_palettized, x = input_19_cast_fp16)[name = string("tensor_13_cast_fp16")];
+            fp16 var_368_promoted_to_fp16 = const()[name = string("op_368_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_369_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_368_promoted_to_fp16)[name = string("op_369_cast_fp16")];
+            fp16 var_370_promoted_to_fp16 = const()[name = string("op_370_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_371_cast_fp16 = add(x = var_369_cast_fp16, y = var_370_promoted_to_fp16)[name = string("op_371_cast_fp16")];
+            fp16 var_372_promoted_to_fp16 = const()[name = string("op_372_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_373_cast_fp16 = sub(x = var_371_cast_fp16, y = var_372_promoted_to_fp16)[name = string("op_373_cast_fp16")];
+            fp16 var_154_promoted_2_to_fp16 = const()[name = string("op_154_promoted_2_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_3_cast_fp16 = floor_div(x = var_373_cast_fp16, y = var_154_promoted_2_to_fp16)[name = string("floor_div_3_cast_fp16")];
+            fp16 var_375_promoted_to_fp16 = const()[name = string("op_375_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_cast_fp16 = add(x = floor_div_3_cast_fp16, y = var_375_promoted_to_fp16)[name = string("current_lengths_cast_fp16")];
+            string lengths_dtype_0 = const()[name = string("lengths_dtype_0"), val = string("int32")];
+            tensor<int32, [1, 188]> expand_dims_6 = const()[name = string("expand_dims_6"), val = tensor<int32, [1, 188]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1332480)))];
+            tensor<int32, [1]> var_384_axes_0 = const()[name = string("op_384_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_cast_fp16_to_int32 = cast(dtype = lengths_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_3")];
+            tensor<int32, [1, 1]> var_384 = expand_dims(axes = var_384_axes_0, x = current_lengths_cast_fp16_to_int32)[name = string("op_384")];
+            tensor<bool, [1, 188]> time_mask = less(x = expand_dims_6, y = var_384)[name = string("time_mask")];
+            tensor<int32, [1]> var_386_axes_0 = const()[name = string("op_386_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 188, 1]> var_386 = expand_dims(axes = var_386_axes_0, x = time_mask)[name = string("op_386")];
+            tensor<int32, [3]> var_388_reps_0 = const()[name = string("op_388_reps_0"), val = tensor<int32, [3]>([1, 1, 10])];
+            tensor<bool, [1, 188, 10]> var_388 = tile(reps = var_388_reps_0, x = var_386)[name = string("op_388")];
+            tensor<int32, [1]> var_394_axes_0 = const()[name = string("op_394_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_11_to_fp16_dtype_0 = const()[name = string("mask_11_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 188, 10]> var_388_to_fp16 = cast(dtype = mask_11_to_fp16_dtype_0, x = var_388)[name = string("cast_2")];
+            tensor<fp16, [1, 1, 188, 10]> var_394_cast_fp16 = expand_dims(axes = var_394_axes_0, x = var_388_to_fp16)[name = string("op_394_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_13_reps_0 = const()[name = string("expanded_mask_13_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 188, 10]> expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_394_cast_fp16)[name = string("expanded_mask_13_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> input_21_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_21_cast_fp16")];
+            string tensor_15_pad_type_0 = const()[name = string("tensor_15_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> tensor_15_strides_0 = const()[name = string("tensor_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> tensor_15_pad_0 = const()[name = string("tensor_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> tensor_15_dilations_0 = const()[name = string("tensor_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_15_groups_0 = const()[name = string("tensor_15_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 1, 1]> encoder_module_pre_encode_conv_6_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333312))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1382528))))[name = string("encoder_module_pre_encode_conv_6_weight_to_fp16_palettized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_6_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_6_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1382720)))];
+            tensor<fp16, [1, 256, 188, 10]> tensor_15_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_6_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = string("tensor_15_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> input_23_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_23_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> tensor_cast_fp16 = relu(x = input_23_cast_fp16)[name = string("tensor_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> x_21_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("x_21_cast_fp16")];
+            tensor<int32, [4]> var_428_perm_0 = const()[name = string("op_428_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_429 = const()[name = string("op_429"), val = tensor<int32, [3]>([1, 188, -1])];
+            tensor<fp16, [1, 188, 256, 10]> var_428_cast_fp16 = transpose(perm = var_428_perm_0, x = x_21_cast_fp16)[name = string("transpose_314")];
+            tensor<fp16, [1, 188, 2560]> input_25_cast_fp16 = reshape(shape = var_429, x = var_428_cast_fp16)[name = string("input_25_cast_fp16")];
+            tensor<fp16, [1024, 2560]> encoder_module_pre_encode_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1383296))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3349440))))[name = string("encoder_module_pre_encode_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_pre_encode_out_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3349632)))];
+            tensor<fp16, [1, 188, 1024]> linear_0_cast_fp16 = linear(bias = encoder_module_pre_encode_out_bias_to_fp16, weight = encoder_module_pre_encode_out_weight_to_fp16_palettized, x = input_25_cast_fp16)[name = string("linear_0_cast_fp16")];
+            string padding_length_dtype_0 = const()[name = string("padding_length_dtype_0"), val = string("int32")];
+            fp16 var_440_to_fp16 = const()[name = string("op_440_to_fp16"), val = fp16(0x1p+5)];
+            tensor<fp16, [1, 188, 1024]> x_23_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_440_to_fp16)[name = string("x_23_cast_fp16")];
+            tensor<int32, [1]> var_469_axes_0 = const()[name = string("op_469_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<int32, [1]> encoder_length = cast(dtype = padding_length_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_1")];
+            tensor<int32, [1, 1]> var_469 = expand_dims(axes = var_469_axes_0, x = encoder_length)[name = string("op_469")];
+            tensor<bool, [1, 188]> pad_mask_1 = less(x = expand_dims_6, y = var_469)[name = string("pad_mask_1")];
+            tensor<int32, [1]> var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188]> var_471 = expand_dims(axes = var_471_axes_0, x = pad_mask_1)[name = string("op_471")];
+            tensor<int32, [3]> var_472 = const()[name = string("op_472"), val = tensor<int32, [3]>([1, 188, 1])];
+            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask_1 = tile(reps = var_472, x = var_471)[name = string("pad_mask_for_att_mask_1")];
+            tensor<int32, [3]> var_474_perm_0 = const()[name = string("op_474_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<bool, [1, 188, 188]> var_474 = transpose(perm = var_474_perm_0, x = pad_mask_for_att_mask_1)[name = string("transpose_313")];
+            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_474)[name = string("pad_mask_for_att_mask")];
+            tensor<bool, [1, 188, 188]> const_81 = const()[name = string("const_81"), val = tensor<bool, [1, 188, 188]>([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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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, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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<bool, [1, 188, 188]> att_mask = logical_and(x = pad_mask_for_att_mask, y = const_81)[name = string("att_mask")];
+            tensor<bool, [1, 188, 188]> mask_13 = logical_not(x = att_mask)[name = string("mask_13")];
+            tensor<bool, [1, 188]> pad_mask = logical_not(x = pad_mask_1)[name = string("pad_mask")];
+            tensor<int32, [1]> input_29_axes_0 = const()[name = string("input_29_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3351744)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3353856)))];
+            fp16 var_166_to_fp16 = const()[name = string("op_166_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 188, 1024]> input_29_cast_fp16 = layer_norm(axes = input_29_axes_0, beta = encoder_module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_feed_forward1_weight_to_fp16, x = x_23_cast_fp16)[name = string("input_29_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3355968))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6501760))))[name = string("encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6501952)))];
+            tensor<fp16, [1, 188, 4096]> linear_1_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16_palettized, x = input_29_cast_fp16)[name = string("linear_1_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_33_cast_fp16 = silu(x = linear_1_cast_fp16)[name = string("input_33_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6510208))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9656000))))[name = string("encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9656192)))];
+            tensor<fp16, [1, 188, 1024]> linear_2_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16_palettized, x = input_33_cast_fp16)[name = string("linear_2_cast_fp16")];
+            fp16 var_507_to_fp16 = const()[name = string("op_507_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_508_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_507_to_fp16)[name = string("op_508_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_39_cast_fp16 = add(x = x_23_cast_fp16, y = var_508_cast_fp16)[name = string("input_39_cast_fp16")];
+            tensor<int32, [1]> query_1_axes_0 = const()[name = string("query_1_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9658304)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9660416)))];
+            tensor<fp16, [1, 188, 1024]> query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = encoder_module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_self_att_weight_to_fp16, x = input_39_cast_fp16)[name = string("query_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9662528))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10449024))))[name = string("encoder_module_layers_0_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10449216)))];
+            tensor<fp16, [1, 188, 1024]> linear_3_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_q_weight_to_fp16_palettized, x = query_1_cast_fp16)[name = string("linear_3_cast_fp16")];
+            tensor<int32, [4]> var_525 = const()[name = string("op_525"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_1_cast_fp16 = reshape(shape = var_525, x = linear_3_cast_fp16)[name = string("q_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10451328))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11237824))))[name = string("encoder_module_layers_0_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11238016)))];
+            tensor<fp16, [1, 188, 1024]> linear_4_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_k_weight_to_fp16_palettized, x = query_1_cast_fp16)[name = string("linear_4_cast_fp16")];
+            tensor<int32, [4]> var_530 = const()[name = string("op_530"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_1_cast_fp16 = reshape(shape = var_530, x = linear_4_cast_fp16)[name = string("k_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11240128))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12026624))))[name = string("encoder_module_layers_0_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12026816)))];
+            tensor<fp16, [1, 188, 1024]> linear_5_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_v_weight_to_fp16_palettized, x = query_1_cast_fp16)[name = string("linear_5_cast_fp16")];
+            tensor<int32, [4]> var_535 = const()[name = string("op_535"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_1_cast_fp16 = reshape(shape = var_535, x = linear_5_cast_fp16)[name = string("v_1_cast_fp16")];
+            tensor<int32, [4]> value_5_perm_0 = const()[name = string("value_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12028928)))];
+            tensor<fp16, [1, 188, 8, 128]> var_547_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_module_layers_0_self_attn_pos_bias_u_to_fp16)[name = string("op_547_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12031040)))];
+            tensor<fp16, [1, 188, 8, 128]> var_549_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_module_layers_0_self_attn_pos_bias_v_to_fp16)[name = string("op_549_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_1_perm_0 = const()[name = string("q_with_bias_v_1_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_27_transpose_x_0 = const()[name = string("x_27_transpose_x_0"), val = bool(false)];
+            bool x_27_transpose_y_0 = const()[name = string("x_27_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_551_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12033152))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12321216))))[name = string("op_551_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_549_cast_fp16)[name = string("transpose_312")];
+            tensor<fp16, [1, 8, 188, 375]> x_27_cast_fp16 = matmul(transpose_x = x_27_transpose_x_0, transpose_y = x_27_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = op_551_to_fp16_palettized)[name = string("x_27_cast_fp16")];
+            tensor<int32, [8]> x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_29_mode_0 = const()[name = string("x_29_mode_0"), val = string("constant")];
+            fp16 const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_29_cast_fp16 = pad(constant_val = const_88_to_fp16, mode = x_29_mode_0, pad = x_29_pad_0, x = x_27_cast_fp16)[name = string("x_29_cast_fp16")];
+            tensor<int32, [4]> var_559 = const()[name = string("op_559"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_31_cast_fp16 = reshape(shape = var_559, x = x_29_cast_fp16)[name = string("x_31_cast_fp16")];
+            tensor<int32, [4]> var_563_begin_0 = const()[name = string("op_563_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_563_end_0 = const()[name = string("op_563_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_563_end_mask_0 = const()[name = string("op_563_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_563_cast_fp16 = slice_by_index(begin = var_563_begin_0, end = var_563_end_0, end_mask = var_563_end_mask_0, x = x_31_cast_fp16)[name = string("op_563_cast_fp16")];
+            tensor<int32, [4]> var_564 = const()[name = string("op_564"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_1_cast_fp16 = reshape(shape = var_564, x = var_563_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<int32, [4]> transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = string("transpose_310")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_96 = transpose(perm = transpose_96_perm_0, x = var_547_cast_fp16)[name = string("transpose_311")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_3_end_0 = const()[name = string("matrix_bd_3_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_573_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = string("op_573_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<fp16, [1, 8, 188, 188]> _inversed_scores_1_cast_fp16 = mul(x = var_573_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")];
+            tensor<int32, [1]> mask_15_axes_0 = const()[name = string("mask_15_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188, 188]> mask_15 = expand_dims(axes = mask_15_axes_0, x = mask_13)[name = string("mask_15")];
+            fp16 var_163_to_fp16 = const()[name = string("op_163_to_fp16"), val = fp16(-0x1.388p+13)];
+            tensor<fp16, [1, 8, 188, 188]> scores_3_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_15)[name = string("scores_3_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_579_cast_fp16 = softmax(axis = var_152, x = scores_3_cast_fp16)[name = string("op_579_cast_fp16")];
+            fp16 var_164_to_fp16 = const()[name = string("op_164_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 188]> input_41_cast_fp16 = select(a = var_164_to_fp16, b = var_579_cast_fp16, cond = mask_15)[name = string("input_41_cast_fp16")];
+            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<fp16, [1, 8, 188, 128]> value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_1_cast_fp16)[name = string("transpose_309")];
+            tensor<fp16, [1, 8, 188, 128]> x_33_cast_fp16 = matmul(transpose_x = x_33_transpose_x_0, transpose_y = x_33_transpose_y_0, x = input_41_cast_fp16, y = value_5_cast_fp16)[name = string("x_33_cast_fp16")];
+            tensor<int32, [4]> var_583_perm_0 = const()[name = string("op_583_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_584 = const()[name = string("op_584"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_583_cast_fp16 = transpose(perm = var_583_perm_0, x = x_33_cast_fp16)[name = string("transpose_308")];
+            tensor<fp16, [1, 188, 1024]> input_43_cast_fp16 = reshape(shape = var_584, x = var_583_cast_fp16)[name = string("input_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12321408))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13107904))))[name = string("encoder_module_layers_0_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13108096)))];
+            tensor<fp16, [1, 188, 1024]> linear_7_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_out_weight_to_fp16_palettized, x = input_43_cast_fp16)[name = string("linear_7_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_47_cast_fp16 = add(x = input_39_cast_fp16, y = linear_7_cast_fp16)[name = string("input_47_cast_fp16")];
+            tensor<int32, [1]> x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13110208)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13112320)))];
+            tensor<fp16, [1, 188, 1024]> x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, beta = encoder_module_layers_0_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_conv_weight_to_fp16, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")];
+            tensor<int32, [3]> input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_0_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13114432))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14687360))))[name = string("encoder_module_layers_0_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14687552)))];
+            tensor<fp16, [1, 1024, 188]> input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_37_cast_fp16)[name = string("transpose_307")];
+            tensor<fp16, [1, 2048, 188]> input_51_cast_fp16 = conv(bias = encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_0_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")];
+            int32 x_39_split_num_splits_0 = const()[name = string("x_39_split_num_splits_0"), val = int32(2)];
+            int32 x_39_split_axis_0 = const()[name = string("x_39_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_39_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_39_split_cast_fp16_1 = split(axis = x_39_split_axis_0, num_splits = x_39_split_num_splits_0, x = input_51_cast_fp16)[name = string("x_39_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_39_split_1_sigmoid_cast_fp16 = sigmoid(x = x_39_split_cast_fp16_1)[name = string("x_39_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_39_cast_fp16 = mul(x = x_39_split_cast_fp16_0, y = x_39_split_1_sigmoid_cast_fp16)[name = string("x_39_cast_fp16")];
+            tensor<int32, [1]> var_608_axes_0 = const()[name = string("op_608_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188]> var_608 = expand_dims(axes = var_608_axes_0, x = pad_mask)[name = string("op_608")];
+            tensor<fp16, [1, 1024, 188]> input_53_cast_fp16 = select(a = var_164_to_fp16, b = x_39_cast_fp16, cond = var_608)[name = string("input_53_cast_fp16")];
+            tensor<int32, [6]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("constant")];
+            fp16 const_91_to_fp16 = const()[name = string("const_91_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_55_cast_fp16 = pad(constant_val = const_91_to_fp16, mode = input_55_mode_0, pad = input_55_pad_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")];
+            string input_57_pad_type_0 = const()[name = string("input_57_pad_type_0"), val = string("valid")];
+            int32 input_57_groups_0 = const()[name = string("input_57_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_57_strides_0 = const()[name = string("input_57_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_57_dilations_0 = const()[name = string("input_57_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_322_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14691712))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14698688))))[name = string("const_322_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_323_to_fp16 = const()[name = string("const_323_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14698880)))];
+            tensor<fp16, [1, 1024, 188]> input_59_cast_fp16 = conv(bias = const_323_to_fp16, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_322_to_fp16_palettized, x = input_55_cast_fp16)[name = string("input_59_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_61_cast_fp16 = silu(x = input_59_cast_fp16)[name = string("input_61_cast_fp16")];
+            string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14700992))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15487488))))[name = string("encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15487680)))];
+            tensor<fp16, [1, 1024, 188]> x_41_cast_fp16 = conv(bias = encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_61_cast_fp16)[name = string("x_41_cast_fp16")];
+            tensor<int32, [3]> input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_41_cast_fp16)[name = string("transpose_306")];
+            tensor<fp16, [1, 188, 1024]> input_65_cast_fp16 = add(x = input_47_cast_fp16, y = input_63_cast_fp16)[name = string("input_65_cast_fp16")];
+            tensor<int32, [1]> input_67_axes_0 = const()[name = string("input_67_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15489792)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15491904)))];
+            tensor<fp16, [1, 188, 1024]> input_67_cast_fp16 = layer_norm(axes = input_67_axes_0, beta = encoder_module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15494016))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18639808))))[name = string("encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18640000)))];
+            tensor<fp16, [1, 188, 4096]> linear_8_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16_palettized, x = input_67_cast_fp16)[name = string("linear_8_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_71_cast_fp16 = silu(x = linear_8_cast_fp16)[name = string("input_71_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18648256))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21794048))))[name = string("encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21794240)))];
+            tensor<fp16, [1, 188, 1024]> linear_9_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_9_cast_fp16")];
+            fp16 var_650_to_fp16 = const()[name = string("op_650_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_651_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_650_to_fp16)[name = string("op_651_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_77_cast_fp16 = add(x = input_65_cast_fp16, y = var_651_cast_fp16)[name = string("input_77_cast_fp16")];
+            tensor<int32, [1]> input_79_axes_0 = const()[name = string("input_79_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21796352)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21798464)))];
+            tensor<fp16, [1, 188, 1024]> input_79_cast_fp16 = layer_norm(axes = input_79_axes_0, beta = encoder_module_layers_0_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_out_weight_to_fp16, x = input_77_cast_fp16)[name = string("input_79_cast_fp16")];
+            tensor<int32, [1]> input_81_axes_0 = const()[name = string("input_81_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21800576)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21802688)))];
+            tensor<fp16, [1, 188, 1024]> input_81_cast_fp16 = layer_norm(axes = input_81_axes_0, beta = encoder_module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_79_cast_fp16)[name = string("input_81_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804800))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24950592))))[name = string("encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24950784)))];
+            tensor<fp16, [1, 188, 4096]> linear_10_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16_palettized, x = input_81_cast_fp16)[name = string("linear_10_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_85_cast_fp16 = silu(x = linear_10_cast_fp16)[name = string("input_85_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24959040))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28104832))))[name = string("encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28105024)))];
+            tensor<fp16, [1, 188, 1024]> linear_11_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16_palettized, x = input_85_cast_fp16)[name = string("linear_11_cast_fp16")];
+            fp16 var_681_to_fp16 = const()[name = string("op_681_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_682_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_681_to_fp16)[name = string("op_682_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_91_cast_fp16 = add(x = input_79_cast_fp16, y = var_682_cast_fp16)[name = string("input_91_cast_fp16")];
+            tensor<int32, [1]> query_3_axes_0 = const()[name = string("query_3_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28107136)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28109248)))];
+            tensor<fp16, [1, 188, 1024]> query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = encoder_module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_self_att_weight_to_fp16, x = input_91_cast_fp16)[name = string("query_3_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28111360))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28897856))))[name = string("encoder_module_layers_1_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28898048)))];
+            tensor<fp16, [1, 188, 1024]> linear_12_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_q_weight_to_fp16_palettized, x = query_3_cast_fp16)[name = string("linear_12_cast_fp16")];
+            tensor<int32, [4]> var_699 = const()[name = string("op_699"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_7_cast_fp16 = reshape(shape = var_699, x = linear_12_cast_fp16)[name = string("q_7_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28900160))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29686656))))[name = string("encoder_module_layers_1_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29686848)))];
+            tensor<fp16, [1, 188, 1024]> linear_13_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_k_weight_to_fp16_palettized, x = query_3_cast_fp16)[name = string("linear_13_cast_fp16")];
+            tensor<int32, [4]> var_704 = const()[name = string("op_704"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_5_cast_fp16 = reshape(shape = var_704, x = linear_13_cast_fp16)[name = string("k_5_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29688960))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30475456))))[name = string("encoder_module_layers_1_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30475648)))];
+            tensor<fp16, [1, 188, 1024]> linear_14_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_v_weight_to_fp16_palettized, x = query_3_cast_fp16)[name = string("linear_14_cast_fp16")];
+            tensor<int32, [4]> var_709 = const()[name = string("op_709"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_3_cast_fp16 = reshape(shape = var_709, x = linear_14_cast_fp16)[name = string("v_3_cast_fp16")];
+            tensor<int32, [4]> value_7_perm_0 = const()[name = string("value_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30477760)))];
+            tensor<fp16, [1, 188, 8, 128]> var_721_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_module_layers_1_self_attn_pos_bias_u_to_fp16)[name = string("op_721_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30479872)))];
+            tensor<fp16, [1, 188, 8, 128]> var_723_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_module_layers_1_self_attn_pos_bias_v_to_fp16)[name = string("op_723_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_3_perm_0 = const()[name = string("q_with_bias_v_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_49_transpose_x_0 = const()[name = string("x_49_transpose_x_0"), val = bool(false)];
+            bool x_49_transpose_y_0 = const()[name = string("x_49_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_725_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30481984))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30770048))))[name = string("op_725_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_723_cast_fp16)[name = string("transpose_305")];
+            tensor<fp16, [1, 8, 188, 375]> x_49_cast_fp16 = matmul(transpose_x = x_49_transpose_x_0, transpose_y = x_49_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = op_725_to_fp16_palettized)[name = string("x_49_cast_fp16")];
+            tensor<int32, [8]> x_51_pad_0 = const()[name = string("x_51_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_51_mode_0 = const()[name = string("x_51_mode_0"), val = string("constant")];
+            fp16 const_98_to_fp16 = const()[name = string("const_98_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_51_cast_fp16 = pad(constant_val = const_98_to_fp16, mode = x_51_mode_0, pad = x_51_pad_0, x = x_49_cast_fp16)[name = string("x_51_cast_fp16")];
+            tensor<int32, [4]> var_733 = const()[name = string("op_733"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_53_cast_fp16 = reshape(shape = var_733, x = x_51_cast_fp16)[name = string("x_53_cast_fp16")];
+            tensor<int32, [4]> var_737_begin_0 = const()[name = string("op_737_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_737_end_0 = const()[name = string("op_737_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_737_end_mask_0 = const()[name = string("op_737_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_737_cast_fp16 = slice_by_index(begin = var_737_begin_0, end = var_737_end_0, end_mask = var_737_end_mask_0, x = x_53_cast_fp16)[name = string("op_737_cast_fp16")];
+            tensor<int32, [4]> var_738 = const()[name = string("op_738"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_5_cast_fp16 = reshape(shape = var_738, x = var_737_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<int32, [4]> transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = string("transpose_303")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_98 = transpose(perm = transpose_98_perm_0, x = var_721_cast_fp16)[name = string("transpose_304")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_7_end_0 = const()[name = string("matrix_bd_7_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_747_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = string("op_747_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<fp16, [1, 8, 188, 188]> _inversed_scores_5_cast_fp16 = mul(x = var_747_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_7_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_15)[name = string("scores_7_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_753_cast_fp16 = softmax(axis = var_152, x = scores_7_cast_fp16)[name = string("op_753_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_93_cast_fp16 = select(a = var_164_to_fp16, b = var_753_cast_fp16, cond = mask_15)[name = string("input_93_cast_fp16")];
+            bool x_55_transpose_x_0 = const()[name = string("x_55_transpose_x_0"), val = bool(false)];
+            bool x_55_transpose_y_0 = const()[name = string("x_55_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_3_cast_fp16)[name = string("transpose_302")];
+            tensor<fp16, [1, 8, 188, 128]> x_55_cast_fp16 = matmul(transpose_x = x_55_transpose_x_0, transpose_y = x_55_transpose_y_0, x = input_93_cast_fp16, y = value_7_cast_fp16)[name = string("x_55_cast_fp16")];
+            tensor<int32, [4]> var_757_perm_0 = const()[name = string("op_757_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_758 = const()[name = string("op_758"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_757_cast_fp16 = transpose(perm = var_757_perm_0, x = x_55_cast_fp16)[name = string("transpose_301")];
+            tensor<fp16, [1, 188, 1024]> input_95_cast_fp16 = reshape(shape = var_758, x = var_757_cast_fp16)[name = string("input_95_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30770240))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31556736))))[name = string("encoder_module_layers_1_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31556928)))];
+            tensor<fp16, [1, 188, 1024]> linear_16_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_out_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_16_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_99_cast_fp16 = add(x = input_91_cast_fp16, y = linear_16_cast_fp16)[name = string("input_99_cast_fp16")];
+            tensor<int32, [1]> x_59_axes_0 = const()[name = string("x_59_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31559040)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31561152)))];
+            tensor<fp16, [1, 188, 1024]> x_59_cast_fp16 = layer_norm(axes = x_59_axes_0, beta = encoder_module_layers_1_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_conv_weight_to_fp16, x = input_99_cast_fp16)[name = string("x_59_cast_fp16")];
+            tensor<int32, [3]> input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_1_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31563264))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33136192))))[name = string("encoder_module_layers_1_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33136384)))];
+            tensor<fp16, [1, 1024, 188]> input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_59_cast_fp16)[name = string("transpose_300")];
+            tensor<fp16, [1, 2048, 188]> input_103_cast_fp16 = conv(bias = encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_1_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")];
+            int32 x_61_split_num_splits_0 = const()[name = string("x_61_split_num_splits_0"), val = int32(2)];
+            int32 x_61_split_axis_0 = const()[name = string("x_61_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_61_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_61_split_cast_fp16_1 = split(axis = x_61_split_axis_0, num_splits = x_61_split_num_splits_0, x = input_103_cast_fp16)[name = string("x_61_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_61_split_1_sigmoid_cast_fp16 = sigmoid(x = x_61_split_cast_fp16_1)[name = string("x_61_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_61_cast_fp16 = mul(x = x_61_split_cast_fp16_0, y = x_61_split_1_sigmoid_cast_fp16)[name = string("x_61_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_105_cast_fp16 = select(a = var_164_to_fp16, b = x_61_cast_fp16, cond = var_608)[name = string("input_105_cast_fp16")];
+            tensor<int32, [6]> input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("constant")];
+            fp16 const_101_to_fp16 = const()[name = string("const_101_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_107_cast_fp16 = pad(constant_val = const_101_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")];
+            string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("valid")];
+            int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_324_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33140544))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33147520))))[name = string("const_324_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_325_to_fp16 = const()[name = string("const_325_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33147712)))];
+            tensor<fp16, [1, 1024, 188]> input_111_cast_fp16 = conv(bias = const_325_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_324_to_fp16_palettized, x = input_107_cast_fp16)[name = string("input_111_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_113_cast_fp16 = silu(x = input_111_cast_fp16)[name = string("input_113_cast_fp16")];
+            string x_63_pad_type_0 = const()[name = string("x_63_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_63_strides_0 = const()[name = string("x_63_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_63_pad_0 = const()[name = string("x_63_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_63_dilations_0 = const()[name = string("x_63_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_63_groups_0 = const()[name = string("x_63_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33149824))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33936320))))[name = string("encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33936512)))];
+            tensor<fp16, [1, 1024, 188]> x_63_cast_fp16 = conv(bias = encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16, dilations = x_63_dilations_0, groups = x_63_groups_0, pad = x_63_pad_0, pad_type = x_63_pad_type_0, strides = x_63_strides_0, weight = encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_113_cast_fp16)[name = string("x_63_cast_fp16")];
+            tensor<int32, [3]> input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_63_cast_fp16)[name = string("transpose_299")];
+            tensor<fp16, [1, 188, 1024]> input_117_cast_fp16 = add(x = input_99_cast_fp16, y = input_115_cast_fp16)[name = string("input_117_cast_fp16")];
+            tensor<int32, [1]> input_119_axes_0 = const()[name = string("input_119_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33938624)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33940736)))];
+            tensor<fp16, [1, 188, 1024]> input_119_cast_fp16 = layer_norm(axes = input_119_axes_0, beta = encoder_module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33942848))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37088640))))[name = string("encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37088832)))];
+            tensor<fp16, [1, 188, 4096]> linear_17_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("linear_17_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_123_cast_fp16 = silu(x = linear_17_cast_fp16)[name = string("input_123_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37097088))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40242880))))[name = string("encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40243072)))];
+            tensor<fp16, [1, 188, 1024]> linear_18_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = string("linear_18_cast_fp16")];
+            fp16 var_824_to_fp16 = const()[name = string("op_824_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_825_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_824_to_fp16)[name = string("op_825_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_129_cast_fp16 = add(x = input_117_cast_fp16, y = var_825_cast_fp16)[name = string("input_129_cast_fp16")];
+            tensor<int32, [1]> input_131_axes_0 = const()[name = string("input_131_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40245184)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40247296)))];
+            tensor<fp16, [1, 188, 1024]> input_131_cast_fp16 = layer_norm(axes = input_131_axes_0, beta = encoder_module_layers_1_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_out_weight_to_fp16, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")];
+            tensor<int32, [1]> input_133_axes_0 = const()[name = string("input_133_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40249408)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40251520)))];
+            tensor<fp16, [1, 188, 1024]> input_133_cast_fp16 = layer_norm(axes = input_133_axes_0, beta = encoder_module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_131_cast_fp16)[name = string("input_133_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40253632))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43399424))))[name = string("encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43399616)))];
+            tensor<fp16, [1, 188, 4096]> linear_19_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16_palettized, x = input_133_cast_fp16)[name = string("linear_19_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_137_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("input_137_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43407872))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46553664))))[name = string("encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46553856)))];
+            tensor<fp16, [1, 188, 1024]> linear_20_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16_palettized, x = input_137_cast_fp16)[name = string("linear_20_cast_fp16")];
+            fp16 var_855_to_fp16 = const()[name = string("op_855_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_856_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_855_to_fp16)[name = string("op_856_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_143_cast_fp16 = add(x = input_131_cast_fp16, y = var_856_cast_fp16)[name = string("input_143_cast_fp16")];
+            tensor<int32, [1]> query_5_axes_0 = const()[name = string("query_5_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46555968)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46558080)))];
+            tensor<fp16, [1, 188, 1024]> query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = encoder_module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_self_att_weight_to_fp16, x = input_143_cast_fp16)[name = string("query_5_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46560192))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47346688))))[name = string("encoder_module_layers_2_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47346880)))];
+            tensor<fp16, [1, 188, 1024]> linear_21_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_q_weight_to_fp16_palettized, x = query_5_cast_fp16)[name = string("linear_21_cast_fp16")];
+            tensor<int32, [4]> var_873 = const()[name = string("op_873"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_13_cast_fp16 = reshape(shape = var_873, x = linear_21_cast_fp16)[name = string("q_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47348992))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48135488))))[name = string("encoder_module_layers_2_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48135680)))];
+            tensor<fp16, [1, 188, 1024]> linear_22_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_k_weight_to_fp16_palettized, x = query_5_cast_fp16)[name = string("linear_22_cast_fp16")];
+            tensor<int32, [4]> var_878 = const()[name = string("op_878"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_9_cast_fp16 = reshape(shape = var_878, x = linear_22_cast_fp16)[name = string("k_9_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48137792))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48924288))))[name = string("encoder_module_layers_2_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48924480)))];
+            tensor<fp16, [1, 188, 1024]> linear_23_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_v_weight_to_fp16_palettized, x = query_5_cast_fp16)[name = string("linear_23_cast_fp16")];
+            tensor<int32, [4]> var_883 = const()[name = string("op_883"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_5_cast_fp16 = reshape(shape = var_883, x = linear_23_cast_fp16)[name = string("v_5_cast_fp16")];
+            tensor<int32, [4]> value_9_perm_0 = const()[name = string("value_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48926592)))];
+            tensor<fp16, [1, 188, 8, 128]> var_895_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_module_layers_2_self_attn_pos_bias_u_to_fp16)[name = string("op_895_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48928704)))];
+            tensor<fp16, [1, 188, 8, 128]> var_897_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_module_layers_2_self_attn_pos_bias_v_to_fp16)[name = string("op_897_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_5_perm_0 = const()[name = string("q_with_bias_v_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_71_transpose_x_0 = const()[name = string("x_71_transpose_x_0"), val = bool(false)];
+            bool x_71_transpose_y_0 = const()[name = string("x_71_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_899_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48930816))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49218880))))[name = string("op_899_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_897_cast_fp16)[name = string("transpose_298")];
+            tensor<fp16, [1, 8, 188, 375]> x_71_cast_fp16 = matmul(transpose_x = x_71_transpose_x_0, transpose_y = x_71_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = op_899_to_fp16_palettized)[name = string("x_71_cast_fp16")];
+            tensor<int32, [8]> x_73_pad_0 = const()[name = string("x_73_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("constant")];
+            fp16 const_108_to_fp16 = const()[name = string("const_108_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_73_cast_fp16 = pad(constant_val = const_108_to_fp16, mode = x_73_mode_0, pad = x_73_pad_0, x = x_71_cast_fp16)[name = string("x_73_cast_fp16")];
+            tensor<int32, [4]> var_907 = const()[name = string("op_907"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_75_cast_fp16 = reshape(shape = var_907, x = x_73_cast_fp16)[name = string("x_75_cast_fp16")];
+            tensor<int32, [4]> var_911_begin_0 = const()[name = string("op_911_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_911_end_0 = const()[name = string("op_911_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_911_end_mask_0 = const()[name = string("op_911_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_911_cast_fp16 = slice_by_index(begin = var_911_begin_0, end = var_911_end_0, end_mask = var_911_end_mask_0, x = x_75_cast_fp16)[name = string("op_911_cast_fp16")];
+            tensor<int32, [4]> var_912 = const()[name = string("op_912"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_9_cast_fp16 = reshape(shape = var_912, x = var_911_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<int32, [4]> transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = string("transpose_296")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_100 = transpose(perm = transpose_100_perm_0, x = var_895_cast_fp16)[name = string("transpose_297")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_11_end_0 = const()[name = string("matrix_bd_11_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_921_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = string("op_921_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<fp16, [1, 8, 188, 188]> _inversed_scores_9_cast_fp16 = mul(x = var_921_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_11_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_15)[name = string("scores_11_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_927_cast_fp16 = softmax(axis = var_152, x = scores_11_cast_fp16)[name = string("op_927_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_145_cast_fp16 = select(a = var_164_to_fp16, b = var_927_cast_fp16, cond = mask_15)[name = string("input_145_cast_fp16")];
+            bool x_77_transpose_x_0 = const()[name = string("x_77_transpose_x_0"), val = bool(false)];
+            bool x_77_transpose_y_0 = const()[name = string("x_77_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_5_cast_fp16)[name = string("transpose_295")];
+            tensor<fp16, [1, 8, 188, 128]> x_77_cast_fp16 = matmul(transpose_x = x_77_transpose_x_0, transpose_y = x_77_transpose_y_0, x = input_145_cast_fp16, y = value_9_cast_fp16)[name = string("x_77_cast_fp16")];
+            tensor<int32, [4]> var_931_perm_0 = const()[name = string("op_931_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_932 = const()[name = string("op_932"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_931_cast_fp16 = transpose(perm = var_931_perm_0, x = x_77_cast_fp16)[name = string("transpose_294")];
+            tensor<fp16, [1, 188, 1024]> input_147_cast_fp16 = reshape(shape = var_932, x = var_931_cast_fp16)[name = string("input_147_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49219072))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50005568))))[name = string("encoder_module_layers_2_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50005760)))];
+            tensor<fp16, [1, 188, 1024]> linear_25_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_out_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("linear_25_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_151_cast_fp16 = add(x = input_143_cast_fp16, y = linear_25_cast_fp16)[name = string("input_151_cast_fp16")];
+            tensor<int32, [1]> x_81_axes_0 = const()[name = string("x_81_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50007872)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50009984)))];
+            tensor<fp16, [1, 188, 1024]> x_81_cast_fp16 = layer_norm(axes = x_81_axes_0, beta = encoder_module_layers_2_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_conv_weight_to_fp16, x = input_151_cast_fp16)[name = string("x_81_cast_fp16")];
+            tensor<int32, [3]> input_153_perm_0 = const()[name = string("input_153_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_2_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50012096))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51585024))))[name = string("encoder_module_layers_2_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51585216)))];
+            tensor<fp16, [1, 1024, 188]> input_153_cast_fp16 = transpose(perm = input_153_perm_0, x = x_81_cast_fp16)[name = string("transpose_293")];
+            tensor<fp16, [1, 2048, 188]> input_155_cast_fp16 = conv(bias = encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_2_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_153_cast_fp16)[name = string("input_155_cast_fp16")];
+            int32 x_83_split_num_splits_0 = const()[name = string("x_83_split_num_splits_0"), val = int32(2)];
+            int32 x_83_split_axis_0 = const()[name = string("x_83_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_83_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_83_split_cast_fp16_1 = split(axis = x_83_split_axis_0, num_splits = x_83_split_num_splits_0, x = input_155_cast_fp16)[name = string("x_83_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_83_split_1_sigmoid_cast_fp16 = sigmoid(x = x_83_split_cast_fp16_1)[name = string("x_83_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_83_cast_fp16 = mul(x = x_83_split_cast_fp16_0, y = x_83_split_1_sigmoid_cast_fp16)[name = string("x_83_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_157_cast_fp16 = select(a = var_164_to_fp16, b = x_83_cast_fp16, cond = var_608)[name = string("input_157_cast_fp16")];
+            tensor<int32, [6]> input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_159_mode_0 = const()[name = string("input_159_mode_0"), val = string("constant")];
+            fp16 const_111_to_fp16 = const()[name = string("const_111_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_159_cast_fp16 = pad(constant_val = const_111_to_fp16, mode = input_159_mode_0, pad = input_159_pad_0, x = input_157_cast_fp16)[name = string("input_159_cast_fp16")];
+            string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("valid")];
+            int32 input_161_groups_0 = const()[name = string("input_161_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_161_strides_0 = const()[name = string("input_161_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_161_dilations_0 = const()[name = string("input_161_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_326_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51589376))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51596352))))[name = string("const_326_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_327_to_fp16 = const()[name = string("const_327_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51596544)))];
+            tensor<fp16, [1, 1024, 188]> input_163_cast_fp16 = conv(bias = const_327_to_fp16, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_326_to_fp16_palettized, x = input_159_cast_fp16)[name = string("input_163_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_165_cast_fp16 = silu(x = input_163_cast_fp16)[name = string("input_165_cast_fp16")];
+            string x_85_pad_type_0 = const()[name = string("x_85_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_85_strides_0 = const()[name = string("x_85_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_85_pad_0 = const()[name = string("x_85_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_85_dilations_0 = const()[name = string("x_85_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_85_groups_0 = const()[name = string("x_85_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51598656))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52385152))))[name = string("encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52385344)))];
+            tensor<fp16, [1, 1024, 188]> x_85_cast_fp16 = conv(bias = encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16, dilations = x_85_dilations_0, groups = x_85_groups_0, pad = x_85_pad_0, pad_type = x_85_pad_type_0, strides = x_85_strides_0, weight = encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_165_cast_fp16)[name = string("x_85_cast_fp16")];
+            tensor<int32, [3]> input_167_perm_0 = const()[name = string("input_167_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_167_cast_fp16 = transpose(perm = input_167_perm_0, x = x_85_cast_fp16)[name = string("transpose_292")];
+            tensor<fp16, [1, 188, 1024]> input_169_cast_fp16 = add(x = input_151_cast_fp16, y = input_167_cast_fp16)[name = string("input_169_cast_fp16")];
+            tensor<int32, [1]> input_171_axes_0 = const()[name = string("input_171_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52387456)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52389568)))];
+            tensor<fp16, [1, 188, 1024]> input_171_cast_fp16 = layer_norm(axes = input_171_axes_0, beta = encoder_module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_169_cast_fp16)[name = string("input_171_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52391680))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55537472))))[name = string("encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55537664)))];
+            tensor<fp16, [1, 188, 4096]> linear_26_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16_palettized, x = input_171_cast_fp16)[name = string("linear_26_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_175_cast_fp16 = silu(x = linear_26_cast_fp16)[name = string("input_175_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55545920))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58691712))))[name = string("encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58691904)))];
+            tensor<fp16, [1, 188, 1024]> linear_27_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16_palettized, x = input_175_cast_fp16)[name = string("linear_27_cast_fp16")];
+            fp16 var_998_to_fp16 = const()[name = string("op_998_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_999_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_998_to_fp16)[name = string("op_999_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_181_cast_fp16 = add(x = input_169_cast_fp16, y = var_999_cast_fp16)[name = string("input_181_cast_fp16")];
+            tensor<int32, [1]> input_183_axes_0 = const()[name = string("input_183_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58694016)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58696128)))];
+            tensor<fp16, [1, 188, 1024]> input_183_cast_fp16 = layer_norm(axes = input_183_axes_0, beta = encoder_module_layers_2_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_out_weight_to_fp16, x = input_181_cast_fp16)[name = string("input_183_cast_fp16")];
+            tensor<int32, [1]> input_185_axes_0 = const()[name = string("input_185_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58698240)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58700352)))];
+            tensor<fp16, [1, 188, 1024]> input_185_cast_fp16 = layer_norm(axes = input_185_axes_0, beta = encoder_module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_183_cast_fp16)[name = string("input_185_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58702464))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61848256))))[name = string("encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61848448)))];
+            tensor<fp16, [1, 188, 4096]> linear_28_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = string("linear_28_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_189_cast_fp16 = silu(x = linear_28_cast_fp16)[name = string("input_189_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61856704))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65002496))))[name = string("encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65002688)))];
+            tensor<fp16, [1, 188, 1024]> linear_29_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16_palettized, x = input_189_cast_fp16)[name = string("linear_29_cast_fp16")];
+            fp16 var_1029_to_fp16 = const()[name = string("op_1029_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1030_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_1029_to_fp16)[name = string("op_1030_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_195_cast_fp16 = add(x = input_183_cast_fp16, y = var_1030_cast_fp16)[name = string("input_195_cast_fp16")];
+            tensor<int32, [1]> query_7_axes_0 = const()[name = string("query_7_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65004800)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65006912)))];
+            tensor<fp16, [1, 188, 1024]> query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = encoder_module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_self_att_weight_to_fp16, x = input_195_cast_fp16)[name = string("query_7_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65009024))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65795520))))[name = string("encoder_module_layers_3_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65795712)))];
+            tensor<fp16, [1, 188, 1024]> linear_30_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_q_weight_to_fp16_palettized, x = query_7_cast_fp16)[name = string("linear_30_cast_fp16")];
+            tensor<int32, [4]> var_1047 = const()[name = string("op_1047"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_19_cast_fp16 = reshape(shape = var_1047, x = linear_30_cast_fp16)[name = string("q_19_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65797824))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66584320))))[name = string("encoder_module_layers_3_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66584512)))];
+            tensor<fp16, [1, 188, 1024]> linear_31_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_k_weight_to_fp16_palettized, x = query_7_cast_fp16)[name = string("linear_31_cast_fp16")];
+            tensor<int32, [4]> var_1052 = const()[name = string("op_1052"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_13_cast_fp16 = reshape(shape = var_1052, x = linear_31_cast_fp16)[name = string("k_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66586624))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67373120))))[name = string("encoder_module_layers_3_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67373312)))];
+            tensor<fp16, [1, 188, 1024]> linear_32_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_v_weight_to_fp16_palettized, x = query_7_cast_fp16)[name = string("linear_32_cast_fp16")];
+            tensor<int32, [4]> var_1057 = const()[name = string("op_1057"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_7_cast_fp16 = reshape(shape = var_1057, x = linear_32_cast_fp16)[name = string("v_7_cast_fp16")];
+            tensor<int32, [4]> value_11_perm_0 = const()[name = string("value_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67375424)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1069_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_module_layers_3_self_attn_pos_bias_u_to_fp16)[name = string("op_1069_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67377536)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1071_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_module_layers_3_self_attn_pos_bias_v_to_fp16)[name = string("op_1071_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_7_perm_0 = const()[name = string("q_with_bias_v_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_93_transpose_x_0 = const()[name = string("x_93_transpose_x_0"), val = bool(false)];
+            bool x_93_transpose_y_0 = const()[name = string("x_93_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1073_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67379648))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67667712))))[name = string("op_1073_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_1071_cast_fp16)[name = string("transpose_291")];
+            tensor<fp16, [1, 8, 188, 375]> x_93_cast_fp16 = matmul(transpose_x = x_93_transpose_x_0, transpose_y = x_93_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = op_1073_to_fp16_palettized)[name = string("x_93_cast_fp16")];
+            tensor<int32, [8]> x_95_pad_0 = const()[name = string("x_95_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("constant")];
+            fp16 const_118_to_fp16 = const()[name = string("const_118_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_95_cast_fp16 = pad(constant_val = const_118_to_fp16, mode = x_95_mode_0, pad = x_95_pad_0, x = x_93_cast_fp16)[name = string("x_95_cast_fp16")];
+            tensor<int32, [4]> var_1081 = const()[name = string("op_1081"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_97_cast_fp16 = reshape(shape = var_1081, x = x_95_cast_fp16)[name = string("x_97_cast_fp16")];
+            tensor<int32, [4]> var_1085_begin_0 = const()[name = string("op_1085_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1085_end_0 = const()[name = string("op_1085_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1085_end_mask_0 = const()[name = string("op_1085_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1085_cast_fp16 = slice_by_index(begin = var_1085_begin_0, end = var_1085_end_0, end_mask = var_1085_end_mask_0, x = x_97_cast_fp16)[name = string("op_1085_cast_fp16")];
+            tensor<int32, [4]> var_1086 = const()[name = string("op_1086"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_13_cast_fp16 = reshape(shape = var_1086, x = var_1085_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<int32, [4]> transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = string("transpose_289")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1069_cast_fp16)[name = string("transpose_290")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_15_end_0 = const()[name = string("matrix_bd_15_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1095_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = string("op_1095_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<fp16, [1, 8, 188, 188]> _inversed_scores_13_cast_fp16 = mul(x = var_1095_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = string("_inversed_scores_13_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_15_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_15)[name = string("scores_15_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1101_cast_fp16 = softmax(axis = var_152, x = scores_15_cast_fp16)[name = string("op_1101_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_197_cast_fp16 = select(a = var_164_to_fp16, b = var_1101_cast_fp16, cond = mask_15)[name = string("input_197_cast_fp16")];
+            bool x_99_transpose_x_0 = const()[name = string("x_99_transpose_x_0"), val = bool(false)];
+            bool x_99_transpose_y_0 = const()[name = string("x_99_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_7_cast_fp16)[name = string("transpose_288")];
+            tensor<fp16, [1, 8, 188, 128]> x_99_cast_fp16 = matmul(transpose_x = x_99_transpose_x_0, transpose_y = x_99_transpose_y_0, x = input_197_cast_fp16, y = value_11_cast_fp16)[name = string("x_99_cast_fp16")];
+            tensor<int32, [4]> var_1105_perm_0 = const()[name = string("op_1105_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1106 = const()[name = string("op_1106"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1105_cast_fp16 = transpose(perm = var_1105_perm_0, x = x_99_cast_fp16)[name = string("transpose_287")];
+            tensor<fp16, [1, 188, 1024]> input_199_cast_fp16 = reshape(shape = var_1106, x = var_1105_cast_fp16)[name = string("input_199_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67667904))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68454400))))[name = string("encoder_module_layers_3_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68454592)))];
+            tensor<fp16, [1, 188, 1024]> linear_34_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_out_weight_to_fp16_palettized, x = input_199_cast_fp16)[name = string("linear_34_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_203_cast_fp16 = add(x = input_195_cast_fp16, y = linear_34_cast_fp16)[name = string("input_203_cast_fp16")];
+            tensor<int32, [1]> x_103_axes_0 = const()[name = string("x_103_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68456704)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68458816)))];
+            tensor<fp16, [1, 188, 1024]> x_103_cast_fp16 = layer_norm(axes = x_103_axes_0, beta = encoder_module_layers_3_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_conv_weight_to_fp16, x = input_203_cast_fp16)[name = string("x_103_cast_fp16")];
+            tensor<int32, [3]> input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_207_pad_type_0 = const()[name = string("input_207_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_207_strides_0 = const()[name = string("input_207_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_207_pad_0 = const()[name = string("input_207_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_207_dilations_0 = const()[name = string("input_207_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_207_groups_0 = const()[name = string("input_207_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_3_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68460928))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70033856))))[name = string("encoder_module_layers_3_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70034048)))];
+            tensor<fp16, [1, 1024, 188]> input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = x_103_cast_fp16)[name = string("transpose_286")];
+            tensor<fp16, [1, 2048, 188]> input_207_cast_fp16 = conv(bias = encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_3_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_205_cast_fp16)[name = string("input_207_cast_fp16")];
+            int32 x_105_split_num_splits_0 = const()[name = string("x_105_split_num_splits_0"), val = int32(2)];
+            int32 x_105_split_axis_0 = const()[name = string("x_105_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_105_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_105_split_cast_fp16_1 = split(axis = x_105_split_axis_0, num_splits = x_105_split_num_splits_0, x = input_207_cast_fp16)[name = string("x_105_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_105_split_1_sigmoid_cast_fp16 = sigmoid(x = x_105_split_cast_fp16_1)[name = string("x_105_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_105_cast_fp16 = mul(x = x_105_split_cast_fp16_0, y = x_105_split_1_sigmoid_cast_fp16)[name = string("x_105_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_209_cast_fp16 = select(a = var_164_to_fp16, b = x_105_cast_fp16, cond = var_608)[name = string("input_209_cast_fp16")];
+            tensor<int32, [6]> input_211_pad_0 = const()[name = string("input_211_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_211_mode_0 = const()[name = string("input_211_mode_0"), val = string("constant")];
+            fp16 const_121_to_fp16 = const()[name = string("const_121_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_211_cast_fp16 = pad(constant_val = const_121_to_fp16, mode = input_211_mode_0, pad = input_211_pad_0, x = input_209_cast_fp16)[name = string("input_211_cast_fp16")];
+            string input_213_pad_type_0 = const()[name = string("input_213_pad_type_0"), val = string("valid")];
+            int32 input_213_groups_0 = const()[name = string("input_213_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_213_strides_0 = const()[name = string("input_213_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_213_dilations_0 = const()[name = string("input_213_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_328_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70038208))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70045184))))[name = string("const_328_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_329_to_fp16 = const()[name = string("const_329_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70045376)))];
+            tensor<fp16, [1, 1024, 188]> input_215_cast_fp16 = conv(bias = const_329_to_fp16, dilations = input_213_dilations_0, groups = input_213_groups_0, pad = input_213_pad_0, pad_type = input_213_pad_type_0, strides = input_213_strides_0, weight = const_328_to_fp16_palettized, x = input_211_cast_fp16)[name = string("input_215_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_217_cast_fp16 = silu(x = input_215_cast_fp16)[name = string("input_217_cast_fp16")];
+            string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70047488))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70833984))))[name = string("encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70834176)))];
+            tensor<fp16, [1, 1024, 188]> x_107_cast_fp16 = conv(bias = encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_217_cast_fp16)[name = string("x_107_cast_fp16")];
+            tensor<int32, [3]> input_219_perm_0 = const()[name = string("input_219_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_219_cast_fp16 = transpose(perm = input_219_perm_0, x = x_107_cast_fp16)[name = string("transpose_285")];
+            tensor<fp16, [1, 188, 1024]> input_221_cast_fp16 = add(x = input_203_cast_fp16, y = input_219_cast_fp16)[name = string("input_221_cast_fp16")];
+            tensor<int32, [1]> input_223_axes_0 = const()[name = string("input_223_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70836288)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70838400)))];
+            tensor<fp16, [1, 188, 1024]> input_223_cast_fp16 = layer_norm(axes = input_223_axes_0, beta = encoder_module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_221_cast_fp16)[name = string("input_223_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70840512))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73986304))))[name = string("encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73986496)))];
+            tensor<fp16, [1, 188, 4096]> linear_35_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16_palettized, x = input_223_cast_fp16)[name = string("linear_35_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_227_cast_fp16 = silu(x = linear_35_cast_fp16)[name = string("input_227_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73994752))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77140544))))[name = string("encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77140736)))];
+            tensor<fp16, [1, 188, 1024]> linear_36_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16_palettized, x = input_227_cast_fp16)[name = string("linear_36_cast_fp16")];
+            fp16 var_1172_to_fp16 = const()[name = string("op_1172_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1173_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_1172_to_fp16)[name = string("op_1173_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_233_cast_fp16 = add(x = input_221_cast_fp16, y = var_1173_cast_fp16)[name = string("input_233_cast_fp16")];
+            tensor<int32, [1]> input_235_axes_0 = const()[name = string("input_235_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77142848)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77144960)))];
+            tensor<fp16, [1, 188, 1024]> input_235_cast_fp16 = layer_norm(axes = input_235_axes_0, beta = encoder_module_layers_3_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_out_weight_to_fp16, x = input_233_cast_fp16)[name = string("input_235_cast_fp16")];
+            tensor<int32, [1]> input_237_axes_0 = const()[name = string("input_237_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77147072)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77149184)))];
+            tensor<fp16, [1, 188, 1024]> input_237_cast_fp16 = layer_norm(axes = input_237_axes_0, beta = encoder_module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77151296))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80297088))))[name = string("encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80297280)))];
+            tensor<fp16, [1, 188, 4096]> linear_37_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = string("linear_37_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_241_cast_fp16 = silu(x = linear_37_cast_fp16)[name = string("input_241_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80305536))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83451328))))[name = string("encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83451520)))];
+            tensor<fp16, [1, 188, 1024]> linear_38_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16_palettized, x = input_241_cast_fp16)[name = string("linear_38_cast_fp16")];
+            fp16 var_1203_to_fp16 = const()[name = string("op_1203_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1204_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_1203_to_fp16)[name = string("op_1204_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_247_cast_fp16 = add(x = input_235_cast_fp16, y = var_1204_cast_fp16)[name = string("input_247_cast_fp16")];
+            tensor<int32, [1]> query_9_axes_0 = const()[name = string("query_9_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83453632)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83455744)))];
+            tensor<fp16, [1, 188, 1024]> query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = encoder_module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_self_att_weight_to_fp16, x = input_247_cast_fp16)[name = string("query_9_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83457856))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84244352))))[name = string("encoder_module_layers_4_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84244544)))];
+            tensor<fp16, [1, 188, 1024]> linear_39_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_q_weight_to_fp16_palettized, x = query_9_cast_fp16)[name = string("linear_39_cast_fp16")];
+            tensor<int32, [4]> var_1221 = const()[name = string("op_1221"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_25_cast_fp16 = reshape(shape = var_1221, x = linear_39_cast_fp16)[name = string("q_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84246656))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85033152))))[name = string("encoder_module_layers_4_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85033344)))];
+            tensor<fp16, [1, 188, 1024]> linear_40_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_k_weight_to_fp16_palettized, x = query_9_cast_fp16)[name = string("linear_40_cast_fp16")];
+            tensor<int32, [4]> var_1226 = const()[name = string("op_1226"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_17_cast_fp16 = reshape(shape = var_1226, x = linear_40_cast_fp16)[name = string("k_17_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85035456))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85821952))))[name = string("encoder_module_layers_4_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85822144)))];
+            tensor<fp16, [1, 188, 1024]> linear_41_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_v_weight_to_fp16_palettized, x = query_9_cast_fp16)[name = string("linear_41_cast_fp16")];
+            tensor<int32, [4]> var_1231 = const()[name = string("op_1231"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_9_cast_fp16 = reshape(shape = var_1231, x = linear_41_cast_fp16)[name = string("v_9_cast_fp16")];
+            tensor<int32, [4]> value_13_perm_0 = const()[name = string("value_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85824256)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1243_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_module_layers_4_self_attn_pos_bias_u_to_fp16)[name = string("op_1243_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85826368)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1245_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_module_layers_4_self_attn_pos_bias_v_to_fp16)[name = string("op_1245_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_9_perm_0 = const()[name = string("q_with_bias_v_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_115_transpose_x_0 = const()[name = string("x_115_transpose_x_0"), val = bool(false)];
+            bool x_115_transpose_y_0 = const()[name = string("x_115_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1247_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85828480))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86116544))))[name = string("op_1247_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_1245_cast_fp16)[name = string("transpose_284")];
+            tensor<fp16, [1, 8, 188, 375]> x_115_cast_fp16 = matmul(transpose_x = x_115_transpose_x_0, transpose_y = x_115_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = op_1247_to_fp16_palettized)[name = string("x_115_cast_fp16")];
+            tensor<int32, [8]> x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_117_mode_0 = const()[name = string("x_117_mode_0"), val = string("constant")];
+            fp16 const_128_to_fp16 = const()[name = string("const_128_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_117_cast_fp16 = pad(constant_val = const_128_to_fp16, mode = x_117_mode_0, pad = x_117_pad_0, x = x_115_cast_fp16)[name = string("x_117_cast_fp16")];
+            tensor<int32, [4]> var_1255 = const()[name = string("op_1255"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_119_cast_fp16 = reshape(shape = var_1255, x = x_117_cast_fp16)[name = string("x_119_cast_fp16")];
+            tensor<int32, [4]> var_1259_begin_0 = const()[name = string("op_1259_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1259_end_0 = const()[name = string("op_1259_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1259_end_mask_0 = const()[name = string("op_1259_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1259_cast_fp16 = slice_by_index(begin = var_1259_begin_0, end = var_1259_end_0, end_mask = var_1259_end_mask_0, x = x_119_cast_fp16)[name = string("op_1259_cast_fp16")];
+            tensor<int32, [4]> var_1260 = const()[name = string("op_1260"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_17_cast_fp16 = reshape(shape = var_1260, x = var_1259_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<int32, [4]> transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = string("transpose_282")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_104 = transpose(perm = transpose_104_perm_0, x = var_1243_cast_fp16)[name = string("transpose_283")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_19_end_0 = const()[name = string("matrix_bd_19_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1269_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = string("op_1269_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<fp16, [1, 8, 188, 188]> _inversed_scores_17_cast_fp16 = mul(x = var_1269_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = string("_inversed_scores_17_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_19_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_15)[name = string("scores_19_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1275_cast_fp16 = softmax(axis = var_152, x = scores_19_cast_fp16)[name = string("op_1275_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_249_cast_fp16 = select(a = var_164_to_fp16, b = var_1275_cast_fp16, cond = mask_15)[name = string("input_249_cast_fp16")];
+            bool x_121_transpose_x_0 = const()[name = string("x_121_transpose_x_0"), val = bool(false)];
+            bool x_121_transpose_y_0 = const()[name = string("x_121_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_9_cast_fp16)[name = string("transpose_281")];
+            tensor<fp16, [1, 8, 188, 128]> x_121_cast_fp16 = matmul(transpose_x = x_121_transpose_x_0, transpose_y = x_121_transpose_y_0, x = input_249_cast_fp16, y = value_13_cast_fp16)[name = string("x_121_cast_fp16")];
+            tensor<int32, [4]> var_1279_perm_0 = const()[name = string("op_1279_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1280 = const()[name = string("op_1280"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1279_cast_fp16 = transpose(perm = var_1279_perm_0, x = x_121_cast_fp16)[name = string("transpose_280")];
+            tensor<fp16, [1, 188, 1024]> input_251_cast_fp16 = reshape(shape = var_1280, x = var_1279_cast_fp16)[name = string("input_251_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86116736))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86903232))))[name = string("encoder_module_layers_4_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86903424)))];
+            tensor<fp16, [1, 188, 1024]> linear_43_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_out_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = string("linear_43_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_255_cast_fp16 = add(x = input_247_cast_fp16, y = linear_43_cast_fp16)[name = string("input_255_cast_fp16")];
+            tensor<int32, [1]> x_125_axes_0 = const()[name = string("x_125_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86905536)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86907648)))];
+            tensor<fp16, [1, 188, 1024]> x_125_cast_fp16 = layer_norm(axes = x_125_axes_0, beta = encoder_module_layers_4_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_conv_weight_to_fp16, x = input_255_cast_fp16)[name = string("x_125_cast_fp16")];
+            tensor<int32, [3]> input_257_perm_0 = const()[name = string("input_257_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_259_pad_type_0 = const()[name = string("input_259_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_259_strides_0 = const()[name = string("input_259_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_259_pad_0 = const()[name = string("input_259_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_259_dilations_0 = const()[name = string("input_259_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_259_groups_0 = const()[name = string("input_259_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_4_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86909760))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88482688))))[name = string("encoder_module_layers_4_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88482880)))];
+            tensor<fp16, [1, 1024, 188]> input_257_cast_fp16 = transpose(perm = input_257_perm_0, x = x_125_cast_fp16)[name = string("transpose_279")];
+            tensor<fp16, [1, 2048, 188]> input_259_cast_fp16 = conv(bias = encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_4_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_257_cast_fp16)[name = string("input_259_cast_fp16")];
+            int32 x_127_split_num_splits_0 = const()[name = string("x_127_split_num_splits_0"), val = int32(2)];
+            int32 x_127_split_axis_0 = const()[name = string("x_127_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_127_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_127_split_cast_fp16_1 = split(axis = x_127_split_axis_0, num_splits = x_127_split_num_splits_0, x = input_259_cast_fp16)[name = string("x_127_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_127_split_1_sigmoid_cast_fp16 = sigmoid(x = x_127_split_cast_fp16_1)[name = string("x_127_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_127_cast_fp16 = mul(x = x_127_split_cast_fp16_0, y = x_127_split_1_sigmoid_cast_fp16)[name = string("x_127_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_261_cast_fp16 = select(a = var_164_to_fp16, b = x_127_cast_fp16, cond = var_608)[name = string("input_261_cast_fp16")];
+            tensor<int32, [6]> input_263_pad_0 = const()[name = string("input_263_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_263_mode_0 = const()[name = string("input_263_mode_0"), val = string("constant")];
+            fp16 const_131_to_fp16 = const()[name = string("const_131_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_263_cast_fp16 = pad(constant_val = const_131_to_fp16, mode = input_263_mode_0, pad = input_263_pad_0, x = input_261_cast_fp16)[name = string("input_263_cast_fp16")];
+            string input_265_pad_type_0 = const()[name = string("input_265_pad_type_0"), val = string("valid")];
+            int32 input_265_groups_0 = const()[name = string("input_265_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_265_strides_0 = const()[name = string("input_265_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_265_pad_0 = const()[name = string("input_265_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_265_dilations_0 = const()[name = string("input_265_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_330_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88487040))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88494016))))[name = string("const_330_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_331_to_fp16 = const()[name = string("const_331_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88494208)))];
+            tensor<fp16, [1, 1024, 188]> input_267_cast_fp16 = conv(bias = const_331_to_fp16, dilations = input_265_dilations_0, groups = input_265_groups_0, pad = input_265_pad_0, pad_type = input_265_pad_type_0, strides = input_265_strides_0, weight = const_330_to_fp16_palettized, x = input_263_cast_fp16)[name = string("input_267_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_269_cast_fp16 = silu(x = input_267_cast_fp16)[name = string("input_269_cast_fp16")];
+            string x_129_pad_type_0 = const()[name = string("x_129_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_129_strides_0 = const()[name = string("x_129_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_129_pad_0 = const()[name = string("x_129_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_129_dilations_0 = const()[name = string("x_129_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_129_groups_0 = const()[name = string("x_129_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_4_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88496320))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89282816))))[name = string("encoder_module_layers_4_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89283008)))];
+            tensor<fp16, [1, 1024, 188]> x_129_cast_fp16 = conv(bias = encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_4_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_269_cast_fp16)[name = string("x_129_cast_fp16")];
+            tensor<int32, [3]> input_271_perm_0 = const()[name = string("input_271_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_271_cast_fp16 = transpose(perm = input_271_perm_0, x = x_129_cast_fp16)[name = string("transpose_278")];
+            tensor<fp16, [1, 188, 1024]> input_273_cast_fp16 = add(x = input_255_cast_fp16, y = input_271_cast_fp16)[name = string("input_273_cast_fp16")];
+            tensor<int32, [1]> input_275_axes_0 = const()[name = string("input_275_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89285120)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89287232)))];
+            tensor<fp16, [1, 188, 1024]> input_275_cast_fp16 = layer_norm(axes = input_275_axes_0, beta = encoder_module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89289344))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92435136))))[name = string("encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92435328)))];
+            tensor<fp16, [1, 188, 4096]> linear_44_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16_palettized, x = input_275_cast_fp16)[name = string("linear_44_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_279_cast_fp16 = silu(x = linear_44_cast_fp16)[name = string("input_279_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92443584))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95589376))))[name = string("encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95589568)))];
+            tensor<fp16, [1, 188, 1024]> linear_45_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16_palettized, x = input_279_cast_fp16)[name = string("linear_45_cast_fp16")];
+            fp16 var_1346_to_fp16 = const()[name = string("op_1346_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1347_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1346_to_fp16)[name = string("op_1347_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_285_cast_fp16 = add(x = input_273_cast_fp16, y = var_1347_cast_fp16)[name = string("input_285_cast_fp16")];
+            tensor<int32, [1]> input_287_axes_0 = const()[name = string("input_287_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95591680)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95593792)))];
+            tensor<fp16, [1, 188, 1024]> input_287_cast_fp16 = layer_norm(axes = input_287_axes_0, beta = encoder_module_layers_4_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_out_weight_to_fp16, x = input_285_cast_fp16)[name = string("input_287_cast_fp16")];
+            tensor<int32, [1]> input_289_axes_0 = const()[name = string("input_289_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95595904)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95598016)))];
+            tensor<fp16, [1, 188, 1024]> input_289_cast_fp16 = layer_norm(axes = input_289_axes_0, beta = encoder_module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_287_cast_fp16)[name = string("input_289_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95600128))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98745920))))[name = string("encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98746112)))];
+            tensor<fp16, [1, 188, 4096]> linear_46_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16_palettized, x = input_289_cast_fp16)[name = string("linear_46_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_293_cast_fp16 = silu(x = linear_46_cast_fp16)[name = string("input_293_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98754368))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101900160))))[name = string("encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101900352)))];
+            tensor<fp16, [1, 188, 1024]> linear_47_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16_palettized, x = input_293_cast_fp16)[name = string("linear_47_cast_fp16")];
+            fp16 var_1377_to_fp16 = const()[name = string("op_1377_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1378_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1377_to_fp16)[name = string("op_1378_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_299_cast_fp16 = add(x = input_287_cast_fp16, y = var_1378_cast_fp16)[name = string("input_299_cast_fp16")];
+            tensor<int32, [1]> query_11_axes_0 = const()[name = string("query_11_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101902464)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101904576)))];
+            tensor<fp16, [1, 188, 1024]> query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = encoder_module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_self_att_weight_to_fp16, x = input_299_cast_fp16)[name = string("query_11_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101906688))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102693184))))[name = string("encoder_module_layers_5_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102693376)))];
+            tensor<fp16, [1, 188, 1024]> linear_48_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_q_weight_to_fp16_palettized, x = query_11_cast_fp16)[name = string("linear_48_cast_fp16")];
+            tensor<int32, [4]> var_1395 = const()[name = string("op_1395"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_31_cast_fp16 = reshape(shape = var_1395, x = linear_48_cast_fp16)[name = string("q_31_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102695488))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103481984))))[name = string("encoder_module_layers_5_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103482176)))];
+            tensor<fp16, [1, 188, 1024]> linear_49_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_k_weight_to_fp16_palettized, x = query_11_cast_fp16)[name = string("linear_49_cast_fp16")];
+            tensor<int32, [4]> var_1400 = const()[name = string("op_1400"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_21_cast_fp16 = reshape(shape = var_1400, x = linear_49_cast_fp16)[name = string("k_21_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103484288))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104270784))))[name = string("encoder_module_layers_5_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104270976)))];
+            tensor<fp16, [1, 188, 1024]> linear_50_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_v_weight_to_fp16_palettized, x = query_11_cast_fp16)[name = string("linear_50_cast_fp16")];
+            tensor<int32, [4]> var_1405 = const()[name = string("op_1405"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_11_cast_fp16 = reshape(shape = var_1405, x = linear_50_cast_fp16)[name = string("v_11_cast_fp16")];
+            tensor<int32, [4]> value_15_perm_0 = const()[name = string("value_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104273088)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1417_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_module_layers_5_self_attn_pos_bias_u_to_fp16)[name = string("op_1417_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104275200)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1419_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_module_layers_5_self_attn_pos_bias_v_to_fp16)[name = string("op_1419_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_11_perm_0 = const()[name = string("q_with_bias_v_11_perm_0"), val = tensor<int32, [4]>([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<fp16, [1, 8, 128, 375]> op_1421_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104277312))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104565376))))[name = string("op_1421_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1419_cast_fp16)[name = string("transpose_277")];
+            tensor<fp16, [1, 8, 188, 375]> 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_1421_to_fp16_palettized)[name = string("x_137_cast_fp16")];
+            tensor<int32, [8]> x_139_pad_0 = const()[name = string("x_139_pad_0"), val = tensor<int32, [8]>([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_138_to_fp16 = const()[name = string("const_138_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_139_cast_fp16 = pad(constant_val = const_138_to_fp16, mode = x_139_mode_0, pad = x_139_pad_0, x = x_137_cast_fp16)[name = string("x_139_cast_fp16")];
+            tensor<int32, [4]> var_1429 = const()[name = string("op_1429"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_141_cast_fp16 = reshape(shape = var_1429, x = x_139_cast_fp16)[name = string("x_141_cast_fp16")];
+            tensor<int32, [4]> var_1433_begin_0 = const()[name = string("op_1433_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1433_end_0 = const()[name = string("op_1433_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1433_end_mask_0 = const()[name = string("op_1433_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1433_cast_fp16 = slice_by_index(begin = var_1433_begin_0, end = var_1433_end_0, end_mask = var_1433_end_mask_0, x = x_141_cast_fp16)[name = string("op_1433_cast_fp16")];
+            tensor<int32, [4]> var_1434 = const()[name = string("op_1434"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_21_cast_fp16 = reshape(shape = var_1434, x = var_1433_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<int32, [4]> transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = string("transpose_275")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1417_cast_fp16)[name = string("transpose_276")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_23_end_0 = const()[name = string("matrix_bd_23_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1443_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = string("op_1443_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<fp16, [1, 8, 188, 188]> _inversed_scores_21_cast_fp16 = mul(x = var_1443_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = string("_inversed_scores_21_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_23_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_15)[name = string("scores_23_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1449_cast_fp16 = softmax(axis = var_152, x = scores_23_cast_fp16)[name = string("op_1449_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_301_cast_fp16 = select(a = var_164_to_fp16, b = var_1449_cast_fp16, cond = mask_15)[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<fp16, [1, 8, 188, 128]> value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_11_cast_fp16)[name = string("transpose_274")];
+            tensor<fp16, [1, 8, 188, 128]> 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_15_cast_fp16)[name = string("x_143_cast_fp16")];
+            tensor<int32, [4]> var_1453_perm_0 = const()[name = string("op_1453_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1454 = const()[name = string("op_1454"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1453_cast_fp16 = transpose(perm = var_1453_perm_0, x = x_143_cast_fp16)[name = string("transpose_273")];
+            tensor<fp16, [1, 188, 1024]> input_303_cast_fp16 = reshape(shape = var_1454, x = var_1453_cast_fp16)[name = string("input_303_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104565568))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105352064))))[name = string("encoder_module_layers_5_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105352256)))];
+            tensor<fp16, [1, 188, 1024]> linear_52_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_out_weight_to_fp16_palettized, x = input_303_cast_fp16)[name = string("linear_52_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_307_cast_fp16 = add(x = input_299_cast_fp16, y = linear_52_cast_fp16)[name = string("input_307_cast_fp16")];
+            tensor<int32, [1]> x_147_axes_0 = const()[name = string("x_147_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105354368)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105356480)))];
+            tensor<fp16, [1, 188, 1024]> x_147_cast_fp16 = layer_norm(axes = x_147_axes_0, beta = encoder_module_layers_5_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_conv_weight_to_fp16, x = input_307_cast_fp16)[name = string("x_147_cast_fp16")];
+            tensor<int32, [3]> input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_311_pad_type_0 = const()[name = string("input_311_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_311_strides_0 = const()[name = string("input_311_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_311_pad_0 = const()[name = string("input_311_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_311_dilations_0 = const()[name = string("input_311_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_311_groups_0 = const()[name = string("input_311_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_5_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105358592))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106931520))))[name = string("encoder_module_layers_5_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106931712)))];
+            tensor<fp16, [1, 1024, 188]> input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_147_cast_fp16)[name = string("transpose_272")];
+            tensor<fp16, [1, 2048, 188]> input_311_cast_fp16 = conv(bias = encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_5_conv_pointwise_conv1_weight_to_fp16_palettized, 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<fp16, [1, 1024, 188]> x_149_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> input_313_cast_fp16 = select(a = var_164_to_fp16, b = x_149_cast_fp16, cond = var_608)[name = string("input_313_cast_fp16")];
+            tensor<int32, [6]> input_315_pad_0 = const()[name = string("input_315_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_315_mode_0 = const()[name = string("input_315_mode_0"), val = string("constant")];
+            fp16 const_141_to_fp16 = const()[name = string("const_141_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_315_cast_fp16 = pad(constant_val = const_141_to_fp16, mode = input_315_mode_0, pad = input_315_pad_0, x = input_313_cast_fp16)[name = string("input_315_cast_fp16")];
+            string input_317_pad_type_0 = const()[name = string("input_317_pad_type_0"), val = string("valid")];
+            int32 input_317_groups_0 = const()[name = string("input_317_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_317_strides_0 = const()[name = string("input_317_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_317_pad_0 = const()[name = string("input_317_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_317_dilations_0 = const()[name = string("input_317_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_332_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106935872))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106942848))))[name = string("const_332_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_333_to_fp16 = const()[name = string("const_333_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106943040)))];
+            tensor<fp16, [1, 1024, 188]> input_319_cast_fp16 = conv(bias = const_333_to_fp16, dilations = input_317_dilations_0, groups = input_317_groups_0, pad = input_317_pad_0, pad_type = input_317_pad_type_0, strides = input_317_strides_0, weight = const_332_to_fp16_palettized, x = input_315_cast_fp16)[name = string("input_319_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_321_cast_fp16 = silu(x = input_319_cast_fp16)[name = string("input_321_cast_fp16")];
+            string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_5_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106945152))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107731648))))[name = string("encoder_module_layers_5_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107731840)))];
+            tensor<fp16, [1, 1024, 188]> x_151_cast_fp16 = conv(bias = encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_5_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_321_cast_fp16)[name = string("x_151_cast_fp16")];
+            tensor<int32, [3]> input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = x_151_cast_fp16)[name = string("transpose_271")];
+            tensor<fp16, [1, 188, 1024]> input_325_cast_fp16 = add(x = input_307_cast_fp16, y = input_323_cast_fp16)[name = string("input_325_cast_fp16")];
+            tensor<int32, [1]> input_327_axes_0 = const()[name = string("input_327_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107733952)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107736064)))];
+            tensor<fp16, [1, 188, 1024]> input_327_cast_fp16 = layer_norm(axes = input_327_axes_0, beta = encoder_module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_325_cast_fp16)[name = string("input_327_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107738176))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110883968))))[name = string("encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110884160)))];
+            tensor<fp16, [1, 188, 4096]> linear_53_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16_palettized, x = input_327_cast_fp16)[name = string("linear_53_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_331_cast_fp16 = silu(x = linear_53_cast_fp16)[name = string("input_331_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110892416))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114038208))))[name = string("encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114038400)))];
+            tensor<fp16, [1, 188, 1024]> linear_54_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16_palettized, x = input_331_cast_fp16)[name = string("linear_54_cast_fp16")];
+            fp16 var_1520_to_fp16 = const()[name = string("op_1520_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1521_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1520_to_fp16)[name = string("op_1521_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_337_cast_fp16 = add(x = input_325_cast_fp16, y = var_1521_cast_fp16)[name = string("input_337_cast_fp16")];
+            tensor<int32, [1]> input_339_axes_0 = const()[name = string("input_339_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114040512)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114042624)))];
+            tensor<fp16, [1, 188, 1024]> input_339_cast_fp16 = layer_norm(axes = input_339_axes_0, beta = encoder_module_layers_5_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_out_weight_to_fp16, x = input_337_cast_fp16)[name = string("input_339_cast_fp16")];
+            tensor<int32, [1]> input_341_axes_0 = const()[name = string("input_341_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114044736)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114046848)))];
+            tensor<fp16, [1, 188, 1024]> input_341_cast_fp16 = layer_norm(axes = input_341_axes_0, beta = encoder_module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_339_cast_fp16)[name = string("input_341_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114048960))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117194752))))[name = string("encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117194944)))];
+            tensor<fp16, [1, 188, 4096]> linear_55_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16_palettized, x = input_341_cast_fp16)[name = string("linear_55_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_345_cast_fp16 = silu(x = linear_55_cast_fp16)[name = string("input_345_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117203200))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120348992))))[name = string("encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120349184)))];
+            tensor<fp16, [1, 188, 1024]> linear_56_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16_palettized, x = input_345_cast_fp16)[name = string("linear_56_cast_fp16")];
+            fp16 var_1551_to_fp16 = const()[name = string("op_1551_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1552_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1551_to_fp16)[name = string("op_1552_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_351_cast_fp16 = add(x = input_339_cast_fp16, y = var_1552_cast_fp16)[name = string("input_351_cast_fp16")];
+            tensor<int32, [1]> query_13_axes_0 = const()[name = string("query_13_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120351296)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120353408)))];
+            tensor<fp16, [1, 188, 1024]> query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = encoder_module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_self_att_weight_to_fp16, x = input_351_cast_fp16)[name = string("query_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120355520))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121142016))))[name = string("encoder_module_layers_6_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121142208)))];
+            tensor<fp16, [1, 188, 1024]> linear_57_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_q_weight_to_fp16_palettized, x = query_13_cast_fp16)[name = string("linear_57_cast_fp16")];
+            tensor<int32, [4]> var_1569 = const()[name = string("op_1569"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_37_cast_fp16 = reshape(shape = var_1569, x = linear_57_cast_fp16)[name = string("q_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121144320))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121930816))))[name = string("encoder_module_layers_6_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121931008)))];
+            tensor<fp16, [1, 188, 1024]> linear_58_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_k_weight_to_fp16_palettized, x = query_13_cast_fp16)[name = string("linear_58_cast_fp16")];
+            tensor<int32, [4]> var_1574 = const()[name = string("op_1574"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_25_cast_fp16 = reshape(shape = var_1574, x = linear_58_cast_fp16)[name = string("k_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121933120))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122719616))))[name = string("encoder_module_layers_6_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122719808)))];
+            tensor<fp16, [1, 188, 1024]> linear_59_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_v_weight_to_fp16_palettized, x = query_13_cast_fp16)[name = string("linear_59_cast_fp16")];
+            tensor<int32, [4]> var_1579 = const()[name = string("op_1579"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_13_cast_fp16 = reshape(shape = var_1579, x = linear_59_cast_fp16)[name = string("v_13_cast_fp16")];
+            tensor<int32, [4]> value_17_perm_0 = const()[name = string("value_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122721920)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1591_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_module_layers_6_self_attn_pos_bias_u_to_fp16)[name = string("op_1591_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122724032)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1593_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_module_layers_6_self_attn_pos_bias_v_to_fp16)[name = string("op_1593_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_13_perm_0 = const()[name = string("q_with_bias_v_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_159_transpose_x_0 = const()[name = string("x_159_transpose_x_0"), val = bool(false)];
+            bool x_159_transpose_y_0 = const()[name = string("x_159_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1595_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122726144))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123014208))))[name = string("op_1595_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1593_cast_fp16)[name = string("transpose_270")];
+            tensor<fp16, [1, 8, 188, 375]> x_159_cast_fp16 = matmul(transpose_x = x_159_transpose_x_0, transpose_y = x_159_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = op_1595_to_fp16_palettized)[name = string("x_159_cast_fp16")];
+            tensor<int32, [8]> x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_161_mode_0 = const()[name = string("x_161_mode_0"), val = string("constant")];
+            fp16 const_148_to_fp16 = const()[name = string("const_148_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_161_cast_fp16 = pad(constant_val = const_148_to_fp16, mode = x_161_mode_0, pad = x_161_pad_0, x = x_159_cast_fp16)[name = string("x_161_cast_fp16")];
+            tensor<int32, [4]> var_1603 = const()[name = string("op_1603"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_163_cast_fp16 = reshape(shape = var_1603, x = x_161_cast_fp16)[name = string("x_163_cast_fp16")];
+            tensor<int32, [4]> var_1607_begin_0 = const()[name = string("op_1607_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1607_end_0 = const()[name = string("op_1607_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1607_end_mask_0 = const()[name = string("op_1607_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1607_cast_fp16 = slice_by_index(begin = var_1607_begin_0, end = var_1607_end_0, end_mask = var_1607_end_mask_0, x = x_163_cast_fp16)[name = string("op_1607_cast_fp16")];
+            tensor<int32, [4]> var_1608 = const()[name = string("op_1608"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_25_cast_fp16 = reshape(shape = var_1608, x = var_1607_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<int32, [4]> transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = string("transpose_268")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1591_cast_fp16)[name = string("transpose_269")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_27_end_0 = const()[name = string("matrix_bd_27_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1617_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = string("op_1617_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<fp16, [1, 8, 188, 188]> _inversed_scores_25_cast_fp16 = mul(x = var_1617_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = string("_inversed_scores_25_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_27_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_15)[name = string("scores_27_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1623_cast_fp16 = softmax(axis = var_152, x = scores_27_cast_fp16)[name = string("op_1623_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_353_cast_fp16 = select(a = var_164_to_fp16, b = var_1623_cast_fp16, cond = mask_15)[name = string("input_353_cast_fp16")];
+            bool x_165_transpose_x_0 = const()[name = string("x_165_transpose_x_0"), val = bool(false)];
+            bool x_165_transpose_y_0 = const()[name = string("x_165_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_13_cast_fp16)[name = string("transpose_267")];
+            tensor<fp16, [1, 8, 188, 128]> x_165_cast_fp16 = matmul(transpose_x = x_165_transpose_x_0, transpose_y = x_165_transpose_y_0, x = input_353_cast_fp16, y = value_17_cast_fp16)[name = string("x_165_cast_fp16")];
+            tensor<int32, [4]> var_1627_perm_0 = const()[name = string("op_1627_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1628 = const()[name = string("op_1628"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1627_cast_fp16 = transpose(perm = var_1627_perm_0, x = x_165_cast_fp16)[name = string("transpose_266")];
+            tensor<fp16, [1, 188, 1024]> input_355_cast_fp16 = reshape(shape = var_1628, x = var_1627_cast_fp16)[name = string("input_355_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123014400))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123800896))))[name = string("encoder_module_layers_6_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123801088)))];
+            tensor<fp16, [1, 188, 1024]> linear_61_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_out_weight_to_fp16_palettized, x = input_355_cast_fp16)[name = string("linear_61_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_359_cast_fp16 = add(x = input_351_cast_fp16, y = linear_61_cast_fp16)[name = string("input_359_cast_fp16")];
+            tensor<int32, [1]> x_169_axes_0 = const()[name = string("x_169_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123803200)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123805312)))];
+            tensor<fp16, [1, 188, 1024]> x_169_cast_fp16 = layer_norm(axes = x_169_axes_0, beta = encoder_module_layers_6_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_conv_weight_to_fp16, x = input_359_cast_fp16)[name = string("x_169_cast_fp16")];
+            tensor<int32, [3]> input_361_perm_0 = const()[name = string("input_361_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_363_pad_type_0 = const()[name = string("input_363_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_363_strides_0 = const()[name = string("input_363_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_363_pad_0 = const()[name = string("input_363_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_363_dilations_0 = const()[name = string("input_363_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_363_groups_0 = const()[name = string("input_363_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_6_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123807424))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125380352))))[name = string("encoder_module_layers_6_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125380544)))];
+            tensor<fp16, [1, 1024, 188]> input_361_cast_fp16 = transpose(perm = input_361_perm_0, x = x_169_cast_fp16)[name = string("transpose_265")];
+            tensor<fp16, [1, 2048, 188]> input_363_cast_fp16 = conv(bias = encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_6_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_361_cast_fp16)[name = string("input_363_cast_fp16")];
+            int32 x_171_split_num_splits_0 = const()[name = string("x_171_split_num_splits_0"), val = int32(2)];
+            int32 x_171_split_axis_0 = const()[name = string("x_171_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_171_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_171_split_cast_fp16_1 = split(axis = x_171_split_axis_0, num_splits = x_171_split_num_splits_0, x = input_363_cast_fp16)[name = string("x_171_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_171_split_1_sigmoid_cast_fp16 = sigmoid(x = x_171_split_cast_fp16_1)[name = string("x_171_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_171_cast_fp16 = mul(x = x_171_split_cast_fp16_0, y = x_171_split_1_sigmoid_cast_fp16)[name = string("x_171_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_365_cast_fp16 = select(a = var_164_to_fp16, b = x_171_cast_fp16, cond = var_608)[name = string("input_365_cast_fp16")];
+            tensor<int32, [6]> input_367_pad_0 = const()[name = string("input_367_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_367_mode_0 = const()[name = string("input_367_mode_0"), val = string("constant")];
+            fp16 const_151_to_fp16 = const()[name = string("const_151_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_367_cast_fp16 = pad(constant_val = const_151_to_fp16, mode = input_367_mode_0, pad = input_367_pad_0, x = input_365_cast_fp16)[name = string("input_367_cast_fp16")];
+            string input_369_pad_type_0 = const()[name = string("input_369_pad_type_0"), val = string("valid")];
+            int32 input_369_groups_0 = const()[name = string("input_369_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_369_strides_0 = const()[name = string("input_369_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_369_dilations_0 = const()[name = string("input_369_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_334_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125384704))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125391680))))[name = string("const_334_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_335_to_fp16 = const()[name = string("const_335_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125391872)))];
+            tensor<fp16, [1, 1024, 188]> input_371_cast_fp16 = conv(bias = const_335_to_fp16, dilations = input_369_dilations_0, groups = input_369_groups_0, pad = input_369_pad_0, pad_type = input_369_pad_type_0, strides = input_369_strides_0, weight = const_334_to_fp16_palettized, x = input_367_cast_fp16)[name = string("input_371_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_373_cast_fp16 = silu(x = input_371_cast_fp16)[name = string("input_373_cast_fp16")];
+            string x_173_pad_type_0 = const()[name = string("x_173_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_173_strides_0 = const()[name = string("x_173_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_173_pad_0 = const()[name = string("x_173_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_173_dilations_0 = const()[name = string("x_173_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_173_groups_0 = const()[name = string("x_173_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125393984))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126180480))))[name = string("encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126180672)))];
+            tensor<fp16, [1, 1024, 188]> x_173_cast_fp16 = conv(bias = encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16, dilations = x_173_dilations_0, groups = x_173_groups_0, pad = x_173_pad_0, pad_type = x_173_pad_type_0, strides = x_173_strides_0, weight = encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_373_cast_fp16)[name = string("x_173_cast_fp16")];
+            tensor<int32, [3]> input_375_perm_0 = const()[name = string("input_375_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_375_cast_fp16 = transpose(perm = input_375_perm_0, x = x_173_cast_fp16)[name = string("transpose_264")];
+            tensor<fp16, [1, 188, 1024]> input_377_cast_fp16 = add(x = input_359_cast_fp16, y = input_375_cast_fp16)[name = string("input_377_cast_fp16")];
+            tensor<int32, [1]> input_379_axes_0 = const()[name = string("input_379_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126182784)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126184896)))];
+            tensor<fp16, [1, 188, 1024]> input_379_cast_fp16 = layer_norm(axes = input_379_axes_0, beta = encoder_module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_377_cast_fp16)[name = string("input_379_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126187008))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129332800))))[name = string("encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129332992)))];
+            tensor<fp16, [1, 188, 4096]> linear_62_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16_palettized, x = input_379_cast_fp16)[name = string("linear_62_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_383_cast_fp16 = silu(x = linear_62_cast_fp16)[name = string("input_383_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129341248))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132487040))))[name = string("encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132487232)))];
+            tensor<fp16, [1, 188, 1024]> linear_63_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16_palettized, x = input_383_cast_fp16)[name = string("linear_63_cast_fp16")];
+            fp16 var_1694_to_fp16 = const()[name = string("op_1694_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1695_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1694_to_fp16)[name = string("op_1695_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_389_cast_fp16 = add(x = input_377_cast_fp16, y = var_1695_cast_fp16)[name = string("input_389_cast_fp16")];
+            tensor<int32, [1]> input_391_axes_0 = const()[name = string("input_391_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132489344)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132491456)))];
+            tensor<fp16, [1, 188, 1024]> input_391_cast_fp16 = layer_norm(axes = input_391_axes_0, beta = encoder_module_layers_6_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_out_weight_to_fp16, x = input_389_cast_fp16)[name = string("input_391_cast_fp16")];
+            tensor<int32, [1]> input_393_axes_0 = const()[name = string("input_393_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132493568)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132495680)))];
+            tensor<fp16, [1, 188, 1024]> input_393_cast_fp16 = layer_norm(axes = input_393_axes_0, beta = encoder_module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_391_cast_fp16)[name = string("input_393_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132497792))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135643584))))[name = string("encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135643776)))];
+            tensor<fp16, [1, 188, 4096]> linear_64_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16_palettized, x = input_393_cast_fp16)[name = string("linear_64_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_397_cast_fp16 = silu(x = linear_64_cast_fp16)[name = string("input_397_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135652032))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138797824))))[name = string("encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138798016)))];
+            tensor<fp16, [1, 188, 1024]> linear_65_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16_palettized, x = input_397_cast_fp16)[name = string("linear_65_cast_fp16")];
+            fp16 var_1725_to_fp16 = const()[name = string("op_1725_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1726_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1725_to_fp16)[name = string("op_1726_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_403_cast_fp16 = add(x = input_391_cast_fp16, y = var_1726_cast_fp16)[name = string("input_403_cast_fp16")];
+            tensor<int32, [1]> query_15_axes_0 = const()[name = string("query_15_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138800128)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138802240)))];
+            tensor<fp16, [1, 188, 1024]> query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = encoder_module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_self_att_weight_to_fp16, x = input_403_cast_fp16)[name = string("query_15_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138804352))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139590848))))[name = string("encoder_module_layers_7_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139591040)))];
+            tensor<fp16, [1, 188, 1024]> linear_66_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_q_weight_to_fp16_palettized, x = query_15_cast_fp16)[name = string("linear_66_cast_fp16")];
+            tensor<int32, [4]> var_1743 = const()[name = string("op_1743"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_43_cast_fp16 = reshape(shape = var_1743, x = linear_66_cast_fp16)[name = string("q_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139593152))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140379648))))[name = string("encoder_module_layers_7_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140379840)))];
+            tensor<fp16, [1, 188, 1024]> linear_67_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_k_weight_to_fp16_palettized, x = query_15_cast_fp16)[name = string("linear_67_cast_fp16")];
+            tensor<int32, [4]> var_1748 = const()[name = string("op_1748"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_29_cast_fp16 = reshape(shape = var_1748, x = linear_67_cast_fp16)[name = string("k_29_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140381952))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141168448))))[name = string("encoder_module_layers_7_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141168640)))];
+            tensor<fp16, [1, 188, 1024]> linear_68_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_v_weight_to_fp16_palettized, x = query_15_cast_fp16)[name = string("linear_68_cast_fp16")];
+            tensor<int32, [4]> var_1753 = const()[name = string("op_1753"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_15_cast_fp16 = reshape(shape = var_1753, x = linear_68_cast_fp16)[name = string("v_15_cast_fp16")];
+            tensor<int32, [4]> value_19_perm_0 = const()[name = string("value_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141170752)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1765_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_module_layers_7_self_attn_pos_bias_u_to_fp16)[name = string("op_1765_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141172864)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1767_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_module_layers_7_self_attn_pos_bias_v_to_fp16)[name = string("op_1767_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_15_perm_0 = const()[name = string("q_with_bias_v_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_181_transpose_x_0 = const()[name = string("x_181_transpose_x_0"), val = bool(false)];
+            bool x_181_transpose_y_0 = const()[name = string("x_181_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1769_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141174976))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141463040))))[name = string("op_1769_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1767_cast_fp16)[name = string("transpose_263")];
+            tensor<fp16, [1, 8, 188, 375]> x_181_cast_fp16 = matmul(transpose_x = x_181_transpose_x_0, transpose_y = x_181_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = op_1769_to_fp16_palettized)[name = string("x_181_cast_fp16")];
+            tensor<int32, [8]> x_183_pad_0 = const()[name = string("x_183_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_183_mode_0 = const()[name = string("x_183_mode_0"), val = string("constant")];
+            fp16 const_158_to_fp16 = const()[name = string("const_158_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_183_cast_fp16 = pad(constant_val = const_158_to_fp16, mode = x_183_mode_0, pad = x_183_pad_0, x = x_181_cast_fp16)[name = string("x_183_cast_fp16")];
+            tensor<int32, [4]> var_1777 = const()[name = string("op_1777"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_185_cast_fp16 = reshape(shape = var_1777, x = x_183_cast_fp16)[name = string("x_185_cast_fp16")];
+            tensor<int32, [4]> var_1781_begin_0 = const()[name = string("op_1781_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1781_end_0 = const()[name = string("op_1781_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1781_end_mask_0 = const()[name = string("op_1781_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1781_cast_fp16 = slice_by_index(begin = var_1781_begin_0, end = var_1781_end_0, end_mask = var_1781_end_mask_0, x = x_185_cast_fp16)[name = string("op_1781_cast_fp16")];
+            tensor<int32, [4]> var_1782 = const()[name = string("op_1782"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_29_cast_fp16 = reshape(shape = var_1782, x = var_1781_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<int32, [4]> transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = string("transpose_261")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_110 = transpose(perm = transpose_110_perm_0, x = var_1765_cast_fp16)[name = string("transpose_262")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_31_end_0 = const()[name = string("matrix_bd_31_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1791_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = string("op_1791_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<fp16, [1, 8, 188, 188]> _inversed_scores_29_cast_fp16 = mul(x = var_1791_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = string("_inversed_scores_29_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_31_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_15)[name = string("scores_31_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1797_cast_fp16 = softmax(axis = var_152, x = scores_31_cast_fp16)[name = string("op_1797_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_405_cast_fp16 = select(a = var_164_to_fp16, b = var_1797_cast_fp16, cond = mask_15)[name = string("input_405_cast_fp16")];
+            bool x_187_transpose_x_0 = const()[name = string("x_187_transpose_x_0"), val = bool(false)];
+            bool x_187_transpose_y_0 = const()[name = string("x_187_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_15_cast_fp16)[name = string("transpose_260")];
+            tensor<fp16, [1, 8, 188, 128]> x_187_cast_fp16 = matmul(transpose_x = x_187_transpose_x_0, transpose_y = x_187_transpose_y_0, x = input_405_cast_fp16, y = value_19_cast_fp16)[name = string("x_187_cast_fp16")];
+            tensor<int32, [4]> var_1801_perm_0 = const()[name = string("op_1801_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1802 = const()[name = string("op_1802"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1801_cast_fp16 = transpose(perm = var_1801_perm_0, x = x_187_cast_fp16)[name = string("transpose_259")];
+            tensor<fp16, [1, 188, 1024]> input_407_cast_fp16 = reshape(shape = var_1802, x = var_1801_cast_fp16)[name = string("input_407_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141463232))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142249728))))[name = string("encoder_module_layers_7_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142249920)))];
+            tensor<fp16, [1, 188, 1024]> linear_70_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_out_weight_to_fp16_palettized, x = input_407_cast_fp16)[name = string("linear_70_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_411_cast_fp16 = add(x = input_403_cast_fp16, y = linear_70_cast_fp16)[name = string("input_411_cast_fp16")];
+            tensor<int32, [1]> x_191_axes_0 = const()[name = string("x_191_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142252032)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142254144)))];
+            tensor<fp16, [1, 188, 1024]> x_191_cast_fp16 = layer_norm(axes = x_191_axes_0, beta = encoder_module_layers_7_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_conv_weight_to_fp16, x = input_411_cast_fp16)[name = string("x_191_cast_fp16")];
+            tensor<int32, [3]> input_413_perm_0 = const()[name = string("input_413_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_415_pad_type_0 = const()[name = string("input_415_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_415_strides_0 = const()[name = string("input_415_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_415_pad_0 = const()[name = string("input_415_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_415_dilations_0 = const()[name = string("input_415_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_415_groups_0 = const()[name = string("input_415_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_7_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142256256))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143829184))))[name = string("encoder_module_layers_7_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143829376)))];
+            tensor<fp16, [1, 1024, 188]> input_413_cast_fp16 = transpose(perm = input_413_perm_0, x = x_191_cast_fp16)[name = string("transpose_258")];
+            tensor<fp16, [1, 2048, 188]> input_415_cast_fp16 = conv(bias = encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_7_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_413_cast_fp16)[name = string("input_415_cast_fp16")];
+            int32 x_193_split_num_splits_0 = const()[name = string("x_193_split_num_splits_0"), val = int32(2)];
+            int32 x_193_split_axis_0 = const()[name = string("x_193_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_193_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_193_split_cast_fp16_1 = split(axis = x_193_split_axis_0, num_splits = x_193_split_num_splits_0, x = input_415_cast_fp16)[name = string("x_193_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_193_split_1_sigmoid_cast_fp16 = sigmoid(x = x_193_split_cast_fp16_1)[name = string("x_193_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_193_cast_fp16 = mul(x = x_193_split_cast_fp16_0, y = x_193_split_1_sigmoid_cast_fp16)[name = string("x_193_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_417_cast_fp16 = select(a = var_164_to_fp16, b = x_193_cast_fp16, cond = var_608)[name = string("input_417_cast_fp16")];
+            tensor<int32, [6]> input_419_pad_0 = const()[name = string("input_419_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("constant")];
+            fp16 const_161_to_fp16 = const()[name = string("const_161_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_419_cast_fp16 = pad(constant_val = const_161_to_fp16, mode = input_419_mode_0, pad = input_419_pad_0, x = input_417_cast_fp16)[name = string("input_419_cast_fp16")];
+            string input_421_pad_type_0 = const()[name = string("input_421_pad_type_0"), val = string("valid")];
+            int32 input_421_groups_0 = const()[name = string("input_421_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_421_strides_0 = const()[name = string("input_421_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_421_pad_0 = const()[name = string("input_421_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_421_dilations_0 = const()[name = string("input_421_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_336_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143833536))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143840512))))[name = string("const_336_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_337_to_fp16 = const()[name = string("const_337_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143840704)))];
+            tensor<fp16, [1, 1024, 188]> input_423_cast_fp16 = conv(bias = const_337_to_fp16, dilations = input_421_dilations_0, groups = input_421_groups_0, pad = input_421_pad_0, pad_type = input_421_pad_type_0, strides = input_421_strides_0, weight = const_336_to_fp16_palettized, x = input_419_cast_fp16)[name = string("input_423_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_425_cast_fp16 = silu(x = input_423_cast_fp16)[name = string("input_425_cast_fp16")];
+            string x_195_pad_type_0 = const()[name = string("x_195_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_195_strides_0 = const()[name = string("x_195_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_195_pad_0 = const()[name = string("x_195_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_195_dilations_0 = const()[name = string("x_195_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_195_groups_0 = const()[name = string("x_195_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143842816))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144629312))))[name = string("encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144629504)))];
+            tensor<fp16, [1, 1024, 188]> x_195_cast_fp16 = conv(bias = encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16, dilations = x_195_dilations_0, groups = x_195_groups_0, pad = x_195_pad_0, pad_type = x_195_pad_type_0, strides = x_195_strides_0, weight = encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_425_cast_fp16)[name = string("x_195_cast_fp16")];
+            tensor<int32, [3]> input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_195_cast_fp16)[name = string("transpose_257")];
+            tensor<fp16, [1, 188, 1024]> input_429_cast_fp16 = add(x = input_411_cast_fp16, y = input_427_cast_fp16)[name = string("input_429_cast_fp16")];
+            tensor<int32, [1]> input_431_axes_0 = const()[name = string("input_431_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144631616)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144633728)))];
+            tensor<fp16, [1, 188, 1024]> input_431_cast_fp16 = layer_norm(axes = input_431_axes_0, beta = encoder_module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_429_cast_fp16)[name = string("input_431_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144635840))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147781632))))[name = string("encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147781824)))];
+            tensor<fp16, [1, 188, 4096]> linear_71_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16_palettized, x = input_431_cast_fp16)[name = string("linear_71_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_435_cast_fp16 = silu(x = linear_71_cast_fp16)[name = string("input_435_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147790080))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150935872))))[name = string("encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150936064)))];
+            tensor<fp16, [1, 188, 1024]> linear_72_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16_palettized, x = input_435_cast_fp16)[name = string("linear_72_cast_fp16")];
+            fp16 var_1868_to_fp16 = const()[name = string("op_1868_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1869_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1868_to_fp16)[name = string("op_1869_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_441_cast_fp16 = add(x = input_429_cast_fp16, y = var_1869_cast_fp16)[name = string("input_441_cast_fp16")];
+            tensor<int32, [1]> input_443_axes_0 = const()[name = string("input_443_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150938176)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150940288)))];
+            tensor<fp16, [1, 188, 1024]> input_443_cast_fp16 = layer_norm(axes = input_443_axes_0, beta = encoder_module_layers_7_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_out_weight_to_fp16, x = input_441_cast_fp16)[name = string("input_443_cast_fp16")];
+            tensor<int32, [1]> input_445_axes_0 = const()[name = string("input_445_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150942400)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150944512)))];
+            tensor<fp16, [1, 188, 1024]> input_445_cast_fp16 = layer_norm(axes = input_445_axes_0, beta = encoder_module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_443_cast_fp16)[name = string("input_445_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150946624))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154092416))))[name = string("encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154092608)))];
+            tensor<fp16, [1, 188, 4096]> linear_73_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16_palettized, x = input_445_cast_fp16)[name = string("linear_73_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_449_cast_fp16 = silu(x = linear_73_cast_fp16)[name = string("input_449_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154100864))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157246656))))[name = string("encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157246848)))];
+            tensor<fp16, [1, 188, 1024]> linear_74_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16_palettized, x = input_449_cast_fp16)[name = string("linear_74_cast_fp16")];
+            fp16 var_1899_to_fp16 = const()[name = string("op_1899_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1900_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1899_to_fp16)[name = string("op_1900_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_455_cast_fp16 = add(x = input_443_cast_fp16, y = var_1900_cast_fp16)[name = string("input_455_cast_fp16")];
+            tensor<int32, [1]> query_17_axes_0 = const()[name = string("query_17_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157248960)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157251072)))];
+            tensor<fp16, [1, 188, 1024]> query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = encoder_module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_self_att_weight_to_fp16, x = input_455_cast_fp16)[name = string("query_17_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157253184))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158039680))))[name = string("encoder_module_layers_8_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158039872)))];
+            tensor<fp16, [1, 188, 1024]> linear_75_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_q_weight_to_fp16_palettized, x = query_17_cast_fp16)[name = string("linear_75_cast_fp16")];
+            tensor<int32, [4]> var_1917 = const()[name = string("op_1917"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_49_cast_fp16 = reshape(shape = var_1917, x = linear_75_cast_fp16)[name = string("q_49_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158041984))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158828480))))[name = string("encoder_module_layers_8_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158828672)))];
+            tensor<fp16, [1, 188, 1024]> linear_76_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_k_weight_to_fp16_palettized, x = query_17_cast_fp16)[name = string("linear_76_cast_fp16")];
+            tensor<int32, [4]> var_1922 = const()[name = string("op_1922"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_33_cast_fp16 = reshape(shape = var_1922, x = linear_76_cast_fp16)[name = string("k_33_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158830784))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159617280))))[name = string("encoder_module_layers_8_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159617472)))];
+            tensor<fp16, [1, 188, 1024]> linear_77_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_v_weight_to_fp16_palettized, x = query_17_cast_fp16)[name = string("linear_77_cast_fp16")];
+            tensor<int32, [4]> var_1927 = const()[name = string("op_1927"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_17_cast_fp16 = reshape(shape = var_1927, x = linear_77_cast_fp16)[name = string("v_17_cast_fp16")];
+            tensor<int32, [4]> value_21_perm_0 = const()[name = string("value_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159619584)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1939_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_module_layers_8_self_attn_pos_bias_u_to_fp16)[name = string("op_1939_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159621696)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1941_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_module_layers_8_self_attn_pos_bias_v_to_fp16)[name = string("op_1941_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_17_perm_0 = const()[name = string("q_with_bias_v_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_203_transpose_x_0 = const()[name = string("x_203_transpose_x_0"), val = bool(false)];
+            bool x_203_transpose_y_0 = const()[name = string("x_203_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1943_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159623808))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159911872))))[name = string("op_1943_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1941_cast_fp16)[name = string("transpose_256")];
+            tensor<fp16, [1, 8, 188, 375]> x_203_cast_fp16 = matmul(transpose_x = x_203_transpose_x_0, transpose_y = x_203_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = op_1943_to_fp16_palettized)[name = string("x_203_cast_fp16")];
+            tensor<int32, [8]> x_205_pad_0 = const()[name = string("x_205_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_205_mode_0 = const()[name = string("x_205_mode_0"), val = string("constant")];
+            fp16 const_168_to_fp16 = const()[name = string("const_168_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_205_cast_fp16 = pad(constant_val = const_168_to_fp16, mode = x_205_mode_0, pad = x_205_pad_0, x = x_203_cast_fp16)[name = string("x_205_cast_fp16")];
+            tensor<int32, [4]> var_1951 = const()[name = string("op_1951"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_207_cast_fp16 = reshape(shape = var_1951, x = x_205_cast_fp16)[name = string("x_207_cast_fp16")];
+            tensor<int32, [4]> var_1955_begin_0 = const()[name = string("op_1955_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1955_end_0 = const()[name = string("op_1955_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1955_end_mask_0 = const()[name = string("op_1955_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1955_cast_fp16 = slice_by_index(begin = var_1955_begin_0, end = var_1955_end_0, end_mask = var_1955_end_mask_0, x = x_207_cast_fp16)[name = string("op_1955_cast_fp16")];
+            tensor<int32, [4]> var_1956 = const()[name = string("op_1956"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_33_cast_fp16 = reshape(shape = var_1956, x = var_1955_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<int32, [4]> transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = string("transpose_254")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_112 = transpose(perm = transpose_112_perm_0, x = var_1939_cast_fp16)[name = string("transpose_255")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_35_end_0 = const()[name = string("matrix_bd_35_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1965_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = string("op_1965_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<fp16, [1, 8, 188, 188]> _inversed_scores_33_cast_fp16 = mul(x = var_1965_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = string("_inversed_scores_33_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_35_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_15)[name = string("scores_35_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1971_cast_fp16 = softmax(axis = var_152, x = scores_35_cast_fp16)[name = string("op_1971_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_457_cast_fp16 = select(a = var_164_to_fp16, b = var_1971_cast_fp16, cond = mask_15)[name = string("input_457_cast_fp16")];
+            bool x_209_transpose_x_0 = const()[name = string("x_209_transpose_x_0"), val = bool(false)];
+            bool x_209_transpose_y_0 = const()[name = string("x_209_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_17_cast_fp16)[name = string("transpose_253")];
+            tensor<fp16, [1, 8, 188, 128]> x_209_cast_fp16 = matmul(transpose_x = x_209_transpose_x_0, transpose_y = x_209_transpose_y_0, x = input_457_cast_fp16, y = value_21_cast_fp16)[name = string("x_209_cast_fp16")];
+            tensor<int32, [4]> var_1975_perm_0 = const()[name = string("op_1975_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1976 = const()[name = string("op_1976"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1975_cast_fp16 = transpose(perm = var_1975_perm_0, x = x_209_cast_fp16)[name = string("transpose_252")];
+            tensor<fp16, [1, 188, 1024]> input_459_cast_fp16 = reshape(shape = var_1976, x = var_1975_cast_fp16)[name = string("input_459_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159912064))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160698560))))[name = string("encoder_module_layers_8_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160698752)))];
+            tensor<fp16, [1, 188, 1024]> linear_79_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_out_weight_to_fp16_palettized, x = input_459_cast_fp16)[name = string("linear_79_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_463_cast_fp16 = add(x = input_455_cast_fp16, y = linear_79_cast_fp16)[name = string("input_463_cast_fp16")];
+            tensor<int32, [1]> x_213_axes_0 = const()[name = string("x_213_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160700864)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160702976)))];
+            tensor<fp16, [1, 188, 1024]> x_213_cast_fp16 = layer_norm(axes = x_213_axes_0, beta = encoder_module_layers_8_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_conv_weight_to_fp16, x = input_463_cast_fp16)[name = string("x_213_cast_fp16")];
+            tensor<int32, [3]> input_465_perm_0 = const()[name = string("input_465_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_467_pad_type_0 = const()[name = string("input_467_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_467_strides_0 = const()[name = string("input_467_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_467_pad_0 = const()[name = string("input_467_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_467_dilations_0 = const()[name = string("input_467_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_467_groups_0 = const()[name = string("input_467_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_8_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160705088))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162278016))))[name = string("encoder_module_layers_8_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162278208)))];
+            tensor<fp16, [1, 1024, 188]> input_465_cast_fp16 = transpose(perm = input_465_perm_0, x = x_213_cast_fp16)[name = string("transpose_251")];
+            tensor<fp16, [1, 2048, 188]> input_467_cast_fp16 = conv(bias = encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_8_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_465_cast_fp16)[name = string("input_467_cast_fp16")];
+            int32 x_215_split_num_splits_0 = const()[name = string("x_215_split_num_splits_0"), val = int32(2)];
+            int32 x_215_split_axis_0 = const()[name = string("x_215_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_215_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_215_split_cast_fp16_1 = split(axis = x_215_split_axis_0, num_splits = x_215_split_num_splits_0, x = input_467_cast_fp16)[name = string("x_215_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_215_split_1_sigmoid_cast_fp16 = sigmoid(x = x_215_split_cast_fp16_1)[name = string("x_215_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_215_cast_fp16 = mul(x = x_215_split_cast_fp16_0, y = x_215_split_1_sigmoid_cast_fp16)[name = string("x_215_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_469_cast_fp16 = select(a = var_164_to_fp16, b = x_215_cast_fp16, cond = var_608)[name = string("input_469_cast_fp16")];
+            tensor<int32, [6]> input_471_pad_0 = const()[name = string("input_471_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_471_mode_0 = const()[name = string("input_471_mode_0"), val = string("constant")];
+            fp16 const_171_to_fp16 = const()[name = string("const_171_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_471_cast_fp16 = pad(constant_val = const_171_to_fp16, mode = input_471_mode_0, pad = input_471_pad_0, x = input_469_cast_fp16)[name = string("input_471_cast_fp16")];
+            string input_473_pad_type_0 = const()[name = string("input_473_pad_type_0"), val = string("valid")];
+            int32 input_473_groups_0 = const()[name = string("input_473_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_473_strides_0 = const()[name = string("input_473_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_473_pad_0 = const()[name = string("input_473_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_473_dilations_0 = const()[name = string("input_473_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_338_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162282368))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162289344))))[name = string("const_338_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_339_to_fp16 = const()[name = string("const_339_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162289536)))];
+            tensor<fp16, [1, 1024, 188]> input_475_cast_fp16 = conv(bias = const_339_to_fp16, dilations = input_473_dilations_0, groups = input_473_groups_0, pad = input_473_pad_0, pad_type = input_473_pad_type_0, strides = input_473_strides_0, weight = const_338_to_fp16_palettized, x = input_471_cast_fp16)[name = string("input_475_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_477_cast_fp16 = silu(x = input_475_cast_fp16)[name = string("input_477_cast_fp16")];
+            string x_217_pad_type_0 = const()[name = string("x_217_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_217_strides_0 = const()[name = string("x_217_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_217_pad_0 = const()[name = string("x_217_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_217_dilations_0 = const()[name = string("x_217_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_217_groups_0 = const()[name = string("x_217_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162291648))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163078144))))[name = string("encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163078336)))];
+            tensor<fp16, [1, 1024, 188]> x_217_cast_fp16 = conv(bias = encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16, dilations = x_217_dilations_0, groups = x_217_groups_0, pad = x_217_pad_0, pad_type = x_217_pad_type_0, strides = x_217_strides_0, weight = encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_477_cast_fp16)[name = string("x_217_cast_fp16")];
+            tensor<int32, [3]> input_479_perm_0 = const()[name = string("input_479_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_479_cast_fp16 = transpose(perm = input_479_perm_0, x = x_217_cast_fp16)[name = string("transpose_250")];
+            tensor<fp16, [1, 188, 1024]> input_481_cast_fp16 = add(x = input_463_cast_fp16, y = input_479_cast_fp16)[name = string("input_481_cast_fp16")];
+            tensor<int32, [1]> input_483_axes_0 = const()[name = string("input_483_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163080448)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163082560)))];
+            tensor<fp16, [1, 188, 1024]> input_483_cast_fp16 = layer_norm(axes = input_483_axes_0, beta = encoder_module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_481_cast_fp16)[name = string("input_483_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163084672))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166230464))))[name = string("encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166230656)))];
+            tensor<fp16, [1, 188, 4096]> linear_80_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16_palettized, x = input_483_cast_fp16)[name = string("linear_80_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_487_cast_fp16 = silu(x = linear_80_cast_fp16)[name = string("input_487_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166238912))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169384704))))[name = string("encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169384896)))];
+            tensor<fp16, [1, 188, 1024]> linear_81_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16_palettized, x = input_487_cast_fp16)[name = string("linear_81_cast_fp16")];
+            fp16 var_2042_to_fp16 = const()[name = string("op_2042_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2043_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_2042_to_fp16)[name = string("op_2043_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_493_cast_fp16 = add(x = input_481_cast_fp16, y = var_2043_cast_fp16)[name = string("input_493_cast_fp16")];
+            tensor<int32, [1]> input_495_axes_0 = const()[name = string("input_495_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169387008)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169389120)))];
+            tensor<fp16, [1, 188, 1024]> input_495_cast_fp16 = layer_norm(axes = input_495_axes_0, beta = encoder_module_layers_8_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_out_weight_to_fp16, x = input_493_cast_fp16)[name = string("input_495_cast_fp16")];
+            tensor<int32, [1]> input_497_axes_0 = const()[name = string("input_497_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169391232)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169393344)))];
+            tensor<fp16, [1, 188, 1024]> input_497_cast_fp16 = layer_norm(axes = input_497_axes_0, beta = encoder_module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_495_cast_fp16)[name = string("input_497_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169395456))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172541248))))[name = string("encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172541440)))];
+            tensor<fp16, [1, 188, 4096]> linear_82_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16_palettized, x = input_497_cast_fp16)[name = string("linear_82_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_501_cast_fp16 = silu(x = linear_82_cast_fp16)[name = string("input_501_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172549696))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175695488))))[name = string("encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175695680)))];
+            tensor<fp16, [1, 188, 1024]> linear_83_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16_palettized, x = input_501_cast_fp16)[name = string("linear_83_cast_fp16")];
+            fp16 var_2073_to_fp16 = const()[name = string("op_2073_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2074_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_2073_to_fp16)[name = string("op_2074_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_507_cast_fp16 = add(x = input_495_cast_fp16, y = var_2074_cast_fp16)[name = string("input_507_cast_fp16")];
+            tensor<int32, [1]> query_19_axes_0 = const()[name = string("query_19_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175697792)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175699904)))];
+            tensor<fp16, [1, 188, 1024]> query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = encoder_module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_self_att_weight_to_fp16, x = input_507_cast_fp16)[name = string("query_19_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175702016))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176488512))))[name = string("encoder_module_layers_9_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176488704)))];
+            tensor<fp16, [1, 188, 1024]> linear_84_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_q_weight_to_fp16_palettized, x = query_19_cast_fp16)[name = string("linear_84_cast_fp16")];
+            tensor<int32, [4]> var_2091 = const()[name = string("op_2091"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_55_cast_fp16 = reshape(shape = var_2091, x = linear_84_cast_fp16)[name = string("q_55_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176490816))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177277312))))[name = string("encoder_module_layers_9_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177277504)))];
+            tensor<fp16, [1, 188, 1024]> linear_85_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_k_weight_to_fp16_palettized, x = query_19_cast_fp16)[name = string("linear_85_cast_fp16")];
+            tensor<int32, [4]> var_2096 = const()[name = string("op_2096"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_37_cast_fp16 = reshape(shape = var_2096, x = linear_85_cast_fp16)[name = string("k_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177279616))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178066112))))[name = string("encoder_module_layers_9_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178066304)))];
+            tensor<fp16, [1, 188, 1024]> linear_86_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_v_weight_to_fp16_palettized, x = query_19_cast_fp16)[name = string("linear_86_cast_fp16")];
+            tensor<int32, [4]> var_2101 = const()[name = string("op_2101"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_19_cast_fp16 = reshape(shape = var_2101, x = linear_86_cast_fp16)[name = string("v_19_cast_fp16")];
+            tensor<int32, [4]> value_23_perm_0 = const()[name = string("value_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178068416)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2113_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_module_layers_9_self_attn_pos_bias_u_to_fp16)[name = string("op_2113_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178070528)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2115_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_module_layers_9_self_attn_pos_bias_v_to_fp16)[name = string("op_2115_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_19_perm_0 = const()[name = string("q_with_bias_v_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_225_transpose_x_0 = const()[name = string("x_225_transpose_x_0"), val = bool(false)];
+            bool x_225_transpose_y_0 = const()[name = string("x_225_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2117_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178072640))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178360704))))[name = string("op_2117_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_2115_cast_fp16)[name = string("transpose_249")];
+            tensor<fp16, [1, 8, 188, 375]> x_225_cast_fp16 = matmul(transpose_x = x_225_transpose_x_0, transpose_y = x_225_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = op_2117_to_fp16_palettized)[name = string("x_225_cast_fp16")];
+            tensor<int32, [8]> x_227_pad_0 = const()[name = string("x_227_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_227_mode_0 = const()[name = string("x_227_mode_0"), val = string("constant")];
+            fp16 const_178_to_fp16 = const()[name = string("const_178_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_227_cast_fp16 = pad(constant_val = const_178_to_fp16, mode = x_227_mode_0, pad = x_227_pad_0, x = x_225_cast_fp16)[name = string("x_227_cast_fp16")];
+            tensor<int32, [4]> var_2125 = const()[name = string("op_2125"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_229_cast_fp16 = reshape(shape = var_2125, x = x_227_cast_fp16)[name = string("x_229_cast_fp16")];
+            tensor<int32, [4]> var_2129_begin_0 = const()[name = string("op_2129_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2129_end_0 = const()[name = string("op_2129_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2129_end_mask_0 = const()[name = string("op_2129_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2129_cast_fp16 = slice_by_index(begin = var_2129_begin_0, end = var_2129_end_0, end_mask = var_2129_end_mask_0, x = x_229_cast_fp16)[name = string("op_2129_cast_fp16")];
+            tensor<int32, [4]> var_2130 = const()[name = string("op_2130"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_37_cast_fp16 = reshape(shape = var_2130, x = var_2129_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<int32, [4]> transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = string("transpose_247")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_114 = transpose(perm = transpose_114_perm_0, x = var_2113_cast_fp16)[name = string("transpose_248")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_39_begin_0 = const()[name = string("matrix_bd_39_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_39_end_0 = const()[name = string("matrix_bd_39_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_39_end_mask_0 = const()[name = string("matrix_bd_39_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2139_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = string("op_2139_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<fp16, [1, 8, 188, 188]> _inversed_scores_37_cast_fp16 = mul(x = var_2139_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = string("_inversed_scores_37_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_39_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_15)[name = string("scores_39_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2145_cast_fp16 = softmax(axis = var_152, x = scores_39_cast_fp16)[name = string("op_2145_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_509_cast_fp16 = select(a = var_164_to_fp16, b = var_2145_cast_fp16, cond = mask_15)[name = string("input_509_cast_fp16")];
+            bool x_231_transpose_x_0 = const()[name = string("x_231_transpose_x_0"), val = bool(false)];
+            bool x_231_transpose_y_0 = const()[name = string("x_231_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_19_cast_fp16)[name = string("transpose_246")];
+            tensor<fp16, [1, 8, 188, 128]> x_231_cast_fp16 = matmul(transpose_x = x_231_transpose_x_0, transpose_y = x_231_transpose_y_0, x = input_509_cast_fp16, y = value_23_cast_fp16)[name = string("x_231_cast_fp16")];
+            tensor<int32, [4]> var_2149_perm_0 = const()[name = string("op_2149_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2150 = const()[name = string("op_2150"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2149_cast_fp16 = transpose(perm = var_2149_perm_0, x = x_231_cast_fp16)[name = string("transpose_245")];
+            tensor<fp16, [1, 188, 1024]> input_511_cast_fp16 = reshape(shape = var_2150, x = var_2149_cast_fp16)[name = string("input_511_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178360896))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179147392))))[name = string("encoder_module_layers_9_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179147584)))];
+            tensor<fp16, [1, 188, 1024]> linear_88_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_out_weight_to_fp16_palettized, x = input_511_cast_fp16)[name = string("linear_88_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_515_cast_fp16 = add(x = input_507_cast_fp16, y = linear_88_cast_fp16)[name = string("input_515_cast_fp16")];
+            tensor<int32, [1]> x_235_axes_0 = const()[name = string("x_235_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179149696)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179151808)))];
+            tensor<fp16, [1, 188, 1024]> x_235_cast_fp16 = layer_norm(axes = x_235_axes_0, beta = encoder_module_layers_9_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_conv_weight_to_fp16, x = input_515_cast_fp16)[name = string("x_235_cast_fp16")];
+            tensor<int32, [3]> input_517_perm_0 = const()[name = string("input_517_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_519_pad_type_0 = const()[name = string("input_519_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_519_strides_0 = const()[name = string("input_519_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_519_pad_0 = const()[name = string("input_519_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_519_dilations_0 = const()[name = string("input_519_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_519_groups_0 = const()[name = string("input_519_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_9_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(179153920))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180726848))))[name = string("encoder_module_layers_9_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180727040)))];
+            tensor<fp16, [1, 1024, 188]> input_517_cast_fp16 = transpose(perm = input_517_perm_0, x = x_235_cast_fp16)[name = string("transpose_244")];
+            tensor<fp16, [1, 2048, 188]> input_519_cast_fp16 = conv(bias = encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_9_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_517_cast_fp16)[name = string("input_519_cast_fp16")];
+            int32 x_237_split_num_splits_0 = const()[name = string("x_237_split_num_splits_0"), val = int32(2)];
+            int32 x_237_split_axis_0 = const()[name = string("x_237_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_237_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_237_split_cast_fp16_1 = split(axis = x_237_split_axis_0, num_splits = x_237_split_num_splits_0, x = input_519_cast_fp16)[name = string("x_237_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_237_split_1_sigmoid_cast_fp16 = sigmoid(x = x_237_split_cast_fp16_1)[name = string("x_237_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_237_cast_fp16 = mul(x = x_237_split_cast_fp16_0, y = x_237_split_1_sigmoid_cast_fp16)[name = string("x_237_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_521_cast_fp16 = select(a = var_164_to_fp16, b = x_237_cast_fp16, cond = var_608)[name = string("input_521_cast_fp16")];
+            tensor<int32, [6]> input_523_pad_0 = const()[name = string("input_523_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_523_mode_0 = const()[name = string("input_523_mode_0"), val = string("constant")];
+            fp16 const_181_to_fp16 = const()[name = string("const_181_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_523_cast_fp16 = pad(constant_val = const_181_to_fp16, mode = input_523_mode_0, pad = input_523_pad_0, x = input_521_cast_fp16)[name = string("input_523_cast_fp16")];
+            string input_525_pad_type_0 = const()[name = string("input_525_pad_type_0"), val = string("valid")];
+            int32 input_525_groups_0 = const()[name = string("input_525_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_525_strides_0 = const()[name = string("input_525_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_525_pad_0 = const()[name = string("input_525_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_525_dilations_0 = const()[name = string("input_525_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_340_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180731200))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180738176))))[name = string("const_340_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_341_to_fp16 = const()[name = string("const_341_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180738368)))];
+            tensor<fp16, [1, 1024, 188]> input_527_cast_fp16 = conv(bias = const_341_to_fp16, dilations = input_525_dilations_0, groups = input_525_groups_0, pad = input_525_pad_0, pad_type = input_525_pad_type_0, strides = input_525_strides_0, weight = const_340_to_fp16_palettized, x = input_523_cast_fp16)[name = string("input_527_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_529_cast_fp16 = silu(x = input_527_cast_fp16)[name = string("input_529_cast_fp16")];
+            string x_239_pad_type_0 = const()[name = string("x_239_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_239_strides_0 = const()[name = string("x_239_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_239_pad_0 = const()[name = string("x_239_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_239_dilations_0 = const()[name = string("x_239_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_239_groups_0 = const()[name = string("x_239_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180740480))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181526976))))[name = string("encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181527168)))];
+            tensor<fp16, [1, 1024, 188]> x_239_cast_fp16 = conv(bias = encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16, dilations = x_239_dilations_0, groups = x_239_groups_0, pad = x_239_pad_0, pad_type = x_239_pad_type_0, strides = x_239_strides_0, weight = encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_529_cast_fp16)[name = string("x_239_cast_fp16")];
+            tensor<int32, [3]> input_531_perm_0 = const()[name = string("input_531_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_531_cast_fp16 = transpose(perm = input_531_perm_0, x = x_239_cast_fp16)[name = string("transpose_243")];
+            tensor<fp16, [1, 188, 1024]> input_533_cast_fp16 = add(x = input_515_cast_fp16, y = input_531_cast_fp16)[name = string("input_533_cast_fp16")];
+            tensor<int32, [1]> input_535_axes_0 = const()[name = string("input_535_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181529280)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181531392)))];
+            tensor<fp16, [1, 188, 1024]> input_535_cast_fp16 = layer_norm(axes = input_535_axes_0, beta = encoder_module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_533_cast_fp16)[name = string("input_535_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181533504))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184679296))))[name = string("encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184679488)))];
+            tensor<fp16, [1, 188, 4096]> linear_89_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16_palettized, x = input_535_cast_fp16)[name = string("linear_89_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_539_cast_fp16 = silu(x = linear_89_cast_fp16)[name = string("input_539_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184687744))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187833536))))[name = string("encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187833728)))];
+            tensor<fp16, [1, 188, 1024]> linear_90_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16_palettized, x = input_539_cast_fp16)[name = string("linear_90_cast_fp16")];
+            fp16 var_2216_to_fp16 = const()[name = string("op_2216_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2217_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_2216_to_fp16)[name = string("op_2217_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_545_cast_fp16 = add(x = input_533_cast_fp16, y = var_2217_cast_fp16)[name = string("input_545_cast_fp16")];
+            tensor<int32, [1]> input_547_axes_0 = const()[name = string("input_547_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187835840)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187837952)))];
+            tensor<fp16, [1, 188, 1024]> input_547_cast_fp16 = layer_norm(axes = input_547_axes_0, beta = encoder_module_layers_9_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_out_weight_to_fp16, x = input_545_cast_fp16)[name = string("input_547_cast_fp16")];
+            tensor<int32, [1]> input_549_axes_0 = const()[name = string("input_549_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187840064)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187842176)))];
+            tensor<fp16, [1, 188, 1024]> input_549_cast_fp16 = layer_norm(axes = input_549_axes_0, beta = encoder_module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_547_cast_fp16)[name = string("input_549_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187844288))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190990080))))[name = string("encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190990272)))];
+            tensor<fp16, [1, 188, 4096]> linear_91_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16_palettized, x = input_549_cast_fp16)[name = string("linear_91_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_553_cast_fp16 = silu(x = linear_91_cast_fp16)[name = string("input_553_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190998528))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194144320))))[name = string("encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194144512)))];
+            tensor<fp16, [1, 188, 1024]> linear_92_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16_palettized, x = input_553_cast_fp16)[name = string("linear_92_cast_fp16")];
+            fp16 var_2247_to_fp16 = const()[name = string("op_2247_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2248_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_2247_to_fp16)[name = string("op_2248_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_559_cast_fp16 = add(x = input_547_cast_fp16, y = var_2248_cast_fp16)[name = string("input_559_cast_fp16")];
+            tensor<int32, [1]> query_21_axes_0 = const()[name = string("query_21_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194146624)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194148736)))];
+            tensor<fp16, [1, 188, 1024]> query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = encoder_module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_self_att_weight_to_fp16, x = input_559_cast_fp16)[name = string("query_21_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194150848))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194937344))))[name = string("encoder_module_layers_10_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194937536)))];
+            tensor<fp16, [1, 188, 1024]> linear_93_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_q_weight_to_fp16_palettized, x = query_21_cast_fp16)[name = string("linear_93_cast_fp16")];
+            tensor<int32, [4]> var_2265 = const()[name = string("op_2265"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_61_cast_fp16 = reshape(shape = var_2265, x = linear_93_cast_fp16)[name = string("q_61_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194939648))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195726144))))[name = string("encoder_module_layers_10_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195726336)))];
+            tensor<fp16, [1, 188, 1024]> linear_94_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_k_weight_to_fp16_palettized, x = query_21_cast_fp16)[name = string("linear_94_cast_fp16")];
+            tensor<int32, [4]> var_2270 = const()[name = string("op_2270"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_41_cast_fp16 = reshape(shape = var_2270, x = linear_94_cast_fp16)[name = string("k_41_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195728448))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196514944))))[name = string("encoder_module_layers_10_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196515136)))];
+            tensor<fp16, [1, 188, 1024]> linear_95_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_v_weight_to_fp16_palettized, x = query_21_cast_fp16)[name = string("linear_95_cast_fp16")];
+            tensor<int32, [4]> var_2275 = const()[name = string("op_2275"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_21_cast_fp16 = reshape(shape = var_2275, x = linear_95_cast_fp16)[name = string("v_21_cast_fp16")];
+            tensor<int32, [4]> value_25_perm_0 = const()[name = string("value_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196517248)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2287_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_module_layers_10_self_attn_pos_bias_u_to_fp16)[name = string("op_2287_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196519360)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2289_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_module_layers_10_self_attn_pos_bias_v_to_fp16)[name = string("op_2289_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_21_perm_0 = const()[name = string("q_with_bias_v_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            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<fp16, [1, 8, 128, 375]> op_2291_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196521472))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196809536))))[name = string("op_2291_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2289_cast_fp16)[name = string("transpose_242")];
+            tensor<fp16, [1, 8, 188, 375]> x_247_cast_fp16 = matmul(transpose_x = x_247_transpose_x_0, transpose_y = x_247_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = op_2291_to_fp16_palettized)[name = string("x_247_cast_fp16")];
+            tensor<int32, [8]> x_249_pad_0 = const()[name = string("x_249_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_249_mode_0 = const()[name = string("x_249_mode_0"), val = string("constant")];
+            fp16 const_188_to_fp16 = const()[name = string("const_188_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_249_cast_fp16 = pad(constant_val = const_188_to_fp16, mode = x_249_mode_0, pad = x_249_pad_0, x = x_247_cast_fp16)[name = string("x_249_cast_fp16")];
+            tensor<int32, [4]> var_2299 = const()[name = string("op_2299"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_251_cast_fp16 = reshape(shape = var_2299, x = x_249_cast_fp16)[name = string("x_251_cast_fp16")];
+            tensor<int32, [4]> var_2303_begin_0 = const()[name = string("op_2303_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2303_end_0 = const()[name = string("op_2303_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2303_end_mask_0 = const()[name = string("op_2303_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2303_cast_fp16 = slice_by_index(begin = var_2303_begin_0, end = var_2303_end_0, end_mask = var_2303_end_mask_0, x = x_251_cast_fp16)[name = string("op_2303_cast_fp16")];
+            tensor<int32, [4]> var_2304 = const()[name = string("op_2304"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_41_cast_fp16 = reshape(shape = var_2304, x = var_2303_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<int32, [4]> transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = string("transpose_240")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_116 = transpose(perm = transpose_116_perm_0, x = var_2287_cast_fp16)[name = string("transpose_241")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_43_begin_0 = const()[name = string("matrix_bd_43_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_43_end_0 = const()[name = string("matrix_bd_43_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_43_end_mask_0 = const()[name = string("matrix_bd_43_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2313_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = string("op_2313_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<fp16, [1, 8, 188, 188]> _inversed_scores_41_cast_fp16 = mul(x = var_2313_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = string("_inversed_scores_41_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_43_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_15)[name = string("scores_43_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2319_cast_fp16 = softmax(axis = var_152, x = scores_43_cast_fp16)[name = string("op_2319_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_561_cast_fp16 = select(a = var_164_to_fp16, b = var_2319_cast_fp16, cond = mask_15)[name = string("input_561_cast_fp16")];
+            bool x_253_transpose_x_0 = const()[name = string("x_253_transpose_x_0"), val = bool(false)];
+            bool x_253_transpose_y_0 = const()[name = string("x_253_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_21_cast_fp16)[name = string("transpose_239")];
+            tensor<fp16, [1, 8, 188, 128]> x_253_cast_fp16 = matmul(transpose_x = x_253_transpose_x_0, transpose_y = x_253_transpose_y_0, x = input_561_cast_fp16, y = value_25_cast_fp16)[name = string("x_253_cast_fp16")];
+            tensor<int32, [4]> var_2323_perm_0 = const()[name = string("op_2323_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2324 = const()[name = string("op_2324"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2323_cast_fp16 = transpose(perm = var_2323_perm_0, x = x_253_cast_fp16)[name = string("transpose_238")];
+            tensor<fp16, [1, 188, 1024]> input_563_cast_fp16 = reshape(shape = var_2324, x = var_2323_cast_fp16)[name = string("input_563_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196809728))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197596224))))[name = string("encoder_module_layers_10_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197596416)))];
+            tensor<fp16, [1, 188, 1024]> linear_97_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_out_weight_to_fp16_palettized, x = input_563_cast_fp16)[name = string("linear_97_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_567_cast_fp16 = add(x = input_559_cast_fp16, y = linear_97_cast_fp16)[name = string("input_567_cast_fp16")];
+            tensor<int32, [1]> x_257_axes_0 = const()[name = string("x_257_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197598528)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197600640)))];
+            tensor<fp16, [1, 188, 1024]> x_257_cast_fp16 = layer_norm(axes = x_257_axes_0, beta = encoder_module_layers_10_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_conv_weight_to_fp16, x = input_567_cast_fp16)[name = string("x_257_cast_fp16")];
+            tensor<int32, [3]> input_569_perm_0 = const()[name = string("input_569_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_571_pad_type_0 = const()[name = string("input_571_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_571_strides_0 = const()[name = string("input_571_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_571_pad_0 = const()[name = string("input_571_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_571_dilations_0 = const()[name = string("input_571_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_571_groups_0 = const()[name = string("input_571_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_10_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197602752))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199175680))))[name = string("encoder_module_layers_10_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199175872)))];
+            tensor<fp16, [1, 1024, 188]> input_569_cast_fp16 = transpose(perm = input_569_perm_0, x = x_257_cast_fp16)[name = string("transpose_237")];
+            tensor<fp16, [1, 2048, 188]> input_571_cast_fp16 = conv(bias = encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_10_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_569_cast_fp16)[name = string("input_571_cast_fp16")];
+            int32 x_259_split_num_splits_0 = const()[name = string("x_259_split_num_splits_0"), val = int32(2)];
+            int32 x_259_split_axis_0 = const()[name = string("x_259_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_259_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_259_split_cast_fp16_1 = split(axis = x_259_split_axis_0, num_splits = x_259_split_num_splits_0, x = input_571_cast_fp16)[name = string("x_259_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_259_split_1_sigmoid_cast_fp16 = sigmoid(x = x_259_split_cast_fp16_1)[name = string("x_259_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_259_cast_fp16 = mul(x = x_259_split_cast_fp16_0, y = x_259_split_1_sigmoid_cast_fp16)[name = string("x_259_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_573_cast_fp16 = select(a = var_164_to_fp16, b = x_259_cast_fp16, cond = var_608)[name = string("input_573_cast_fp16")];
+            tensor<int32, [6]> input_575_pad_0 = const()[name = string("input_575_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_575_mode_0 = const()[name = string("input_575_mode_0"), val = string("constant")];
+            fp16 const_191_to_fp16 = const()[name = string("const_191_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_575_cast_fp16 = pad(constant_val = const_191_to_fp16, mode = input_575_mode_0, pad = input_575_pad_0, x = input_573_cast_fp16)[name = string("input_575_cast_fp16")];
+            string input_577_pad_type_0 = const()[name = string("input_577_pad_type_0"), val = string("valid")];
+            int32 input_577_groups_0 = const()[name = string("input_577_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_577_strides_0 = const()[name = string("input_577_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_577_pad_0 = const()[name = string("input_577_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_577_dilations_0 = const()[name = string("input_577_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_342_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199180032))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199187008))))[name = string("const_342_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_343_to_fp16 = const()[name = string("const_343_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199187200)))];
+            tensor<fp16, [1, 1024, 188]> input_579_cast_fp16 = conv(bias = const_343_to_fp16, dilations = input_577_dilations_0, groups = input_577_groups_0, pad = input_577_pad_0, pad_type = input_577_pad_type_0, strides = input_577_strides_0, weight = const_342_to_fp16_palettized, x = input_575_cast_fp16)[name = string("input_579_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_581_cast_fp16 = silu(x = input_579_cast_fp16)[name = string("input_581_cast_fp16")];
+            string x_261_pad_type_0 = const()[name = string("x_261_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_261_strides_0 = const()[name = string("x_261_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_261_pad_0 = const()[name = string("x_261_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_261_dilations_0 = const()[name = string("x_261_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_261_groups_0 = const()[name = string("x_261_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199189312))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199975808))))[name = string("encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199976000)))];
+            tensor<fp16, [1, 1024, 188]> x_261_cast_fp16 = conv(bias = encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16, dilations = x_261_dilations_0, groups = x_261_groups_0, pad = x_261_pad_0, pad_type = x_261_pad_type_0, strides = x_261_strides_0, weight = encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_581_cast_fp16)[name = string("x_261_cast_fp16")];
+            tensor<int32, [3]> input_583_perm_0 = const()[name = string("input_583_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_583_cast_fp16 = transpose(perm = input_583_perm_0, x = x_261_cast_fp16)[name = string("transpose_236")];
+            tensor<fp16, [1, 188, 1024]> input_585_cast_fp16 = add(x = input_567_cast_fp16, y = input_583_cast_fp16)[name = string("input_585_cast_fp16")];
+            tensor<int32, [1]> input_587_axes_0 = const()[name = string("input_587_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199978112)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199980224)))];
+            tensor<fp16, [1, 188, 1024]> input_587_cast_fp16 = layer_norm(axes = input_587_axes_0, beta = encoder_module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_585_cast_fp16)[name = string("input_587_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199982336))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203128128))))[name = string("encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203128320)))];
+            tensor<fp16, [1, 188, 4096]> linear_98_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16_palettized, x = input_587_cast_fp16)[name = string("linear_98_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_591_cast_fp16 = silu(x = linear_98_cast_fp16)[name = string("input_591_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203136576))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206282368))))[name = string("encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206282560)))];
+            tensor<fp16, [1, 188, 1024]> linear_99_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16_palettized, x = input_591_cast_fp16)[name = string("linear_99_cast_fp16")];
+            fp16 var_2390_to_fp16 = const()[name = string("op_2390_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2391_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2390_to_fp16)[name = string("op_2391_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_597_cast_fp16 = add(x = input_585_cast_fp16, y = var_2391_cast_fp16)[name = string("input_597_cast_fp16")];
+            tensor<int32, [1]> input_599_axes_0 = const()[name = string("input_599_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206284672)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206286784)))];
+            tensor<fp16, [1, 188, 1024]> input_599_cast_fp16 = layer_norm(axes = input_599_axes_0, beta = encoder_module_layers_10_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_out_weight_to_fp16, x = input_597_cast_fp16)[name = string("input_599_cast_fp16")];
+            tensor<int32, [1]> input_601_axes_0 = const()[name = string("input_601_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206288896)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206291008)))];
+            tensor<fp16, [1, 188, 1024]> input_601_cast_fp16 = layer_norm(axes = input_601_axes_0, beta = encoder_module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_599_cast_fp16)[name = string("input_601_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206293120))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209438912))))[name = string("encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209439104)))];
+            tensor<fp16, [1, 188, 4096]> linear_100_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16_palettized, x = input_601_cast_fp16)[name = string("linear_100_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_605_cast_fp16 = silu(x = linear_100_cast_fp16)[name = string("input_605_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209447360))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212593152))))[name = string("encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212593344)))];
+            tensor<fp16, [1, 188, 1024]> linear_101_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16_palettized, x = input_605_cast_fp16)[name = string("linear_101_cast_fp16")];
+            fp16 var_2421_to_fp16 = const()[name = string("op_2421_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2422_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2421_to_fp16)[name = string("op_2422_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_611_cast_fp16 = add(x = input_599_cast_fp16, y = var_2422_cast_fp16)[name = string("input_611_cast_fp16")];
+            tensor<int32, [1]> query_23_axes_0 = const()[name = string("query_23_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212595456)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212597568)))];
+            tensor<fp16, [1, 188, 1024]> query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = encoder_module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_self_att_weight_to_fp16, x = input_611_cast_fp16)[name = string("query_23_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212599680))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213386176))))[name = string("encoder_module_layers_11_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213386368)))];
+            tensor<fp16, [1, 188, 1024]> linear_102_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_q_weight_to_fp16_palettized, x = query_23_cast_fp16)[name = string("linear_102_cast_fp16")];
+            tensor<int32, [4]> var_2439 = const()[name = string("op_2439"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_67_cast_fp16 = reshape(shape = var_2439, x = linear_102_cast_fp16)[name = string("q_67_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213388480))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214174976))))[name = string("encoder_module_layers_11_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214175168)))];
+            tensor<fp16, [1, 188, 1024]> linear_103_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_k_weight_to_fp16_palettized, x = query_23_cast_fp16)[name = string("linear_103_cast_fp16")];
+            tensor<int32, [4]> var_2444 = const()[name = string("op_2444"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_45_cast_fp16 = reshape(shape = var_2444, x = linear_103_cast_fp16)[name = string("k_45_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214177280))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214963776))))[name = string("encoder_module_layers_11_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214963968)))];
+            tensor<fp16, [1, 188, 1024]> linear_104_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_v_weight_to_fp16_palettized, x = query_23_cast_fp16)[name = string("linear_104_cast_fp16")];
+            tensor<int32, [4]> var_2449 = const()[name = string("op_2449"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_23_cast_fp16 = reshape(shape = var_2449, x = linear_104_cast_fp16)[name = string("v_23_cast_fp16")];
+            tensor<int32, [4]> value_27_perm_0 = const()[name = string("value_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214966080)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2461_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_module_layers_11_self_attn_pos_bias_u_to_fp16)[name = string("op_2461_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214968192)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2463_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_module_layers_11_self_attn_pos_bias_v_to_fp16)[name = string("op_2463_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_23_perm_0 = const()[name = string("q_with_bias_v_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_269_transpose_x_0 = const()[name = string("x_269_transpose_x_0"), val = bool(false)];
+            bool x_269_transpose_y_0 = const()[name = string("x_269_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2465_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214970304))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215258368))))[name = string("op_2465_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2463_cast_fp16)[name = string("transpose_235")];
+            tensor<fp16, [1, 8, 188, 375]> x_269_cast_fp16 = matmul(transpose_x = x_269_transpose_x_0, transpose_y = x_269_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = op_2465_to_fp16_palettized)[name = string("x_269_cast_fp16")];
+            tensor<int32, [8]> x_271_pad_0 = const()[name = string("x_271_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_271_mode_0 = const()[name = string("x_271_mode_0"), val = string("constant")];
+            fp16 const_198_to_fp16 = const()[name = string("const_198_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_271_cast_fp16 = pad(constant_val = const_198_to_fp16, mode = x_271_mode_0, pad = x_271_pad_0, x = x_269_cast_fp16)[name = string("x_271_cast_fp16")];
+            tensor<int32, [4]> var_2473 = const()[name = string("op_2473"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_273_cast_fp16 = reshape(shape = var_2473, x = x_271_cast_fp16)[name = string("x_273_cast_fp16")];
+            tensor<int32, [4]> var_2477_begin_0 = const()[name = string("op_2477_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2477_end_0 = const()[name = string("op_2477_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2477_end_mask_0 = const()[name = string("op_2477_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2477_cast_fp16 = slice_by_index(begin = var_2477_begin_0, end = var_2477_end_0, end_mask = var_2477_end_mask_0, x = x_273_cast_fp16)[name = string("op_2477_cast_fp16")];
+            tensor<int32, [4]> var_2478 = const()[name = string("op_2478"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_45_cast_fp16 = reshape(shape = var_2478, x = var_2477_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<int32, [4]> transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = string("transpose_233")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2461_cast_fp16)[name = string("transpose_234")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_47_begin_0 = const()[name = string("matrix_bd_47_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_47_end_0 = const()[name = string("matrix_bd_47_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_47_end_mask_0 = const()[name = string("matrix_bd_47_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2487_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = string("op_2487_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<fp16, [1, 8, 188, 188]> _inversed_scores_45_cast_fp16 = mul(x = var_2487_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = string("_inversed_scores_45_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_47_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_15)[name = string("scores_47_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2493_cast_fp16 = softmax(axis = var_152, x = scores_47_cast_fp16)[name = string("op_2493_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_613_cast_fp16 = select(a = var_164_to_fp16, b = var_2493_cast_fp16, cond = mask_15)[name = string("input_613_cast_fp16")];
+            bool x_275_transpose_x_0 = const()[name = string("x_275_transpose_x_0"), val = bool(false)];
+            bool x_275_transpose_y_0 = const()[name = string("x_275_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_23_cast_fp16)[name = string("transpose_232")];
+            tensor<fp16, [1, 8, 188, 128]> x_275_cast_fp16 = matmul(transpose_x = x_275_transpose_x_0, transpose_y = x_275_transpose_y_0, x = input_613_cast_fp16, y = value_27_cast_fp16)[name = string("x_275_cast_fp16")];
+            tensor<int32, [4]> var_2497_perm_0 = const()[name = string("op_2497_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2498 = const()[name = string("op_2498"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2497_cast_fp16 = transpose(perm = var_2497_perm_0, x = x_275_cast_fp16)[name = string("transpose_231")];
+            tensor<fp16, [1, 188, 1024]> input_615_cast_fp16 = reshape(shape = var_2498, x = var_2497_cast_fp16)[name = string("input_615_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215258560))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216045056))))[name = string("encoder_module_layers_11_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216045248)))];
+            tensor<fp16, [1, 188, 1024]> linear_106_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_out_weight_to_fp16_palettized, x = input_615_cast_fp16)[name = string("linear_106_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_619_cast_fp16 = add(x = input_611_cast_fp16, y = linear_106_cast_fp16)[name = string("input_619_cast_fp16")];
+            tensor<int32, [1]> x_279_axes_0 = const()[name = string("x_279_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216047360)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216049472)))];
+            tensor<fp16, [1, 188, 1024]> x_279_cast_fp16 = layer_norm(axes = x_279_axes_0, beta = encoder_module_layers_11_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_conv_weight_to_fp16, x = input_619_cast_fp16)[name = string("x_279_cast_fp16")];
+            tensor<int32, [3]> input_621_perm_0 = const()[name = string("input_621_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_623_pad_type_0 = const()[name = string("input_623_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_623_strides_0 = const()[name = string("input_623_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_623_pad_0 = const()[name = string("input_623_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_623_dilations_0 = const()[name = string("input_623_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_623_groups_0 = const()[name = string("input_623_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_11_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216051584))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217624512))))[name = string("encoder_module_layers_11_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217624704)))];
+            tensor<fp16, [1, 1024, 188]> input_621_cast_fp16 = transpose(perm = input_621_perm_0, x = x_279_cast_fp16)[name = string("transpose_230")];
+            tensor<fp16, [1, 2048, 188]> input_623_cast_fp16 = conv(bias = encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_11_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_621_cast_fp16)[name = string("input_623_cast_fp16")];
+            int32 x_281_split_num_splits_0 = const()[name = string("x_281_split_num_splits_0"), val = int32(2)];
+            int32 x_281_split_axis_0 = const()[name = string("x_281_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_281_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_281_split_cast_fp16_1 = split(axis = x_281_split_axis_0, num_splits = x_281_split_num_splits_0, x = input_623_cast_fp16)[name = string("x_281_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_281_split_1_sigmoid_cast_fp16 = sigmoid(x = x_281_split_cast_fp16_1)[name = string("x_281_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_281_cast_fp16 = mul(x = x_281_split_cast_fp16_0, y = x_281_split_1_sigmoid_cast_fp16)[name = string("x_281_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_625_cast_fp16 = select(a = var_164_to_fp16, b = x_281_cast_fp16, cond = var_608)[name = string("input_625_cast_fp16")];
+            tensor<int32, [6]> input_627_pad_0 = const()[name = string("input_627_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_627_mode_0 = const()[name = string("input_627_mode_0"), val = string("constant")];
+            fp16 const_201_to_fp16 = const()[name = string("const_201_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_627_cast_fp16 = pad(constant_val = const_201_to_fp16, mode = input_627_mode_0, pad = input_627_pad_0, x = input_625_cast_fp16)[name = string("input_627_cast_fp16")];
+            string input_629_pad_type_0 = const()[name = string("input_629_pad_type_0"), val = string("valid")];
+            int32 input_629_groups_0 = const()[name = string("input_629_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_629_strides_0 = const()[name = string("input_629_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_629_pad_0 = const()[name = string("input_629_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_629_dilations_0 = const()[name = string("input_629_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_344_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217628864))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217635840))))[name = string("const_344_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_345_to_fp16 = const()[name = string("const_345_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217636032)))];
+            tensor<fp16, [1, 1024, 188]> input_631_cast_fp16 = conv(bias = const_345_to_fp16, dilations = input_629_dilations_0, groups = input_629_groups_0, pad = input_629_pad_0, pad_type = input_629_pad_type_0, strides = input_629_strides_0, weight = const_344_to_fp16_palettized, x = input_627_cast_fp16)[name = string("input_631_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_633_cast_fp16 = silu(x = input_631_cast_fp16)[name = string("input_633_cast_fp16")];
+            string x_283_pad_type_0 = const()[name = string("x_283_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_283_strides_0 = const()[name = string("x_283_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_283_pad_0 = const()[name = string("x_283_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_283_dilations_0 = const()[name = string("x_283_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_283_groups_0 = const()[name = string("x_283_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217638144))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218424640))))[name = string("encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218424832)))];
+            tensor<fp16, [1, 1024, 188]> x_283_cast_fp16 = conv(bias = encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16, dilations = x_283_dilations_0, groups = x_283_groups_0, pad = x_283_pad_0, pad_type = x_283_pad_type_0, strides = x_283_strides_0, weight = encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_633_cast_fp16)[name = string("x_283_cast_fp16")];
+            tensor<int32, [3]> input_635_perm_0 = const()[name = string("input_635_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_635_cast_fp16 = transpose(perm = input_635_perm_0, x = x_283_cast_fp16)[name = string("transpose_229")];
+            tensor<fp16, [1, 188, 1024]> input_637_cast_fp16 = add(x = input_619_cast_fp16, y = input_635_cast_fp16)[name = string("input_637_cast_fp16")];
+            tensor<int32, [1]> input_639_axes_0 = const()[name = string("input_639_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218426944)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218429056)))];
+            tensor<fp16, [1, 188, 1024]> input_639_cast_fp16 = layer_norm(axes = input_639_axes_0, beta = encoder_module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_637_cast_fp16)[name = string("input_639_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218431168))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221576960))))[name = string("encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221577152)))];
+            tensor<fp16, [1, 188, 4096]> linear_107_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16_palettized, x = input_639_cast_fp16)[name = string("linear_107_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_643_cast_fp16 = silu(x = linear_107_cast_fp16)[name = string("input_643_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221585408))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224731200))))[name = string("encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224731392)))];
+            tensor<fp16, [1, 188, 1024]> linear_108_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16_palettized, x = input_643_cast_fp16)[name = string("linear_108_cast_fp16")];
+            fp16 var_2564_to_fp16 = const()[name = string("op_2564_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2565_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2564_to_fp16)[name = string("op_2565_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_649_cast_fp16 = add(x = input_637_cast_fp16, y = var_2565_cast_fp16)[name = string("input_649_cast_fp16")];
+            tensor<int32, [1]> input_651_axes_0 = const()[name = string("input_651_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224733504)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224735616)))];
+            tensor<fp16, [1, 188, 1024]> input_651_cast_fp16 = layer_norm(axes = input_651_axes_0, beta = encoder_module_layers_11_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_out_weight_to_fp16, x = input_649_cast_fp16)[name = string("input_651_cast_fp16")];
+            tensor<int32, [1]> input_653_axes_0 = const()[name = string("input_653_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224737728)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224739840)))];
+            tensor<fp16, [1, 188, 1024]> input_653_cast_fp16 = layer_norm(axes = input_653_axes_0, beta = encoder_module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_651_cast_fp16)[name = string("input_653_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224741952))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227887744))))[name = string("encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227887936)))];
+            tensor<fp16, [1, 188, 4096]> linear_109_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16_palettized, x = input_653_cast_fp16)[name = string("linear_109_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_657_cast_fp16 = silu(x = linear_109_cast_fp16)[name = string("input_657_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227896192))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231041984))))[name = string("encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231042176)))];
+            tensor<fp16, [1, 188, 1024]> linear_110_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16_palettized, x = input_657_cast_fp16)[name = string("linear_110_cast_fp16")];
+            fp16 var_2595_to_fp16 = const()[name = string("op_2595_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2596_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2595_to_fp16)[name = string("op_2596_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_663_cast_fp16 = add(x = input_651_cast_fp16, y = var_2596_cast_fp16)[name = string("input_663_cast_fp16")];
+            tensor<int32, [1]> query_25_axes_0 = const()[name = string("query_25_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231044288)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231046400)))];
+            tensor<fp16, [1, 188, 1024]> query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = encoder_module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_self_att_weight_to_fp16, x = input_663_cast_fp16)[name = string("query_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231048512))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231835008))))[name = string("encoder_module_layers_12_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231835200)))];
+            tensor<fp16, [1, 188, 1024]> linear_111_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_q_weight_to_fp16_palettized, x = query_25_cast_fp16)[name = string("linear_111_cast_fp16")];
+            tensor<int32, [4]> var_2613 = const()[name = string("op_2613"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_73_cast_fp16 = reshape(shape = var_2613, x = linear_111_cast_fp16)[name = string("q_73_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231837312))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232623808))))[name = string("encoder_module_layers_12_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232624000)))];
+            tensor<fp16, [1, 188, 1024]> linear_112_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_k_weight_to_fp16_palettized, x = query_25_cast_fp16)[name = string("linear_112_cast_fp16")];
+            tensor<int32, [4]> var_2618 = const()[name = string("op_2618"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_49_cast_fp16 = reshape(shape = var_2618, x = linear_112_cast_fp16)[name = string("k_49_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232626112))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233412608))))[name = string("encoder_module_layers_12_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233412800)))];
+            tensor<fp16, [1, 188, 1024]> linear_113_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_v_weight_to_fp16_palettized, x = query_25_cast_fp16)[name = string("linear_113_cast_fp16")];
+            tensor<int32, [4]> var_2623 = const()[name = string("op_2623"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_25_cast_fp16 = reshape(shape = var_2623, x = linear_113_cast_fp16)[name = string("v_25_cast_fp16")];
+            tensor<int32, [4]> value_29_perm_0 = const()[name = string("value_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233414912)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2635_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_module_layers_12_self_attn_pos_bias_u_to_fp16)[name = string("op_2635_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233417024)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2637_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_module_layers_12_self_attn_pos_bias_v_to_fp16)[name = string("op_2637_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_25_perm_0 = const()[name = string("q_with_bias_v_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_291_transpose_x_0 = const()[name = string("x_291_transpose_x_0"), val = bool(false)];
+            bool x_291_transpose_y_0 = const()[name = string("x_291_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2639_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233419136))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233707200))))[name = string("op_2639_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2637_cast_fp16)[name = string("transpose_228")];
+            tensor<fp16, [1, 8, 188, 375]> x_291_cast_fp16 = matmul(transpose_x = x_291_transpose_x_0, transpose_y = x_291_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = op_2639_to_fp16_palettized)[name = string("x_291_cast_fp16")];
+            tensor<int32, [8]> x_293_pad_0 = const()[name = string("x_293_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_293_mode_0 = const()[name = string("x_293_mode_0"), val = string("constant")];
+            fp16 const_208_to_fp16 = const()[name = string("const_208_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_293_cast_fp16 = pad(constant_val = const_208_to_fp16, mode = x_293_mode_0, pad = x_293_pad_0, x = x_291_cast_fp16)[name = string("x_293_cast_fp16")];
+            tensor<int32, [4]> var_2647 = const()[name = string("op_2647"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_295_cast_fp16 = reshape(shape = var_2647, x = x_293_cast_fp16)[name = string("x_295_cast_fp16")];
+            tensor<int32, [4]> var_2651_begin_0 = const()[name = string("op_2651_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2651_end_0 = const()[name = string("op_2651_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2651_end_mask_0 = const()[name = string("op_2651_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2651_cast_fp16 = slice_by_index(begin = var_2651_begin_0, end = var_2651_end_0, end_mask = var_2651_end_mask_0, x = x_295_cast_fp16)[name = string("op_2651_cast_fp16")];
+            tensor<int32, [4]> var_2652 = const()[name = string("op_2652"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_49_cast_fp16 = reshape(shape = var_2652, x = var_2651_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<int32, [4]> transpose_120_perm_0 = const()[name = string("transpose_120_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_121_perm_0 = const()[name = string("transpose_121_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = string("transpose_226")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_120 = transpose(perm = transpose_120_perm_0, x = var_2635_cast_fp16)[name = string("transpose_227")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_51_begin_0 = const()[name = string("matrix_bd_51_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_51_end_0 = const()[name = string("matrix_bd_51_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_51_end_mask_0 = const()[name = string("matrix_bd_51_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2661_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = string("op_2661_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<fp16, [1, 8, 188, 188]> _inversed_scores_49_cast_fp16 = mul(x = var_2661_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = string("_inversed_scores_49_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_51_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_15)[name = string("scores_51_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2667_cast_fp16 = softmax(axis = var_152, x = scores_51_cast_fp16)[name = string("op_2667_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_665_cast_fp16 = select(a = var_164_to_fp16, b = var_2667_cast_fp16, cond = mask_15)[name = string("input_665_cast_fp16")];
+            bool x_297_transpose_x_0 = const()[name = string("x_297_transpose_x_0"), val = bool(false)];
+            bool x_297_transpose_y_0 = const()[name = string("x_297_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_25_cast_fp16)[name = string("transpose_225")];
+            tensor<fp16, [1, 8, 188, 128]> x_297_cast_fp16 = matmul(transpose_x = x_297_transpose_x_0, transpose_y = x_297_transpose_y_0, x = input_665_cast_fp16, y = value_29_cast_fp16)[name = string("x_297_cast_fp16")];
+            tensor<int32, [4]> var_2671_perm_0 = const()[name = string("op_2671_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2672 = const()[name = string("op_2672"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2671_cast_fp16 = transpose(perm = var_2671_perm_0, x = x_297_cast_fp16)[name = string("transpose_224")];
+            tensor<fp16, [1, 188, 1024]> input_667_cast_fp16 = reshape(shape = var_2672, x = var_2671_cast_fp16)[name = string("input_667_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233707392))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234493888))))[name = string("encoder_module_layers_12_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234494080)))];
+            tensor<fp16, [1, 188, 1024]> linear_115_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_out_weight_to_fp16_palettized, x = input_667_cast_fp16)[name = string("linear_115_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_671_cast_fp16 = add(x = input_663_cast_fp16, y = linear_115_cast_fp16)[name = string("input_671_cast_fp16")];
+            tensor<int32, [1]> x_301_axes_0 = const()[name = string("x_301_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234496192)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234498304)))];
+            tensor<fp16, [1, 188, 1024]> x_301_cast_fp16 = layer_norm(axes = x_301_axes_0, beta = encoder_module_layers_12_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_conv_weight_to_fp16, x = input_671_cast_fp16)[name = string("x_301_cast_fp16")];
+            tensor<int32, [3]> input_673_perm_0 = const()[name = string("input_673_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_675_pad_type_0 = const()[name = string("input_675_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_675_strides_0 = const()[name = string("input_675_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_675_pad_0 = const()[name = string("input_675_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_675_dilations_0 = const()[name = string("input_675_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_675_groups_0 = const()[name = string("input_675_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_12_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234500416))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236073344))))[name = string("encoder_module_layers_12_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236073536)))];
+            tensor<fp16, [1, 1024, 188]> input_673_cast_fp16 = transpose(perm = input_673_perm_0, x = x_301_cast_fp16)[name = string("transpose_223")];
+            tensor<fp16, [1, 2048, 188]> input_675_cast_fp16 = conv(bias = encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_12_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_673_cast_fp16)[name = string("input_675_cast_fp16")];
+            int32 x_303_split_num_splits_0 = const()[name = string("x_303_split_num_splits_0"), val = int32(2)];
+            int32 x_303_split_axis_0 = const()[name = string("x_303_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_303_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_303_split_cast_fp16_1 = split(axis = x_303_split_axis_0, num_splits = x_303_split_num_splits_0, x = input_675_cast_fp16)[name = string("x_303_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_303_split_1_sigmoid_cast_fp16 = sigmoid(x = x_303_split_cast_fp16_1)[name = string("x_303_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_303_cast_fp16 = mul(x = x_303_split_cast_fp16_0, y = x_303_split_1_sigmoid_cast_fp16)[name = string("x_303_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_677_cast_fp16 = select(a = var_164_to_fp16, b = x_303_cast_fp16, cond = var_608)[name = string("input_677_cast_fp16")];
+            tensor<int32, [6]> input_679_pad_0 = const()[name = string("input_679_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_679_mode_0 = const()[name = string("input_679_mode_0"), val = string("constant")];
+            fp16 const_211_to_fp16 = const()[name = string("const_211_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_679_cast_fp16 = pad(constant_val = const_211_to_fp16, mode = input_679_mode_0, pad = input_679_pad_0, x = input_677_cast_fp16)[name = string("input_679_cast_fp16")];
+            string input_681_pad_type_0 = const()[name = string("input_681_pad_type_0"), val = string("valid")];
+            int32 input_681_groups_0 = const()[name = string("input_681_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_681_strides_0 = const()[name = string("input_681_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_681_pad_0 = const()[name = string("input_681_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_681_dilations_0 = const()[name = string("input_681_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_346_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236077696))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236084672))))[name = string("const_346_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_347_to_fp16 = const()[name = string("const_347_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236084864)))];
+            tensor<fp16, [1, 1024, 188]> input_683_cast_fp16 = conv(bias = const_347_to_fp16, dilations = input_681_dilations_0, groups = input_681_groups_0, pad = input_681_pad_0, pad_type = input_681_pad_type_0, strides = input_681_strides_0, weight = const_346_to_fp16_palettized, x = input_679_cast_fp16)[name = string("input_683_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_685_cast_fp16 = silu(x = input_683_cast_fp16)[name = string("input_685_cast_fp16")];
+            string x_305_pad_type_0 = const()[name = string("x_305_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_305_strides_0 = const()[name = string("x_305_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_305_pad_0 = const()[name = string("x_305_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_305_dilations_0 = const()[name = string("x_305_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_305_groups_0 = const()[name = string("x_305_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236086976))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236873472))))[name = string("encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236873664)))];
+            tensor<fp16, [1, 1024, 188]> x_305_cast_fp16 = conv(bias = encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16, dilations = x_305_dilations_0, groups = x_305_groups_0, pad = x_305_pad_0, pad_type = x_305_pad_type_0, strides = x_305_strides_0, weight = encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_685_cast_fp16)[name = string("x_305_cast_fp16")];
+            tensor<int32, [3]> input_687_perm_0 = const()[name = string("input_687_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_687_cast_fp16 = transpose(perm = input_687_perm_0, x = x_305_cast_fp16)[name = string("transpose_222")];
+            tensor<fp16, [1, 188, 1024]> input_689_cast_fp16 = add(x = input_671_cast_fp16, y = input_687_cast_fp16)[name = string("input_689_cast_fp16")];
+            tensor<int32, [1]> input_691_axes_0 = const()[name = string("input_691_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236875776)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236877888)))];
+            tensor<fp16, [1, 188, 1024]> input_691_cast_fp16 = layer_norm(axes = input_691_axes_0, beta = encoder_module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_689_cast_fp16)[name = string("input_691_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236880000))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240025792))))[name = string("encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240025984)))];
+            tensor<fp16, [1, 188, 4096]> linear_116_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16_palettized, x = input_691_cast_fp16)[name = string("linear_116_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_695_cast_fp16 = silu(x = linear_116_cast_fp16)[name = string("input_695_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240034240))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243180032))))[name = string("encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243180224)))];
+            tensor<fp16, [1, 188, 1024]> linear_117_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16_palettized, x = input_695_cast_fp16)[name = string("linear_117_cast_fp16")];
+            fp16 var_2738_to_fp16 = const()[name = string("op_2738_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2739_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2738_to_fp16)[name = string("op_2739_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_701_cast_fp16 = add(x = input_689_cast_fp16, y = var_2739_cast_fp16)[name = string("input_701_cast_fp16")];
+            tensor<int32, [1]> input_703_axes_0 = const()[name = string("input_703_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243182336)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243184448)))];
+            tensor<fp16, [1, 188, 1024]> input_703_cast_fp16 = layer_norm(axes = input_703_axes_0, beta = encoder_module_layers_12_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_out_weight_to_fp16, x = input_701_cast_fp16)[name = string("input_703_cast_fp16")];
+            tensor<int32, [1]> input_705_axes_0 = const()[name = string("input_705_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243186560)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243188672)))];
+            tensor<fp16, [1, 188, 1024]> input_705_cast_fp16 = layer_norm(axes = input_705_axes_0, beta = encoder_module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_703_cast_fp16)[name = string("input_705_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243190784))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246336576))))[name = string("encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246336768)))];
+            tensor<fp16, [1, 188, 4096]> linear_118_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16_palettized, x = input_705_cast_fp16)[name = string("linear_118_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_709_cast_fp16 = silu(x = linear_118_cast_fp16)[name = string("input_709_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246345024))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(249490816))))[name = string("encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(249491008)))];
+            tensor<fp16, [1, 188, 1024]> linear_119_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16_palettized, x = input_709_cast_fp16)[name = string("linear_119_cast_fp16")];
+            fp16 var_2769_to_fp16 = const()[name = string("op_2769_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2770_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2769_to_fp16)[name = string("op_2770_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_715_cast_fp16 = add(x = input_703_cast_fp16, y = var_2770_cast_fp16)[name = string("input_715_cast_fp16")];
+            tensor<int32, [1]> query_27_axes_0 = const()[name = string("query_27_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(249493120)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(249495232)))];
+            tensor<fp16, [1, 188, 1024]> query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = encoder_module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_self_att_weight_to_fp16, x = input_715_cast_fp16)[name = string("query_27_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(249497344))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250283840))))[name = string("encoder_module_layers_13_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250284032)))];
+            tensor<fp16, [1, 188, 1024]> linear_120_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_q_weight_to_fp16_palettized, x = query_27_cast_fp16)[name = string("linear_120_cast_fp16")];
+            tensor<int32, [4]> var_2787 = const()[name = string("op_2787"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_79_cast_fp16 = reshape(shape = var_2787, x = linear_120_cast_fp16)[name = string("q_79_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250286144))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251072640))))[name = string("encoder_module_layers_13_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251072832)))];
+            tensor<fp16, [1, 188, 1024]> linear_121_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_k_weight_to_fp16_palettized, x = query_27_cast_fp16)[name = string("linear_121_cast_fp16")];
+            tensor<int32, [4]> var_2792 = const()[name = string("op_2792"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_53_cast_fp16 = reshape(shape = var_2792, x = linear_121_cast_fp16)[name = string("k_53_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251074944))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251861440))))[name = string("encoder_module_layers_13_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251861632)))];
+            tensor<fp16, [1, 188, 1024]> linear_122_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_v_weight_to_fp16_palettized, x = query_27_cast_fp16)[name = string("linear_122_cast_fp16")];
+            tensor<int32, [4]> var_2797 = const()[name = string("op_2797"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_27_cast_fp16 = reshape(shape = var_2797, x = linear_122_cast_fp16)[name = string("v_27_cast_fp16")];
+            tensor<int32, [4]> value_31_perm_0 = const()[name = string("value_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251863744)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2809_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_module_layers_13_self_attn_pos_bias_u_to_fp16)[name = string("op_2809_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251865856)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2811_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_module_layers_13_self_attn_pos_bias_v_to_fp16)[name = string("op_2811_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_27_perm_0 = const()[name = string("q_with_bias_v_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_313_transpose_x_0 = const()[name = string("x_313_transpose_x_0"), val = bool(false)];
+            bool x_313_transpose_y_0 = const()[name = string("x_313_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2813_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(251867968))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252156032))))[name = string("op_2813_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2811_cast_fp16)[name = string("transpose_221")];
+            tensor<fp16, [1, 8, 188, 375]> x_313_cast_fp16 = matmul(transpose_x = x_313_transpose_x_0, transpose_y = x_313_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = op_2813_to_fp16_palettized)[name = string("x_313_cast_fp16")];
+            tensor<int32, [8]> x_315_pad_0 = const()[name = string("x_315_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_315_mode_0 = const()[name = string("x_315_mode_0"), val = string("constant")];
+            fp16 const_218_to_fp16 = const()[name = string("const_218_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_315_cast_fp16 = pad(constant_val = const_218_to_fp16, mode = x_315_mode_0, pad = x_315_pad_0, x = x_313_cast_fp16)[name = string("x_315_cast_fp16")];
+            tensor<int32, [4]> var_2821 = const()[name = string("op_2821"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_317_cast_fp16 = reshape(shape = var_2821, x = x_315_cast_fp16)[name = string("x_317_cast_fp16")];
+            tensor<int32, [4]> var_2825_begin_0 = const()[name = string("op_2825_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2825_end_0 = const()[name = string("op_2825_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2825_end_mask_0 = const()[name = string("op_2825_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2825_cast_fp16 = slice_by_index(begin = var_2825_begin_0, end = var_2825_end_0, end_mask = var_2825_end_mask_0, x = x_317_cast_fp16)[name = string("op_2825_cast_fp16")];
+            tensor<int32, [4]> var_2826 = const()[name = string("op_2826"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_53_cast_fp16 = reshape(shape = var_2826, x = var_2825_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<int32, [4]> transpose_122_perm_0 = const()[name = string("transpose_122_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_123_perm_0 = const()[name = string("transpose_123_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = string("transpose_219")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_122 = transpose(perm = transpose_122_perm_0, x = var_2809_cast_fp16)[name = string("transpose_220")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_55_begin_0 = const()[name = string("matrix_bd_55_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_55_end_0 = const()[name = string("matrix_bd_55_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_55_end_mask_0 = const()[name = string("matrix_bd_55_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2835_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = string("op_2835_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<fp16, [1, 8, 188, 188]> _inversed_scores_53_cast_fp16 = mul(x = var_2835_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = string("_inversed_scores_53_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_55_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_15)[name = string("scores_55_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2841_cast_fp16 = softmax(axis = var_152, x = scores_55_cast_fp16)[name = string("op_2841_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_717_cast_fp16 = select(a = var_164_to_fp16, b = var_2841_cast_fp16, cond = mask_15)[name = string("input_717_cast_fp16")];
+            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<fp16, [1, 8, 188, 128]> value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_27_cast_fp16)[name = string("transpose_218")];
+            tensor<fp16, [1, 8, 188, 128]> x_319_cast_fp16 = matmul(transpose_x = x_319_transpose_x_0, transpose_y = x_319_transpose_y_0, x = input_717_cast_fp16, y = value_31_cast_fp16)[name = string("x_319_cast_fp16")];
+            tensor<int32, [4]> var_2845_perm_0 = const()[name = string("op_2845_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2846 = const()[name = string("op_2846"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2845_cast_fp16 = transpose(perm = var_2845_perm_0, x = x_319_cast_fp16)[name = string("transpose_217")];
+            tensor<fp16, [1, 188, 1024]> input_719_cast_fp16 = reshape(shape = var_2846, x = var_2845_cast_fp16)[name = string("input_719_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252156224))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252942720))))[name = string("encoder_module_layers_13_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252942912)))];
+            tensor<fp16, [1, 188, 1024]> linear_124_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_out_weight_to_fp16_palettized, x = input_719_cast_fp16)[name = string("linear_124_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_723_cast_fp16 = add(x = input_715_cast_fp16, y = linear_124_cast_fp16)[name = string("input_723_cast_fp16")];
+            tensor<int32, [1]> x_323_axes_0 = const()[name = string("x_323_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252945024)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252947136)))];
+            tensor<fp16, [1, 188, 1024]> x_323_cast_fp16 = layer_norm(axes = x_323_axes_0, beta = encoder_module_layers_13_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_conv_weight_to_fp16, x = input_723_cast_fp16)[name = string("x_323_cast_fp16")];
+            tensor<int32, [3]> input_725_perm_0 = const()[name = string("input_725_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_727_pad_type_0 = const()[name = string("input_727_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_727_strides_0 = const()[name = string("input_727_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_727_pad_0 = const()[name = string("input_727_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_727_dilations_0 = const()[name = string("input_727_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_727_groups_0 = const()[name = string("input_727_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_13_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252949248))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254522176))))[name = string("encoder_module_layers_13_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254522368)))];
+            tensor<fp16, [1, 1024, 188]> input_725_cast_fp16 = transpose(perm = input_725_perm_0, x = x_323_cast_fp16)[name = string("transpose_216")];
+            tensor<fp16, [1, 2048, 188]> input_727_cast_fp16 = conv(bias = encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_13_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_725_cast_fp16)[name = string("input_727_cast_fp16")];
+            int32 x_325_split_num_splits_0 = const()[name = string("x_325_split_num_splits_0"), val = int32(2)];
+            int32 x_325_split_axis_0 = const()[name = string("x_325_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_325_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_325_split_cast_fp16_1 = split(axis = x_325_split_axis_0, num_splits = x_325_split_num_splits_0, x = input_727_cast_fp16)[name = string("x_325_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_325_split_1_sigmoid_cast_fp16 = sigmoid(x = x_325_split_cast_fp16_1)[name = string("x_325_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_325_cast_fp16 = mul(x = x_325_split_cast_fp16_0, y = x_325_split_1_sigmoid_cast_fp16)[name = string("x_325_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_729_cast_fp16 = select(a = var_164_to_fp16, b = x_325_cast_fp16, cond = var_608)[name = string("input_729_cast_fp16")];
+            tensor<int32, [6]> input_731_pad_0 = const()[name = string("input_731_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_731_mode_0 = const()[name = string("input_731_mode_0"), val = string("constant")];
+            fp16 const_221_to_fp16 = const()[name = string("const_221_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_731_cast_fp16 = pad(constant_val = const_221_to_fp16, mode = input_731_mode_0, pad = input_731_pad_0, x = input_729_cast_fp16)[name = string("input_731_cast_fp16")];
+            string input_733_pad_type_0 = const()[name = string("input_733_pad_type_0"), val = string("valid")];
+            int32 input_733_groups_0 = const()[name = string("input_733_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_733_strides_0 = const()[name = string("input_733_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_733_pad_0 = const()[name = string("input_733_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_733_dilations_0 = const()[name = string("input_733_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_348_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254526528))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254533504))))[name = string("const_348_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_349_to_fp16 = const()[name = string("const_349_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254533696)))];
+            tensor<fp16, [1, 1024, 188]> input_735_cast_fp16 = conv(bias = const_349_to_fp16, dilations = input_733_dilations_0, groups = input_733_groups_0, pad = input_733_pad_0, pad_type = input_733_pad_type_0, strides = input_733_strides_0, weight = const_348_to_fp16_palettized, x = input_731_cast_fp16)[name = string("input_735_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_737_cast_fp16 = silu(x = input_735_cast_fp16)[name = string("input_737_cast_fp16")];
+            string x_327_pad_type_0 = const()[name = string("x_327_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_327_strides_0 = const()[name = string("x_327_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_327_pad_0 = const()[name = string("x_327_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_327_dilations_0 = const()[name = string("x_327_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_327_groups_0 = const()[name = string("x_327_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254535808))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255322304))))[name = string("encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255322496)))];
+            tensor<fp16, [1, 1024, 188]> x_327_cast_fp16 = conv(bias = encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16, dilations = x_327_dilations_0, groups = x_327_groups_0, pad = x_327_pad_0, pad_type = x_327_pad_type_0, strides = x_327_strides_0, weight = encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_737_cast_fp16)[name = string("x_327_cast_fp16")];
+            tensor<int32, [3]> input_739_perm_0 = const()[name = string("input_739_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_739_cast_fp16 = transpose(perm = input_739_perm_0, x = x_327_cast_fp16)[name = string("transpose_215")];
+            tensor<fp16, [1, 188, 1024]> input_741_cast_fp16 = add(x = input_723_cast_fp16, y = input_739_cast_fp16)[name = string("input_741_cast_fp16")];
+            tensor<int32, [1]> input_743_axes_0 = const()[name = string("input_743_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255324608)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255326720)))];
+            tensor<fp16, [1, 188, 1024]> input_743_cast_fp16 = layer_norm(axes = input_743_axes_0, beta = encoder_module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_741_cast_fp16)[name = string("input_743_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(255328832))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258474624))))[name = string("encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258474816)))];
+            tensor<fp16, [1, 188, 4096]> linear_125_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16_palettized, x = input_743_cast_fp16)[name = string("linear_125_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_747_cast_fp16 = silu(x = linear_125_cast_fp16)[name = string("input_747_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258483072))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261628864))))[name = string("encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261629056)))];
+            tensor<fp16, [1, 188, 1024]> linear_126_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16_palettized, x = input_747_cast_fp16)[name = string("linear_126_cast_fp16")];
+            fp16 var_2912_to_fp16 = const()[name = string("op_2912_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2913_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2912_to_fp16)[name = string("op_2913_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_753_cast_fp16 = add(x = input_741_cast_fp16, y = var_2913_cast_fp16)[name = string("input_753_cast_fp16")];
+            tensor<int32, [1]> input_755_axes_0 = const()[name = string("input_755_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261631168)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261633280)))];
+            tensor<fp16, [1, 188, 1024]> input_755_cast_fp16 = layer_norm(axes = input_755_axes_0, beta = encoder_module_layers_13_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_out_weight_to_fp16, x = input_753_cast_fp16)[name = string("input_755_cast_fp16")];
+            tensor<int32, [1]> input_757_axes_0 = const()[name = string("input_757_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261635392)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261637504)))];
+            tensor<fp16, [1, 188, 1024]> input_757_cast_fp16 = layer_norm(axes = input_757_axes_0, beta = encoder_module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_755_cast_fp16)[name = string("input_757_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261639616))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(264785408))))[name = string("encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(264785600)))];
+            tensor<fp16, [1, 188, 4096]> linear_127_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16_palettized, x = input_757_cast_fp16)[name = string("linear_127_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_761_cast_fp16 = silu(x = linear_127_cast_fp16)[name = string("input_761_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(264793856))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267939648))))[name = string("encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267939840)))];
+            tensor<fp16, [1, 188, 1024]> linear_128_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16_palettized, x = input_761_cast_fp16)[name = string("linear_128_cast_fp16")];
+            fp16 var_2943_to_fp16 = const()[name = string("op_2943_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2944_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2943_to_fp16)[name = string("op_2944_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_767_cast_fp16 = add(x = input_755_cast_fp16, y = var_2944_cast_fp16)[name = string("input_767_cast_fp16")];
+            tensor<int32, [1]> query_29_axes_0 = const()[name = string("query_29_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267941952)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267944064)))];
+            tensor<fp16, [1, 188, 1024]> query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = encoder_module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_self_att_weight_to_fp16, x = input_767_cast_fp16)[name = string("query_29_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(267946176))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268732672))))[name = string("encoder_module_layers_14_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268732864)))];
+            tensor<fp16, [1, 188, 1024]> linear_129_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_q_weight_to_fp16_palettized, x = query_29_cast_fp16)[name = string("linear_129_cast_fp16")];
+            tensor<int32, [4]> var_2961 = const()[name = string("op_2961"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_85_cast_fp16 = reshape(shape = var_2961, x = linear_129_cast_fp16)[name = string("q_85_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268734976))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269521472))))[name = string("encoder_module_layers_14_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269521664)))];
+            tensor<fp16, [1, 188, 1024]> linear_130_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_k_weight_to_fp16_palettized, x = query_29_cast_fp16)[name = string("linear_130_cast_fp16")];
+            tensor<int32, [4]> var_2966 = const()[name = string("op_2966"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_57_cast_fp16 = reshape(shape = var_2966, x = linear_130_cast_fp16)[name = string("k_57_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(269523776))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270310272))))[name = string("encoder_module_layers_14_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270310464)))];
+            tensor<fp16, [1, 188, 1024]> linear_131_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_v_weight_to_fp16_palettized, x = query_29_cast_fp16)[name = string("linear_131_cast_fp16")];
+            tensor<int32, [4]> var_2971 = const()[name = string("op_2971"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_29_cast_fp16 = reshape(shape = var_2971, x = linear_131_cast_fp16)[name = string("v_29_cast_fp16")];
+            tensor<int32, [4]> value_33_perm_0 = const()[name = string("value_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270312576)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2983_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_module_layers_14_self_attn_pos_bias_u_to_fp16)[name = string("op_2983_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270314688)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2985_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_module_layers_14_self_attn_pos_bias_v_to_fp16)[name = string("op_2985_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_29_perm_0 = const()[name = string("q_with_bias_v_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_335_transpose_x_0 = const()[name = string("x_335_transpose_x_0"), val = bool(false)];
+            bool x_335_transpose_y_0 = const()[name = string("x_335_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2987_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270316800))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270604864))))[name = string("op_2987_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2985_cast_fp16)[name = string("transpose_214")];
+            tensor<fp16, [1, 8, 188, 375]> x_335_cast_fp16 = matmul(transpose_x = x_335_transpose_x_0, transpose_y = x_335_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = op_2987_to_fp16_palettized)[name = string("x_335_cast_fp16")];
+            tensor<int32, [8]> x_337_pad_0 = const()[name = string("x_337_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_337_mode_0 = const()[name = string("x_337_mode_0"), val = string("constant")];
+            fp16 const_228_to_fp16 = const()[name = string("const_228_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_337_cast_fp16 = pad(constant_val = const_228_to_fp16, mode = x_337_mode_0, pad = x_337_pad_0, x = x_335_cast_fp16)[name = string("x_337_cast_fp16")];
+            tensor<int32, [4]> var_2995 = const()[name = string("op_2995"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_339_cast_fp16 = reshape(shape = var_2995, x = x_337_cast_fp16)[name = string("x_339_cast_fp16")];
+            tensor<int32, [4]> var_2999_begin_0 = const()[name = string("op_2999_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2999_end_0 = const()[name = string("op_2999_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2999_end_mask_0 = const()[name = string("op_2999_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2999_cast_fp16 = slice_by_index(begin = var_2999_begin_0, end = var_2999_end_0, end_mask = var_2999_end_mask_0, x = x_339_cast_fp16)[name = string("op_2999_cast_fp16")];
+            tensor<int32, [4]> var_3000 = const()[name = string("op_3000"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_57_cast_fp16 = reshape(shape = var_3000, x = var_2999_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<int32, [4]> transpose_124_perm_0 = const()[name = string("transpose_124_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_125_perm_0 = const()[name = string("transpose_125_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = string("transpose_212")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_124 = transpose(perm = transpose_124_perm_0, x = var_2983_cast_fp16)[name = string("transpose_213")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_59_begin_0 = const()[name = string("matrix_bd_59_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_59_end_0 = const()[name = string("matrix_bd_59_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_59_end_mask_0 = const()[name = string("matrix_bd_59_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3009_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = string("op_3009_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<fp16, [1, 8, 188, 188]> _inversed_scores_57_cast_fp16 = mul(x = var_3009_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = string("_inversed_scores_57_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_59_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_15)[name = string("scores_59_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3015_cast_fp16 = softmax(axis = var_152, x = scores_59_cast_fp16)[name = string("op_3015_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_769_cast_fp16 = select(a = var_164_to_fp16, b = var_3015_cast_fp16, cond = mask_15)[name = string("input_769_cast_fp16")];
+            bool x_341_transpose_x_0 = const()[name = string("x_341_transpose_x_0"), val = bool(false)];
+            bool x_341_transpose_y_0 = const()[name = string("x_341_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_29_cast_fp16)[name = string("transpose_211")];
+            tensor<fp16, [1, 8, 188, 128]> x_341_cast_fp16 = matmul(transpose_x = x_341_transpose_x_0, transpose_y = x_341_transpose_y_0, x = input_769_cast_fp16, y = value_33_cast_fp16)[name = string("x_341_cast_fp16")];
+            tensor<int32, [4]> var_3019_perm_0 = const()[name = string("op_3019_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3020 = const()[name = string("op_3020"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3019_cast_fp16 = transpose(perm = var_3019_perm_0, x = x_341_cast_fp16)[name = string("transpose_210")];
+            tensor<fp16, [1, 188, 1024]> input_771_cast_fp16 = reshape(shape = var_3020, x = var_3019_cast_fp16)[name = string("input_771_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270605056))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271391552))))[name = string("encoder_module_layers_14_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271391744)))];
+            tensor<fp16, [1, 188, 1024]> linear_133_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_out_weight_to_fp16_palettized, x = input_771_cast_fp16)[name = string("linear_133_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_775_cast_fp16 = add(x = input_767_cast_fp16, y = linear_133_cast_fp16)[name = string("input_775_cast_fp16")];
+            tensor<int32, [1]> x_345_axes_0 = const()[name = string("x_345_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271393856)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271395968)))];
+            tensor<fp16, [1, 188, 1024]> x_345_cast_fp16 = layer_norm(axes = x_345_axes_0, beta = encoder_module_layers_14_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_conv_weight_to_fp16, x = input_775_cast_fp16)[name = string("x_345_cast_fp16")];
+            tensor<int32, [3]> input_777_perm_0 = const()[name = string("input_777_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_779_pad_type_0 = const()[name = string("input_779_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_779_strides_0 = const()[name = string("input_779_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_779_pad_0 = const()[name = string("input_779_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_779_dilations_0 = const()[name = string("input_779_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_779_groups_0 = const()[name = string("input_779_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_14_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271398080))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272971008))))[name = string("encoder_module_layers_14_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272971200)))];
+            tensor<fp16, [1, 1024, 188]> input_777_cast_fp16 = transpose(perm = input_777_perm_0, x = x_345_cast_fp16)[name = string("transpose_209")];
+            tensor<fp16, [1, 2048, 188]> input_779_cast_fp16 = conv(bias = encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_14_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_777_cast_fp16)[name = string("input_779_cast_fp16")];
+            int32 x_347_split_num_splits_0 = const()[name = string("x_347_split_num_splits_0"), val = int32(2)];
+            int32 x_347_split_axis_0 = const()[name = string("x_347_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_347_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_347_split_cast_fp16_1 = split(axis = x_347_split_axis_0, num_splits = x_347_split_num_splits_0, x = input_779_cast_fp16)[name = string("x_347_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_347_split_1_sigmoid_cast_fp16 = sigmoid(x = x_347_split_cast_fp16_1)[name = string("x_347_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_347_cast_fp16 = mul(x = x_347_split_cast_fp16_0, y = x_347_split_1_sigmoid_cast_fp16)[name = string("x_347_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_781_cast_fp16 = select(a = var_164_to_fp16, b = x_347_cast_fp16, cond = var_608)[name = string("input_781_cast_fp16")];
+            tensor<int32, [6]> input_783_pad_0 = const()[name = string("input_783_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_783_mode_0 = const()[name = string("input_783_mode_0"), val = string("constant")];
+            fp16 const_231_to_fp16 = const()[name = string("const_231_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_783_cast_fp16 = pad(constant_val = const_231_to_fp16, mode = input_783_mode_0, pad = input_783_pad_0, x = input_781_cast_fp16)[name = string("input_783_cast_fp16")];
+            string input_785_pad_type_0 = const()[name = string("input_785_pad_type_0"), val = string("valid")];
+            int32 input_785_groups_0 = const()[name = string("input_785_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_785_strides_0 = const()[name = string("input_785_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_785_pad_0 = const()[name = string("input_785_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_785_dilations_0 = const()[name = string("input_785_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_350_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272975360))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272982336))))[name = string("const_350_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_351_to_fp16 = const()[name = string("const_351_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272982528)))];
+            tensor<fp16, [1, 1024, 188]> input_787_cast_fp16 = conv(bias = const_351_to_fp16, dilations = input_785_dilations_0, groups = input_785_groups_0, pad = input_785_pad_0, pad_type = input_785_pad_type_0, strides = input_785_strides_0, weight = const_350_to_fp16_palettized, x = input_783_cast_fp16)[name = string("input_787_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_789_cast_fp16 = silu(x = input_787_cast_fp16)[name = string("input_789_cast_fp16")];
+            string x_349_pad_type_0 = const()[name = string("x_349_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_349_strides_0 = const()[name = string("x_349_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_349_pad_0 = const()[name = string("x_349_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_349_dilations_0 = const()[name = string("x_349_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_349_groups_0 = const()[name = string("x_349_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272984640))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273771136))))[name = string("encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273771328)))];
+            tensor<fp16, [1, 1024, 188]> x_349_cast_fp16 = conv(bias = encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16, dilations = x_349_dilations_0, groups = x_349_groups_0, pad = x_349_pad_0, pad_type = x_349_pad_type_0, strides = x_349_strides_0, weight = encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_789_cast_fp16)[name = string("x_349_cast_fp16")];
+            tensor<int32, [3]> input_791_perm_0 = const()[name = string("input_791_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_791_cast_fp16 = transpose(perm = input_791_perm_0, x = x_349_cast_fp16)[name = string("transpose_208")];
+            tensor<fp16, [1, 188, 1024]> input_793_cast_fp16 = add(x = input_775_cast_fp16, y = input_791_cast_fp16)[name = string("input_793_cast_fp16")];
+            tensor<int32, [1]> input_795_axes_0 = const()[name = string("input_795_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273773440)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273775552)))];
+            tensor<fp16, [1, 188, 1024]> input_795_cast_fp16 = layer_norm(axes = input_795_axes_0, beta = encoder_module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_793_cast_fp16)[name = string("input_795_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273777664))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276923456))))[name = string("encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276923648)))];
+            tensor<fp16, [1, 188, 4096]> linear_134_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16_palettized, x = input_795_cast_fp16)[name = string("linear_134_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_799_cast_fp16 = silu(x = linear_134_cast_fp16)[name = string("input_799_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(276931904))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280077696))))[name = string("encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280077888)))];
+            tensor<fp16, [1, 188, 1024]> linear_135_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16_palettized, x = input_799_cast_fp16)[name = string("linear_135_cast_fp16")];
+            fp16 var_3086_to_fp16 = const()[name = string("op_3086_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3087_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_3086_to_fp16)[name = string("op_3087_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_805_cast_fp16 = add(x = input_793_cast_fp16, y = var_3087_cast_fp16)[name = string("input_805_cast_fp16")];
+            tensor<int32, [1]> input_807_axes_0 = const()[name = string("input_807_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280080000)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280082112)))];
+            tensor<fp16, [1, 188, 1024]> input_807_cast_fp16 = layer_norm(axes = input_807_axes_0, beta = encoder_module_layers_14_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_out_weight_to_fp16, x = input_805_cast_fp16)[name = string("input_807_cast_fp16")];
+            tensor<int32, [1]> input_809_axes_0 = const()[name = string("input_809_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280084224)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280086336)))];
+            tensor<fp16, [1, 188, 1024]> input_809_cast_fp16 = layer_norm(axes = input_809_axes_0, beta = encoder_module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_807_cast_fp16)[name = string("input_809_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280088448))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283234240))))[name = string("encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283234432)))];
+            tensor<fp16, [1, 188, 4096]> linear_136_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16_palettized, x = input_809_cast_fp16)[name = string("linear_136_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_813_cast_fp16 = silu(x = linear_136_cast_fp16)[name = string("input_813_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283242688))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286388480))))[name = string("encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286388672)))];
+            tensor<fp16, [1, 188, 1024]> linear_137_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16_palettized, x = input_813_cast_fp16)[name = string("linear_137_cast_fp16")];
+            fp16 var_3117_to_fp16 = const()[name = string("op_3117_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3118_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_3117_to_fp16)[name = string("op_3118_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_819_cast_fp16 = add(x = input_807_cast_fp16, y = var_3118_cast_fp16)[name = string("input_819_cast_fp16")];
+            tensor<int32, [1]> query_31_axes_0 = const()[name = string("query_31_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286390784)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286392896)))];
+            tensor<fp16, [1, 188, 1024]> query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = encoder_module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_self_att_weight_to_fp16, x = input_819_cast_fp16)[name = string("query_31_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286395008))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287181504))))[name = string("encoder_module_layers_15_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287181696)))];
+            tensor<fp16, [1, 188, 1024]> linear_138_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_q_weight_to_fp16_palettized, x = query_31_cast_fp16)[name = string("linear_138_cast_fp16")];
+            tensor<int32, [4]> var_3135 = const()[name = string("op_3135"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_91_cast_fp16 = reshape(shape = var_3135, x = linear_138_cast_fp16)[name = string("q_91_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287183808))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287970304))))[name = string("encoder_module_layers_15_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287970496)))];
+            tensor<fp16, [1, 188, 1024]> linear_139_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_k_weight_to_fp16_palettized, x = query_31_cast_fp16)[name = string("linear_139_cast_fp16")];
+            tensor<int32, [4]> var_3140 = const()[name = string("op_3140"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_61_cast_fp16 = reshape(shape = var_3140, x = linear_139_cast_fp16)[name = string("k_61_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287972608))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288759104))))[name = string("encoder_module_layers_15_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288759296)))];
+            tensor<fp16, [1, 188, 1024]> linear_140_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_v_weight_to_fp16_palettized, x = query_31_cast_fp16)[name = string("linear_140_cast_fp16")];
+            tensor<int32, [4]> var_3145 = const()[name = string("op_3145"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_31_cast_fp16 = reshape(shape = var_3145, x = linear_140_cast_fp16)[name = string("v_31_cast_fp16")];
+            tensor<int32, [4]> value_35_perm_0 = const()[name = string("value_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288761408)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3157_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_module_layers_15_self_attn_pos_bias_u_to_fp16)[name = string("op_3157_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288763520)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3159_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_module_layers_15_self_attn_pos_bias_v_to_fp16)[name = string("op_3159_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_31_perm_0 = const()[name = string("q_with_bias_v_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_357_transpose_x_0 = const()[name = string("x_357_transpose_x_0"), val = bool(false)];
+            bool x_357_transpose_y_0 = const()[name = string("x_357_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3161_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288765632))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289053696))))[name = string("op_3161_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_3159_cast_fp16)[name = string("transpose_207")];
+            tensor<fp16, [1, 8, 188, 375]> x_357_cast_fp16 = matmul(transpose_x = x_357_transpose_x_0, transpose_y = x_357_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = op_3161_to_fp16_palettized)[name = string("x_357_cast_fp16")];
+            tensor<int32, [8]> x_359_pad_0 = const()[name = string("x_359_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_359_mode_0 = const()[name = string("x_359_mode_0"), val = string("constant")];
+            fp16 const_238_to_fp16 = const()[name = string("const_238_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_359_cast_fp16 = pad(constant_val = const_238_to_fp16, mode = x_359_mode_0, pad = x_359_pad_0, x = x_357_cast_fp16)[name = string("x_359_cast_fp16")];
+            tensor<int32, [4]> var_3169 = const()[name = string("op_3169"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_361_cast_fp16 = reshape(shape = var_3169, x = x_359_cast_fp16)[name = string("x_361_cast_fp16")];
+            tensor<int32, [4]> var_3173_begin_0 = const()[name = string("op_3173_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3173_end_0 = const()[name = string("op_3173_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3173_end_mask_0 = const()[name = string("op_3173_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3173_cast_fp16 = slice_by_index(begin = var_3173_begin_0, end = var_3173_end_0, end_mask = var_3173_end_mask_0, x = x_361_cast_fp16)[name = string("op_3173_cast_fp16")];
+            tensor<int32, [4]> var_3174 = const()[name = string("op_3174"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_61_cast_fp16 = reshape(shape = var_3174, x = var_3173_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<int32, [4]> transpose_126_perm_0 = const()[name = string("transpose_126_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_127_perm_0 = const()[name = string("transpose_127_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = string("transpose_205")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_126 = transpose(perm = transpose_126_perm_0, x = var_3157_cast_fp16)[name = string("transpose_206")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_63_begin_0 = const()[name = string("matrix_bd_63_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_63_end_0 = const()[name = string("matrix_bd_63_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_63_end_mask_0 = const()[name = string("matrix_bd_63_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3183_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = string("op_3183_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<fp16, [1, 8, 188, 188]> _inversed_scores_61_cast_fp16 = mul(x = var_3183_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = string("_inversed_scores_61_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_63_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_15)[name = string("scores_63_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3189_cast_fp16 = softmax(axis = var_152, x = scores_63_cast_fp16)[name = string("op_3189_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_821_cast_fp16 = select(a = var_164_to_fp16, b = var_3189_cast_fp16, cond = mask_15)[name = string("input_821_cast_fp16")];
+            bool x_363_transpose_x_0 = const()[name = string("x_363_transpose_x_0"), val = bool(false)];
+            bool x_363_transpose_y_0 = const()[name = string("x_363_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_31_cast_fp16)[name = string("transpose_204")];
+            tensor<fp16, [1, 8, 188, 128]> x_363_cast_fp16 = matmul(transpose_x = x_363_transpose_x_0, transpose_y = x_363_transpose_y_0, x = input_821_cast_fp16, y = value_35_cast_fp16)[name = string("x_363_cast_fp16")];
+            tensor<int32, [4]> var_3193_perm_0 = const()[name = string("op_3193_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3194 = const()[name = string("op_3194"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3193_cast_fp16 = transpose(perm = var_3193_perm_0, x = x_363_cast_fp16)[name = string("transpose_203")];
+            tensor<fp16, [1, 188, 1024]> input_823_cast_fp16 = reshape(shape = var_3194, x = var_3193_cast_fp16)[name = string("input_823_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289053888))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289840384))))[name = string("encoder_module_layers_15_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289840576)))];
+            tensor<fp16, [1, 188, 1024]> linear_142_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_out_weight_to_fp16_palettized, x = input_823_cast_fp16)[name = string("linear_142_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_827_cast_fp16 = add(x = input_819_cast_fp16, y = linear_142_cast_fp16)[name = string("input_827_cast_fp16")];
+            tensor<int32, [1]> x_367_axes_0 = const()[name = string("x_367_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289842688)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289844800)))];
+            tensor<fp16, [1, 188, 1024]> x_367_cast_fp16 = layer_norm(axes = x_367_axes_0, beta = encoder_module_layers_15_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_conv_weight_to_fp16, x = input_827_cast_fp16)[name = string("x_367_cast_fp16")];
+            tensor<int32, [3]> input_829_perm_0 = const()[name = string("input_829_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_831_pad_type_0 = const()[name = string("input_831_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_831_strides_0 = const()[name = string("input_831_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_831_pad_0 = const()[name = string("input_831_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_831_dilations_0 = const()[name = string("input_831_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_831_groups_0 = const()[name = string("input_831_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_15_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289846912))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291419840))))[name = string("encoder_module_layers_15_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291420032)))];
+            tensor<fp16, [1, 1024, 188]> input_829_cast_fp16 = transpose(perm = input_829_perm_0, x = x_367_cast_fp16)[name = string("transpose_202")];
+            tensor<fp16, [1, 2048, 188]> input_831_cast_fp16 = conv(bias = encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_15_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_829_cast_fp16)[name = string("input_831_cast_fp16")];
+            int32 x_369_split_num_splits_0 = const()[name = string("x_369_split_num_splits_0"), val = int32(2)];
+            int32 x_369_split_axis_0 = const()[name = string("x_369_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_369_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_369_split_cast_fp16_1 = split(axis = x_369_split_axis_0, num_splits = x_369_split_num_splits_0, x = input_831_cast_fp16)[name = string("x_369_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_369_split_1_sigmoid_cast_fp16 = sigmoid(x = x_369_split_cast_fp16_1)[name = string("x_369_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_369_cast_fp16 = mul(x = x_369_split_cast_fp16_0, y = x_369_split_1_sigmoid_cast_fp16)[name = string("x_369_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_833_cast_fp16 = select(a = var_164_to_fp16, b = x_369_cast_fp16, cond = var_608)[name = string("input_833_cast_fp16")];
+            tensor<int32, [6]> input_835_pad_0 = const()[name = string("input_835_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_835_mode_0 = const()[name = string("input_835_mode_0"), val = string("constant")];
+            fp16 const_241_to_fp16 = const()[name = string("const_241_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_835_cast_fp16 = pad(constant_val = const_241_to_fp16, mode = input_835_mode_0, pad = input_835_pad_0, x = input_833_cast_fp16)[name = string("input_835_cast_fp16")];
+            string input_837_pad_type_0 = const()[name = string("input_837_pad_type_0"), val = string("valid")];
+            int32 input_837_groups_0 = const()[name = string("input_837_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_837_strides_0 = const()[name = string("input_837_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_837_pad_0 = const()[name = string("input_837_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_837_dilations_0 = const()[name = string("input_837_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_352_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291424192))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291431168))))[name = string("const_352_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_353_to_fp16 = const()[name = string("const_353_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291431360)))];
+            tensor<fp16, [1, 1024, 188]> input_839_cast_fp16 = conv(bias = const_353_to_fp16, dilations = input_837_dilations_0, groups = input_837_groups_0, pad = input_837_pad_0, pad_type = input_837_pad_type_0, strides = input_837_strides_0, weight = const_352_to_fp16_palettized, x = input_835_cast_fp16)[name = string("input_839_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_841_cast_fp16 = silu(x = input_839_cast_fp16)[name = string("input_841_cast_fp16")];
+            string x_371_pad_type_0 = const()[name = string("x_371_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_371_strides_0 = const()[name = string("x_371_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_371_pad_0 = const()[name = string("x_371_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_371_dilations_0 = const()[name = string("x_371_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_371_groups_0 = const()[name = string("x_371_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291433472))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292219968))))[name = string("encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292220160)))];
+            tensor<fp16, [1, 1024, 188]> x_371_cast_fp16 = conv(bias = encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16, dilations = x_371_dilations_0, groups = x_371_groups_0, pad = x_371_pad_0, pad_type = x_371_pad_type_0, strides = x_371_strides_0, weight = encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_841_cast_fp16)[name = string("x_371_cast_fp16")];
+            tensor<int32, [3]> input_843_perm_0 = const()[name = string("input_843_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_843_cast_fp16 = transpose(perm = input_843_perm_0, x = x_371_cast_fp16)[name = string("transpose_201")];
+            tensor<fp16, [1, 188, 1024]> input_845_cast_fp16 = add(x = input_827_cast_fp16, y = input_843_cast_fp16)[name = string("input_845_cast_fp16")];
+            tensor<int32, [1]> input_847_axes_0 = const()[name = string("input_847_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292222272)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292224384)))];
+            tensor<fp16, [1, 188, 1024]> input_847_cast_fp16 = layer_norm(axes = input_847_axes_0, beta = encoder_module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_845_cast_fp16)[name = string("input_847_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292226496))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295372288))))[name = string("encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295372480)))];
+            tensor<fp16, [1, 188, 4096]> linear_143_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16_palettized, x = input_847_cast_fp16)[name = string("linear_143_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_851_cast_fp16 = silu(x = linear_143_cast_fp16)[name = string("input_851_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295380736))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298526528))))[name = string("encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298526720)))];
+            tensor<fp16, [1, 188, 1024]> linear_144_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16_palettized, x = input_851_cast_fp16)[name = string("linear_144_cast_fp16")];
+            fp16 var_3260_to_fp16 = const()[name = string("op_3260_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3261_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_3260_to_fp16)[name = string("op_3261_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_857_cast_fp16 = add(x = input_845_cast_fp16, y = var_3261_cast_fp16)[name = string("input_857_cast_fp16")];
+            tensor<int32, [1]> input_859_axes_0 = const()[name = string("input_859_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298528832)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298530944)))];
+            tensor<fp16, [1, 188, 1024]> input_859_cast_fp16 = layer_norm(axes = input_859_axes_0, beta = encoder_module_layers_15_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_out_weight_to_fp16, x = input_857_cast_fp16)[name = string("input_859_cast_fp16")];
+            tensor<int32, [1]> input_861_axes_0 = const()[name = string("input_861_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298533056)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298535168)))];
+            tensor<fp16, [1, 188, 1024]> input_861_cast_fp16 = layer_norm(axes = input_861_axes_0, beta = encoder_module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_859_cast_fp16)[name = string("input_861_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298537280))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301683072))))[name = string("encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301683264)))];
+            tensor<fp16, [1, 188, 4096]> linear_145_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16_palettized, x = input_861_cast_fp16)[name = string("linear_145_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_865_cast_fp16 = silu(x = linear_145_cast_fp16)[name = string("input_865_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301691520))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304837312))))[name = string("encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304837504)))];
+            tensor<fp16, [1, 188, 1024]> linear_146_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16_palettized, x = input_865_cast_fp16)[name = string("linear_146_cast_fp16")];
+            fp16 var_3291_to_fp16 = const()[name = string("op_3291_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3292_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3291_to_fp16)[name = string("op_3292_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_871_cast_fp16 = add(x = input_859_cast_fp16, y = var_3292_cast_fp16)[name = string("input_871_cast_fp16")];
+            tensor<int32, [1]> query_33_axes_0 = const()[name = string("query_33_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304839616)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304841728)))];
+            tensor<fp16, [1, 188, 1024]> query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = encoder_module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_self_att_weight_to_fp16, x = input_871_cast_fp16)[name = string("query_33_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304843840))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305630336))))[name = string("encoder_module_layers_16_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305630528)))];
+            tensor<fp16, [1, 188, 1024]> linear_147_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_q_weight_to_fp16_palettized, x = query_33_cast_fp16)[name = string("linear_147_cast_fp16")];
+            tensor<int32, [4]> var_3309 = const()[name = string("op_3309"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_97_cast_fp16 = reshape(shape = var_3309, x = linear_147_cast_fp16)[name = string("q_97_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305632640))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306419136))))[name = string("encoder_module_layers_16_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306419328)))];
+            tensor<fp16, [1, 188, 1024]> linear_148_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_k_weight_to_fp16_palettized, x = query_33_cast_fp16)[name = string("linear_148_cast_fp16")];
+            tensor<int32, [4]> var_3314 = const()[name = string("op_3314"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_65_cast_fp16 = reshape(shape = var_3314, x = linear_148_cast_fp16)[name = string("k_65_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306421440))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307207936))))[name = string("encoder_module_layers_16_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307208128)))];
+            tensor<fp16, [1, 188, 1024]> linear_149_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_v_weight_to_fp16_palettized, x = query_33_cast_fp16)[name = string("linear_149_cast_fp16")];
+            tensor<int32, [4]> var_3319 = const()[name = string("op_3319"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_33_cast_fp16 = reshape(shape = var_3319, x = linear_149_cast_fp16)[name = string("v_33_cast_fp16")];
+            tensor<int32, [4]> value_37_perm_0 = const()[name = string("value_37_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307210240)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3331_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_module_layers_16_self_attn_pos_bias_u_to_fp16)[name = string("op_3331_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307212352)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3333_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_module_layers_16_self_attn_pos_bias_v_to_fp16)[name = string("op_3333_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_33_perm_0 = const()[name = string("q_with_bias_v_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_379_transpose_x_0 = const()[name = string("x_379_transpose_x_0"), val = bool(false)];
+            bool x_379_transpose_y_0 = const()[name = string("x_379_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3335_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307214464))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307502528))))[name = string("op_3335_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_3333_cast_fp16)[name = string("transpose_200")];
+            tensor<fp16, [1, 8, 188, 375]> x_379_cast_fp16 = matmul(transpose_x = x_379_transpose_x_0, transpose_y = x_379_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = op_3335_to_fp16_palettized)[name = string("x_379_cast_fp16")];
+            tensor<int32, [8]> x_381_pad_0 = const()[name = string("x_381_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_381_mode_0 = const()[name = string("x_381_mode_0"), val = string("constant")];
+            fp16 const_248_to_fp16 = const()[name = string("const_248_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_381_cast_fp16 = pad(constant_val = const_248_to_fp16, mode = x_381_mode_0, pad = x_381_pad_0, x = x_379_cast_fp16)[name = string("x_381_cast_fp16")];
+            tensor<int32, [4]> var_3343 = const()[name = string("op_3343"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_383_cast_fp16 = reshape(shape = var_3343, x = x_381_cast_fp16)[name = string("x_383_cast_fp16")];
+            tensor<int32, [4]> var_3347_begin_0 = const()[name = string("op_3347_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3347_end_0 = const()[name = string("op_3347_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3347_end_mask_0 = const()[name = string("op_3347_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3347_cast_fp16 = slice_by_index(begin = var_3347_begin_0, end = var_3347_end_0, end_mask = var_3347_end_mask_0, x = x_383_cast_fp16)[name = string("op_3347_cast_fp16")];
+            tensor<int32, [4]> var_3348 = const()[name = string("op_3348"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_65_cast_fp16 = reshape(shape = var_3348, x = var_3347_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<int32, [4]> transpose_128_perm_0 = const()[name = string("transpose_128_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_129_perm_0 = const()[name = string("transpose_129_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = string("transpose_198")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_128 = transpose(perm = transpose_128_perm_0, x = var_3331_cast_fp16)[name = string("transpose_199")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_67_begin_0 = const()[name = string("matrix_bd_67_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_67_end_0 = const()[name = string("matrix_bd_67_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_67_end_mask_0 = const()[name = string("matrix_bd_67_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3357_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = string("op_3357_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<fp16, [1, 8, 188, 188]> _inversed_scores_65_cast_fp16 = mul(x = var_3357_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = string("_inversed_scores_65_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_67_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_15)[name = string("scores_67_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3363_cast_fp16 = softmax(axis = var_152, x = scores_67_cast_fp16)[name = string("op_3363_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_873_cast_fp16 = select(a = var_164_to_fp16, b = var_3363_cast_fp16, cond = mask_15)[name = string("input_873_cast_fp16")];
+            bool x_385_transpose_x_0 = const()[name = string("x_385_transpose_x_0"), val = bool(false)];
+            bool x_385_transpose_y_0 = const()[name = string("x_385_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_33_cast_fp16)[name = string("transpose_197")];
+            tensor<fp16, [1, 8, 188, 128]> x_385_cast_fp16 = matmul(transpose_x = x_385_transpose_x_0, transpose_y = x_385_transpose_y_0, x = input_873_cast_fp16, y = value_37_cast_fp16)[name = string("x_385_cast_fp16")];
+            tensor<int32, [4]> var_3367_perm_0 = const()[name = string("op_3367_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3368 = const()[name = string("op_3368"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3367_cast_fp16 = transpose(perm = var_3367_perm_0, x = x_385_cast_fp16)[name = string("transpose_196")];
+            tensor<fp16, [1, 188, 1024]> input_875_cast_fp16 = reshape(shape = var_3368, x = var_3367_cast_fp16)[name = string("input_875_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307502720))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308289216))))[name = string("encoder_module_layers_16_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308289408)))];
+            tensor<fp16, [1, 188, 1024]> linear_151_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_out_weight_to_fp16_palettized, x = input_875_cast_fp16)[name = string("linear_151_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_879_cast_fp16 = add(x = input_871_cast_fp16, y = linear_151_cast_fp16)[name = string("input_879_cast_fp16")];
+            tensor<int32, [1]> x_389_axes_0 = const()[name = string("x_389_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308291520)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308293632)))];
+            tensor<fp16, [1, 188, 1024]> x_389_cast_fp16 = layer_norm(axes = x_389_axes_0, beta = encoder_module_layers_16_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_conv_weight_to_fp16, x = input_879_cast_fp16)[name = string("x_389_cast_fp16")];
+            tensor<int32, [3]> input_881_perm_0 = const()[name = string("input_881_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_883_pad_type_0 = const()[name = string("input_883_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_883_strides_0 = const()[name = string("input_883_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_883_pad_0 = const()[name = string("input_883_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_883_dilations_0 = const()[name = string("input_883_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_883_groups_0 = const()[name = string("input_883_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_16_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308295744))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309868672))))[name = string("encoder_module_layers_16_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309868864)))];
+            tensor<fp16, [1, 1024, 188]> input_881_cast_fp16 = transpose(perm = input_881_perm_0, x = x_389_cast_fp16)[name = string("transpose_195")];
+            tensor<fp16, [1, 2048, 188]> input_883_cast_fp16 = conv(bias = encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_16_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_881_cast_fp16)[name = string("input_883_cast_fp16")];
+            int32 x_391_split_num_splits_0 = const()[name = string("x_391_split_num_splits_0"), val = int32(2)];
+            int32 x_391_split_axis_0 = const()[name = string("x_391_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_391_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_391_split_cast_fp16_1 = split(axis = x_391_split_axis_0, num_splits = x_391_split_num_splits_0, x = input_883_cast_fp16)[name = string("x_391_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_391_split_1_sigmoid_cast_fp16 = sigmoid(x = x_391_split_cast_fp16_1)[name = string("x_391_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_391_cast_fp16 = mul(x = x_391_split_cast_fp16_0, y = x_391_split_1_sigmoid_cast_fp16)[name = string("x_391_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_885_cast_fp16 = select(a = var_164_to_fp16, b = x_391_cast_fp16, cond = var_608)[name = string("input_885_cast_fp16")];
+            tensor<int32, [6]> input_887_pad_0 = const()[name = string("input_887_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_887_mode_0 = const()[name = string("input_887_mode_0"), val = string("constant")];
+            fp16 const_251_to_fp16 = const()[name = string("const_251_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_887_cast_fp16 = pad(constant_val = const_251_to_fp16, mode = input_887_mode_0, pad = input_887_pad_0, x = input_885_cast_fp16)[name = string("input_887_cast_fp16")];
+            string input_889_pad_type_0 = const()[name = string("input_889_pad_type_0"), val = string("valid")];
+            int32 input_889_groups_0 = const()[name = string("input_889_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_889_strides_0 = const()[name = string("input_889_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_889_pad_0 = const()[name = string("input_889_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_889_dilations_0 = const()[name = string("input_889_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_354_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309873024))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309880000))))[name = string("const_354_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_355_to_fp16 = const()[name = string("const_355_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309880192)))];
+            tensor<fp16, [1, 1024, 188]> input_891_cast_fp16 = conv(bias = const_355_to_fp16, dilations = input_889_dilations_0, groups = input_889_groups_0, pad = input_889_pad_0, pad_type = input_889_pad_type_0, strides = input_889_strides_0, weight = const_354_to_fp16_palettized, x = input_887_cast_fp16)[name = string("input_891_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_893_cast_fp16 = silu(x = input_891_cast_fp16)[name = string("input_893_cast_fp16")];
+            string x_393_pad_type_0 = const()[name = string("x_393_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_393_strides_0 = const()[name = string("x_393_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_393_pad_0 = const()[name = string("x_393_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_393_dilations_0 = const()[name = string("x_393_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_393_groups_0 = const()[name = string("x_393_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309882304))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310668800))))[name = string("encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310668992)))];
+            tensor<fp16, [1, 1024, 188]> x_393_cast_fp16 = conv(bias = encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16, dilations = x_393_dilations_0, groups = x_393_groups_0, pad = x_393_pad_0, pad_type = x_393_pad_type_0, strides = x_393_strides_0, weight = encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_893_cast_fp16)[name = string("x_393_cast_fp16")];
+            tensor<int32, [3]> input_895_perm_0 = const()[name = string("input_895_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_895_cast_fp16 = transpose(perm = input_895_perm_0, x = x_393_cast_fp16)[name = string("transpose_194")];
+            tensor<fp16, [1, 188, 1024]> input_897_cast_fp16 = add(x = input_879_cast_fp16, y = input_895_cast_fp16)[name = string("input_897_cast_fp16")];
+            tensor<int32, [1]> input_899_axes_0 = const()[name = string("input_899_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310671104)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310673216)))];
+            tensor<fp16, [1, 188, 1024]> input_899_cast_fp16 = layer_norm(axes = input_899_axes_0, beta = encoder_module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_897_cast_fp16)[name = string("input_899_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310675328))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313821120))))[name = string("encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313821312)))];
+            tensor<fp16, [1, 188, 4096]> linear_152_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16_palettized, x = input_899_cast_fp16)[name = string("linear_152_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_903_cast_fp16 = silu(x = linear_152_cast_fp16)[name = string("input_903_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313829568))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316975360))))[name = string("encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316975552)))];
+            tensor<fp16, [1, 188, 1024]> linear_153_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16_palettized, x = input_903_cast_fp16)[name = string("linear_153_cast_fp16")];
+            fp16 var_3434_to_fp16 = const()[name = string("op_3434_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3435_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_3434_to_fp16)[name = string("op_3435_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_909_cast_fp16 = add(x = input_897_cast_fp16, y = var_3435_cast_fp16)[name = string("input_909_cast_fp16")];
+            tensor<int32, [1]> input_911_axes_0 = const()[name = string("input_911_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316977664)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316979776)))];
+            tensor<fp16, [1, 188, 1024]> input_911_cast_fp16 = layer_norm(axes = input_911_axes_0, beta = encoder_module_layers_16_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_out_weight_to_fp16, x = input_909_cast_fp16)[name = string("input_911_cast_fp16")];
+            tensor<int32, [1]> input_913_axes_0 = const()[name = string("input_913_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316981888)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316984000)))];
+            tensor<fp16, [1, 188, 1024]> input_913_cast_fp16 = layer_norm(axes = input_913_axes_0, beta = encoder_module_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_feed_forward1_weight_to_fp16, x = input_911_cast_fp16)[name = string("input_913_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316986112))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320131904))))[name = string("encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320132096)))];
+            tensor<fp16, [1, 188, 4096]> linear_154_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16_palettized, x = input_913_cast_fp16)[name = string("linear_154_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_917_cast_fp16 = silu(x = linear_154_cast_fp16)[name = string("input_917_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320140352))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323286144))))[name = string("encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323286336)))];
+            tensor<fp16, [1, 188, 1024]> linear_155_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16_palettized, x = input_917_cast_fp16)[name = string("linear_155_cast_fp16")];
+            fp16 var_3465_to_fp16 = const()[name = string("op_3465_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3466_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_3465_to_fp16)[name = string("op_3466_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_923_cast_fp16 = add(x = input_911_cast_fp16, y = var_3466_cast_fp16)[name = string("input_923_cast_fp16")];
+            tensor<int32, [1]> query_35_axes_0 = const()[name = string("query_35_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323288448)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323290560)))];
+            tensor<fp16, [1, 188, 1024]> query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = encoder_module_layers_17_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_self_att_weight_to_fp16, x = input_923_cast_fp16)[name = string("query_35_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323292672))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324079168))))[name = string("encoder_module_layers_17_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324079360)))];
+            tensor<fp16, [1, 188, 1024]> linear_156_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_q_weight_to_fp16_palettized, x = query_35_cast_fp16)[name = string("linear_156_cast_fp16")];
+            tensor<int32, [4]> var_3483 = const()[name = string("op_3483"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_103_cast_fp16 = reshape(shape = var_3483, x = linear_156_cast_fp16)[name = string("q_103_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324081472))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324867968))))[name = string("encoder_module_layers_17_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324868160)))];
+            tensor<fp16, [1, 188, 1024]> linear_157_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_k_weight_to_fp16_palettized, x = query_35_cast_fp16)[name = string("linear_157_cast_fp16")];
+            tensor<int32, [4]> var_3488 = const()[name = string("op_3488"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_69_cast_fp16 = reshape(shape = var_3488, x = linear_157_cast_fp16)[name = string("k_69_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324870272))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325656768))))[name = string("encoder_module_layers_17_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325656960)))];
+            tensor<fp16, [1, 188, 1024]> linear_158_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_v_weight_to_fp16_palettized, x = query_35_cast_fp16)[name = string("linear_158_cast_fp16")];
+            tensor<int32, [4]> var_3493 = const()[name = string("op_3493"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_35_cast_fp16 = reshape(shape = var_3493, x = linear_158_cast_fp16)[name = string("v_35_cast_fp16")];
+            tensor<int32, [4]> value_39_perm_0 = const()[name = string("value_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325659072)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3505_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_module_layers_17_self_attn_pos_bias_u_to_fp16)[name = string("op_3505_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325661184)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3507_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_module_layers_17_self_attn_pos_bias_v_to_fp16)[name = string("op_3507_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_35_perm_0 = const()[name = string("q_with_bias_v_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_401_transpose_x_0 = const()[name = string("x_401_transpose_x_0"), val = bool(false)];
+            bool x_401_transpose_y_0 = const()[name = string("x_401_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3509_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325663296))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325951360))))[name = string("op_3509_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3507_cast_fp16)[name = string("transpose_193")];
+            tensor<fp16, [1, 8, 188, 375]> x_401_cast_fp16 = matmul(transpose_x = x_401_transpose_x_0, transpose_y = x_401_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = op_3509_to_fp16_palettized)[name = string("x_401_cast_fp16")];
+            tensor<int32, [8]> x_403_pad_0 = const()[name = string("x_403_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_403_mode_0 = const()[name = string("x_403_mode_0"), val = string("constant")];
+            fp16 const_258_to_fp16 = const()[name = string("const_258_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_403_cast_fp16 = pad(constant_val = const_258_to_fp16, mode = x_403_mode_0, pad = x_403_pad_0, x = x_401_cast_fp16)[name = string("x_403_cast_fp16")];
+            tensor<int32, [4]> var_3517 = const()[name = string("op_3517"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_405_cast_fp16 = reshape(shape = var_3517, x = x_403_cast_fp16)[name = string("x_405_cast_fp16")];
+            tensor<int32, [4]> var_3521_begin_0 = const()[name = string("op_3521_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3521_end_0 = const()[name = string("op_3521_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3521_end_mask_0 = const()[name = string("op_3521_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3521_cast_fp16 = slice_by_index(begin = var_3521_begin_0, end = var_3521_end_0, end_mask = var_3521_end_mask_0, x = x_405_cast_fp16)[name = string("op_3521_cast_fp16")];
+            tensor<int32, [4]> var_3522 = const()[name = string("op_3522"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_69_cast_fp16 = reshape(shape = var_3522, x = var_3521_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<int32, [4]> transpose_130_perm_0 = const()[name = string("transpose_130_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_131_perm_0 = const()[name = string("transpose_131_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = string("transpose_191")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_130 = transpose(perm = transpose_130_perm_0, x = var_3505_cast_fp16)[name = string("transpose_192")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_71_begin_0 = const()[name = string("matrix_bd_71_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_71_end_0 = const()[name = string("matrix_bd_71_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_71_end_mask_0 = const()[name = string("matrix_bd_71_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3531_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = string("op_3531_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<fp16, [1, 8, 188, 188]> _inversed_scores_69_cast_fp16 = mul(x = var_3531_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = string("_inversed_scores_69_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_71_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_15)[name = string("scores_71_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3537_cast_fp16 = softmax(axis = var_152, x = scores_71_cast_fp16)[name = string("op_3537_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_925_cast_fp16 = select(a = var_164_to_fp16, b = var_3537_cast_fp16, cond = mask_15)[name = string("input_925_cast_fp16")];
+            bool x_407_transpose_x_0 = const()[name = string("x_407_transpose_x_0"), val = bool(false)];
+            bool x_407_transpose_y_0 = const()[name = string("x_407_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_35_cast_fp16)[name = string("transpose_190")];
+            tensor<fp16, [1, 8, 188, 128]> x_407_cast_fp16 = matmul(transpose_x = x_407_transpose_x_0, transpose_y = x_407_transpose_y_0, x = input_925_cast_fp16, y = value_39_cast_fp16)[name = string("x_407_cast_fp16")];
+            tensor<int32, [4]> var_3541_perm_0 = const()[name = string("op_3541_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3542 = const()[name = string("op_3542"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3541_cast_fp16 = transpose(perm = var_3541_perm_0, x = x_407_cast_fp16)[name = string("transpose_189")];
+            tensor<fp16, [1, 188, 1024]> input_927_cast_fp16 = reshape(shape = var_3542, x = var_3541_cast_fp16)[name = string("input_927_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325951552))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326738048))))[name = string("encoder_module_layers_17_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326738240)))];
+            tensor<fp16, [1, 188, 1024]> linear_160_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_out_weight_to_fp16_palettized, x = input_927_cast_fp16)[name = string("linear_160_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_931_cast_fp16 = add(x = input_923_cast_fp16, y = linear_160_cast_fp16)[name = string("input_931_cast_fp16")];
+            tensor<int32, [1]> x_411_axes_0 = const()[name = string("x_411_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326740352)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326742464)))];
+            tensor<fp16, [1, 188, 1024]> x_411_cast_fp16 = layer_norm(axes = x_411_axes_0, beta = encoder_module_layers_17_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_conv_weight_to_fp16, x = input_931_cast_fp16)[name = string("x_411_cast_fp16")];
+            tensor<int32, [3]> input_933_perm_0 = const()[name = string("input_933_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_935_pad_type_0 = const()[name = string("input_935_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_935_strides_0 = const()[name = string("input_935_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_935_pad_0 = const()[name = string("input_935_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_935_dilations_0 = const()[name = string("input_935_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_935_groups_0 = const()[name = string("input_935_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_17_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326744576))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328317504))))[name = string("encoder_module_layers_17_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328317696)))];
+            tensor<fp16, [1, 1024, 188]> input_933_cast_fp16 = transpose(perm = input_933_perm_0, x = x_411_cast_fp16)[name = string("transpose_188")];
+            tensor<fp16, [1, 2048, 188]> input_935_cast_fp16 = conv(bias = encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_17_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_933_cast_fp16)[name = string("input_935_cast_fp16")];
+            int32 x_413_split_num_splits_0 = const()[name = string("x_413_split_num_splits_0"), val = int32(2)];
+            int32 x_413_split_axis_0 = const()[name = string("x_413_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_413_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_413_split_cast_fp16_1 = split(axis = x_413_split_axis_0, num_splits = x_413_split_num_splits_0, x = input_935_cast_fp16)[name = string("x_413_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_413_split_1_sigmoid_cast_fp16 = sigmoid(x = x_413_split_cast_fp16_1)[name = string("x_413_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_413_cast_fp16 = mul(x = x_413_split_cast_fp16_0, y = x_413_split_1_sigmoid_cast_fp16)[name = string("x_413_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_937_cast_fp16 = select(a = var_164_to_fp16, b = x_413_cast_fp16, cond = var_608)[name = string("input_937_cast_fp16")];
+            tensor<int32, [6]> input_939_pad_0 = const()[name = string("input_939_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_939_mode_0 = const()[name = string("input_939_mode_0"), val = string("constant")];
+            fp16 const_261_to_fp16 = const()[name = string("const_261_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_939_cast_fp16 = pad(constant_val = const_261_to_fp16, mode = input_939_mode_0, pad = input_939_pad_0, x = input_937_cast_fp16)[name = string("input_939_cast_fp16")];
+            string input_941_pad_type_0 = const()[name = string("input_941_pad_type_0"), val = string("valid")];
+            int32 input_941_groups_0 = const()[name = string("input_941_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_941_strides_0 = const()[name = string("input_941_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_941_pad_0 = const()[name = string("input_941_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_941_dilations_0 = const()[name = string("input_941_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_356_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328321856))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328328832))))[name = string("const_356_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_357_to_fp16 = const()[name = string("const_357_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328329024)))];
+            tensor<fp16, [1, 1024, 188]> input_943_cast_fp16 = conv(bias = const_357_to_fp16, dilations = input_941_dilations_0, groups = input_941_groups_0, pad = input_941_pad_0, pad_type = input_941_pad_type_0, strides = input_941_strides_0, weight = const_356_to_fp16_palettized, x = input_939_cast_fp16)[name = string("input_943_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_945_cast_fp16 = silu(x = input_943_cast_fp16)[name = string("input_945_cast_fp16")];
+            string x_415_pad_type_0 = const()[name = string("x_415_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_415_strides_0 = const()[name = string("x_415_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_415_pad_0 = const()[name = string("x_415_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_415_dilations_0 = const()[name = string("x_415_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_415_groups_0 = const()[name = string("x_415_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_17_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328331136))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329117632))))[name = string("encoder_module_layers_17_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329117824)))];
+            tensor<fp16, [1, 1024, 188]> x_415_cast_fp16 = conv(bias = encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_17_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_945_cast_fp16)[name = string("x_415_cast_fp16")];
+            tensor<int32, [3]> input_947_perm_0 = const()[name = string("input_947_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_947_cast_fp16 = transpose(perm = input_947_perm_0, x = x_415_cast_fp16)[name = string("transpose_187")];
+            tensor<fp16, [1, 188, 1024]> input_949_cast_fp16 = add(x = input_931_cast_fp16, y = input_947_cast_fp16)[name = string("input_949_cast_fp16")];
+            tensor<int32, [1]> input_951_axes_0 = const()[name = string("input_951_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329119936)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329122048)))];
+            tensor<fp16, [1, 188, 1024]> input_951_cast_fp16 = layer_norm(axes = input_951_axes_0, beta = encoder_module_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_feed_forward2_weight_to_fp16, x = input_949_cast_fp16)[name = string("input_951_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329124160))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332269952))))[name = string("encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332270144)))];
+            tensor<fp16, [1, 188, 4096]> linear_161_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16_palettized, x = input_951_cast_fp16)[name = string("linear_161_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_955_cast_fp16 = silu(x = linear_161_cast_fp16)[name = string("input_955_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332278400))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335424192))))[name = string("encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335424384)))];
+            tensor<fp16, [1, 188, 1024]> linear_162_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16_palettized, x = input_955_cast_fp16)[name = string("linear_162_cast_fp16")];
+            fp16 var_3608_to_fp16 = const()[name = string("op_3608_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3609_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_3608_to_fp16)[name = string("op_3609_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_961_cast_fp16 = add(x = input_949_cast_fp16, y = var_3609_cast_fp16)[name = string("input_961_cast_fp16")];
+            tensor<int32, [1]> input_963_axes_0 = const()[name = string("input_963_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335426496)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335428608)))];
+            tensor<fp16, [1, 188, 1024]> input_963_cast_fp16 = layer_norm(axes = input_963_axes_0, beta = encoder_module_layers_17_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_out_weight_to_fp16, x = input_961_cast_fp16)[name = string("input_963_cast_fp16")];
+            tensor<int32, [1]> input_965_axes_0 = const()[name = string("input_965_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335430720)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335432832)))];
+            tensor<fp16, [1, 188, 1024]> input_965_cast_fp16 = layer_norm(axes = input_965_axes_0, beta = encoder_module_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_feed_forward1_weight_to_fp16, x = input_963_cast_fp16)[name = string("input_965_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335434944))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338580736))))[name = string("encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338580928)))];
+            tensor<fp16, [1, 188, 4096]> linear_163_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16_palettized, x = input_965_cast_fp16)[name = string("linear_163_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_969_cast_fp16 = silu(x = linear_163_cast_fp16)[name = string("input_969_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338589184))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341734976))))[name = string("encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341735168)))];
+            tensor<fp16, [1, 188, 1024]> linear_164_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16_palettized, x = input_969_cast_fp16)[name = string("linear_164_cast_fp16")];
+            fp16 var_3639_to_fp16 = const()[name = string("op_3639_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3640_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3639_to_fp16)[name = string("op_3640_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_975_cast_fp16 = add(x = input_963_cast_fp16, y = var_3640_cast_fp16)[name = string("input_975_cast_fp16")];
+            tensor<int32, [1]> query_37_axes_0 = const()[name = string("query_37_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341737280)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341739392)))];
+            tensor<fp16, [1, 188, 1024]> query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = encoder_module_layers_18_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_self_att_weight_to_fp16, x = input_975_cast_fp16)[name = string("query_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(341741504))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342528000))))[name = string("encoder_module_layers_18_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342528192)))];
+            tensor<fp16, [1, 188, 1024]> linear_165_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_q_weight_to_fp16_palettized, x = query_37_cast_fp16)[name = string("linear_165_cast_fp16")];
+            tensor<int32, [4]> var_3657 = const()[name = string("op_3657"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_109_cast_fp16 = reshape(shape = var_3657, x = linear_165_cast_fp16)[name = string("q_109_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(342530304))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343316800))))[name = string("encoder_module_layers_18_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343316992)))];
+            tensor<fp16, [1, 188, 1024]> linear_166_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_k_weight_to_fp16_palettized, x = query_37_cast_fp16)[name = string("linear_166_cast_fp16")];
+            tensor<int32, [4]> var_3662 = const()[name = string("op_3662"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_73_cast_fp16 = reshape(shape = var_3662, x = linear_166_cast_fp16)[name = string("k_73_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(343319104))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344105600))))[name = string("encoder_module_layers_18_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344105792)))];
+            tensor<fp16, [1, 188, 1024]> linear_167_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_v_weight_to_fp16_palettized, x = query_37_cast_fp16)[name = string("linear_167_cast_fp16")];
+            tensor<int32, [4]> var_3667 = const()[name = string("op_3667"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_37_cast_fp16 = reshape(shape = var_3667, x = linear_167_cast_fp16)[name = string("v_37_cast_fp16")];
+            tensor<int32, [4]> value_41_perm_0 = const()[name = string("value_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344107904)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3679_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_module_layers_18_self_attn_pos_bias_u_to_fp16)[name = string("op_3679_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344110016)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3681_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_module_layers_18_self_attn_pos_bias_v_to_fp16)[name = string("op_3681_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_37_perm_0 = const()[name = string("q_with_bias_v_37_perm_0"), val = tensor<int32, [4]>([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<fp16, [1, 8, 128, 375]> op_3683_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344112128))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344400192))))[name = string("op_3683_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3681_cast_fp16)[name = string("transpose_186")];
+            tensor<fp16, [1, 8, 188, 375]> x_423_cast_fp16 = matmul(transpose_x = x_423_transpose_x_0, transpose_y = x_423_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = op_3683_to_fp16_palettized)[name = string("x_423_cast_fp16")];
+            tensor<int32, [8]> x_425_pad_0 = const()[name = string("x_425_pad_0"), val = tensor<int32, [8]>([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_268_to_fp16 = const()[name = string("const_268_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_425_cast_fp16 = pad(constant_val = const_268_to_fp16, mode = x_425_mode_0, pad = x_425_pad_0, x = x_423_cast_fp16)[name = string("x_425_cast_fp16")];
+            tensor<int32, [4]> var_3691 = const()[name = string("op_3691"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_427_cast_fp16 = reshape(shape = var_3691, x = x_425_cast_fp16)[name = string("x_427_cast_fp16")];
+            tensor<int32, [4]> var_3695_begin_0 = const()[name = string("op_3695_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3695_end_0 = const()[name = string("op_3695_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3695_end_mask_0 = const()[name = string("op_3695_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3695_cast_fp16 = slice_by_index(begin = var_3695_begin_0, end = var_3695_end_0, end_mask = var_3695_end_mask_0, x = x_427_cast_fp16)[name = string("op_3695_cast_fp16")];
+            tensor<int32, [4]> var_3696 = const()[name = string("op_3696"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_73_cast_fp16 = reshape(shape = var_3696, x = var_3695_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<int32, [4]> transpose_132_perm_0 = const()[name = string("transpose_132_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_133_perm_0 = const()[name = string("transpose_133_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = string("transpose_184")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_132 = transpose(perm = transpose_132_perm_0, x = var_3679_cast_fp16)[name = string("transpose_185")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_75_begin_0 = const()[name = string("matrix_bd_75_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_75_end_0 = const()[name = string("matrix_bd_75_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_75_end_mask_0 = const()[name = string("matrix_bd_75_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3705_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = string("op_3705_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<fp16, [1, 8, 188, 188]> _inversed_scores_73_cast_fp16 = mul(x = var_3705_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = string("_inversed_scores_73_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_75_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_15)[name = string("scores_75_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3711_cast_fp16 = softmax(axis = var_152, x = scores_75_cast_fp16)[name = string("op_3711_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_977_cast_fp16 = select(a = var_164_to_fp16, b = var_3711_cast_fp16, cond = mask_15)[name = string("input_977_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<fp16, [1, 8, 188, 128]> value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_37_cast_fp16)[name = string("transpose_183")];
+            tensor<fp16, [1, 8, 188, 128]> x_429_cast_fp16 = matmul(transpose_x = x_429_transpose_x_0, transpose_y = x_429_transpose_y_0, x = input_977_cast_fp16, y = value_41_cast_fp16)[name = string("x_429_cast_fp16")];
+            tensor<int32, [4]> var_3715_perm_0 = const()[name = string("op_3715_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3716 = const()[name = string("op_3716"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3715_cast_fp16 = transpose(perm = var_3715_perm_0, x = x_429_cast_fp16)[name = string("transpose_182")];
+            tensor<fp16, [1, 188, 1024]> input_979_cast_fp16 = reshape(shape = var_3716, x = var_3715_cast_fp16)[name = string("input_979_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344400384))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345186880))))[name = string("encoder_module_layers_18_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345187072)))];
+            tensor<fp16, [1, 188, 1024]> linear_169_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_out_weight_to_fp16_palettized, x = input_979_cast_fp16)[name = string("linear_169_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_983_cast_fp16 = add(x = input_975_cast_fp16, y = linear_169_cast_fp16)[name = string("input_983_cast_fp16")];
+            tensor<int32, [1]> x_433_axes_0 = const()[name = string("x_433_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345189184)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345191296)))];
+            tensor<fp16, [1, 188, 1024]> x_433_cast_fp16 = layer_norm(axes = x_433_axes_0, beta = encoder_module_layers_18_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_conv_weight_to_fp16, x = input_983_cast_fp16)[name = string("x_433_cast_fp16")];
+            tensor<int32, [3]> input_985_perm_0 = const()[name = string("input_985_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_987_pad_type_0 = const()[name = string("input_987_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_987_strides_0 = const()[name = string("input_987_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_987_pad_0 = const()[name = string("input_987_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_987_dilations_0 = const()[name = string("input_987_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_987_groups_0 = const()[name = string("input_987_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_18_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345193408))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346766336))))[name = string("encoder_module_layers_18_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346766528)))];
+            tensor<fp16, [1, 1024, 188]> input_985_cast_fp16 = transpose(perm = input_985_perm_0, x = x_433_cast_fp16)[name = string("transpose_181")];
+            tensor<fp16, [1, 2048, 188]> input_987_cast_fp16 = conv(bias = encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_18_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_985_cast_fp16)[name = string("input_987_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<fp16, [1, 1024, 188]> x_435_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_435_split_cast_fp16_1 = split(axis = x_435_split_axis_0, num_splits = x_435_split_num_splits_0, x = input_987_cast_fp16)[name = string("x_435_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> input_989_cast_fp16 = select(a = var_164_to_fp16, b = x_435_cast_fp16, cond = var_608)[name = string("input_989_cast_fp16")];
+            tensor<int32, [6]> input_991_pad_0 = const()[name = string("input_991_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_991_mode_0 = const()[name = string("input_991_mode_0"), val = string("constant")];
+            fp16 const_271_to_fp16 = const()[name = string("const_271_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_991_cast_fp16 = pad(constant_val = const_271_to_fp16, mode = input_991_mode_0, pad = input_991_pad_0, x = input_989_cast_fp16)[name = string("input_991_cast_fp16")];
+            string input_993_pad_type_0 = const()[name = string("input_993_pad_type_0"), val = string("valid")];
+            int32 input_993_groups_0 = const()[name = string("input_993_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_993_strides_0 = const()[name = string("input_993_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_993_pad_0 = const()[name = string("input_993_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_993_dilations_0 = const()[name = string("input_993_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_358_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346770688))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346777664))))[name = string("const_358_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_359_to_fp16 = const()[name = string("const_359_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346777856)))];
+            tensor<fp16, [1, 1024, 188]> input_995_cast_fp16 = conv(bias = const_359_to_fp16, dilations = input_993_dilations_0, groups = input_993_groups_0, pad = input_993_pad_0, pad_type = input_993_pad_type_0, strides = input_993_strides_0, weight = const_358_to_fp16_palettized, x = input_991_cast_fp16)[name = string("input_995_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_997_cast_fp16 = silu(x = input_995_cast_fp16)[name = string("input_997_cast_fp16")];
+            string x_437_pad_type_0 = const()[name = string("x_437_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_437_strides_0 = const()[name = string("x_437_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_437_pad_0 = const()[name = string("x_437_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_437_dilations_0 = const()[name = string("x_437_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_437_groups_0 = const()[name = string("x_437_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_18_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346779968))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(347566464))))[name = string("encoder_module_layers_18_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(347566656)))];
+            tensor<fp16, [1, 1024, 188]> x_437_cast_fp16 = conv(bias = encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_18_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_997_cast_fp16)[name = string("x_437_cast_fp16")];
+            tensor<int32, [3]> input_999_perm_0 = const()[name = string("input_999_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_999_cast_fp16 = transpose(perm = input_999_perm_0, x = x_437_cast_fp16)[name = string("transpose_180")];
+            tensor<fp16, [1, 188, 1024]> input_1001_cast_fp16 = add(x = input_983_cast_fp16, y = input_999_cast_fp16)[name = string("input_1001_cast_fp16")];
+            tensor<int32, [1]> input_1003_axes_0 = const()[name = string("input_1003_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(347568768)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(347570880)))];
+            tensor<fp16, [1, 188, 1024]> input_1003_cast_fp16 = layer_norm(axes = input_1003_axes_0, beta = encoder_module_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_feed_forward2_weight_to_fp16, x = input_1001_cast_fp16)[name = string("input_1003_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(347572992))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350718784))))[name = string("encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350718976)))];
+            tensor<fp16, [1, 188, 4096]> linear_170_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1003_cast_fp16)[name = string("linear_170_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1007_cast_fp16 = silu(x = linear_170_cast_fp16)[name = string("input_1007_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350727232))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353873024))))[name = string("encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353873216)))];
+            tensor<fp16, [1, 188, 1024]> linear_171_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1007_cast_fp16)[name = string("linear_171_cast_fp16")];
+            fp16 var_3782_to_fp16 = const()[name = string("op_3782_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3783_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_3782_to_fp16)[name = string("op_3783_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1013_cast_fp16 = add(x = input_1001_cast_fp16, y = var_3783_cast_fp16)[name = string("input_1013_cast_fp16")];
+            tensor<int32, [1]> input_1015_axes_0 = const()[name = string("input_1015_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353875328)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353877440)))];
+            tensor<fp16, [1, 188, 1024]> input_1015_cast_fp16 = layer_norm(axes = input_1015_axes_0, beta = encoder_module_layers_18_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_out_weight_to_fp16, x = input_1013_cast_fp16)[name = string("input_1015_cast_fp16")];
+            tensor<int32, [1]> input_1017_axes_0 = const()[name = string("input_1017_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353879552)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353881664)))];
+            tensor<fp16, [1, 188, 1024]> input_1017_cast_fp16 = layer_norm(axes = input_1017_axes_0, beta = encoder_module_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1015_cast_fp16)[name = string("input_1017_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353883776))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357029568))))[name = string("encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357029760)))];
+            tensor<fp16, [1, 188, 4096]> linear_172_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1017_cast_fp16)[name = string("linear_172_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1021_cast_fp16 = silu(x = linear_172_cast_fp16)[name = string("input_1021_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357038016))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360183808))))[name = string("encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360184000)))];
+            tensor<fp16, [1, 188, 1024]> linear_173_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1021_cast_fp16)[name = string("linear_173_cast_fp16")];
+            fp16 var_3813_to_fp16 = const()[name = string("op_3813_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3814_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3813_to_fp16)[name = string("op_3814_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1027_cast_fp16 = add(x = input_1015_cast_fp16, y = var_3814_cast_fp16)[name = string("input_1027_cast_fp16")];
+            tensor<int32, [1]> query_39_axes_0 = const()[name = string("query_39_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360186112)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360188224)))];
+            tensor<fp16, [1, 188, 1024]> query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = encoder_module_layers_19_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_self_att_weight_to_fp16, x = input_1027_cast_fp16)[name = string("query_39_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360190336))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360976832))))[name = string("encoder_module_layers_19_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360977024)))];
+            tensor<fp16, [1, 188, 1024]> linear_174_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_q_weight_to_fp16_palettized, x = query_39_cast_fp16)[name = string("linear_174_cast_fp16")];
+            tensor<int32, [4]> var_3831 = const()[name = string("op_3831"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_115_cast_fp16 = reshape(shape = var_3831, x = linear_174_cast_fp16)[name = string("q_115_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360979136))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361765632))))[name = string("encoder_module_layers_19_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361765824)))];
+            tensor<fp16, [1, 188, 1024]> linear_175_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_k_weight_to_fp16_palettized, x = query_39_cast_fp16)[name = string("linear_175_cast_fp16")];
+            tensor<int32, [4]> var_3836 = const()[name = string("op_3836"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_77_cast_fp16 = reshape(shape = var_3836, x = linear_175_cast_fp16)[name = string("k_77_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361767936))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362554432))))[name = string("encoder_module_layers_19_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362554624)))];
+            tensor<fp16, [1, 188, 1024]> linear_176_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_v_weight_to_fp16_palettized, x = query_39_cast_fp16)[name = string("linear_176_cast_fp16")];
+            tensor<int32, [4]> var_3841 = const()[name = string("op_3841"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_39_cast_fp16 = reshape(shape = var_3841, x = linear_176_cast_fp16)[name = string("v_39_cast_fp16")];
+            tensor<int32, [4]> value_43_perm_0 = const()[name = string("value_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362556736)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3853_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_module_layers_19_self_attn_pos_bias_u_to_fp16)[name = string("op_3853_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362558848)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3855_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_module_layers_19_self_attn_pos_bias_v_to_fp16)[name = string("op_3855_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_39_perm_0 = const()[name = string("q_with_bias_v_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_445_transpose_x_0 = const()[name = string("x_445_transpose_x_0"), val = bool(false)];
+            bool x_445_transpose_y_0 = const()[name = string("x_445_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3857_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362560960))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362849024))))[name = string("op_3857_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3855_cast_fp16)[name = string("transpose_179")];
+            tensor<fp16, [1, 8, 188, 375]> x_445_cast_fp16 = matmul(transpose_x = x_445_transpose_x_0, transpose_y = x_445_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = op_3857_to_fp16_palettized)[name = string("x_445_cast_fp16")];
+            tensor<int32, [8]> x_447_pad_0 = const()[name = string("x_447_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_447_mode_0 = const()[name = string("x_447_mode_0"), val = string("constant")];
+            fp16 const_278_to_fp16 = const()[name = string("const_278_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_447_cast_fp16 = pad(constant_val = const_278_to_fp16, mode = x_447_mode_0, pad = x_447_pad_0, x = x_445_cast_fp16)[name = string("x_447_cast_fp16")];
+            tensor<int32, [4]> var_3865 = const()[name = string("op_3865"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_449_cast_fp16 = reshape(shape = var_3865, x = x_447_cast_fp16)[name = string("x_449_cast_fp16")];
+            tensor<int32, [4]> var_3869_begin_0 = const()[name = string("op_3869_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3869_end_0 = const()[name = string("op_3869_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3869_end_mask_0 = const()[name = string("op_3869_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3869_cast_fp16 = slice_by_index(begin = var_3869_begin_0, end = var_3869_end_0, end_mask = var_3869_end_mask_0, x = x_449_cast_fp16)[name = string("op_3869_cast_fp16")];
+            tensor<int32, [4]> var_3870 = const()[name = string("op_3870"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_77_cast_fp16 = reshape(shape = var_3870, x = var_3869_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<int32, [4]> transpose_134_perm_0 = const()[name = string("transpose_134_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_135_perm_0 = const()[name = string("transpose_135_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = string("transpose_177")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_134 = transpose(perm = transpose_134_perm_0, x = var_3853_cast_fp16)[name = string("transpose_178")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_79_begin_0 = const()[name = string("matrix_bd_79_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_79_end_0 = const()[name = string("matrix_bd_79_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_79_end_mask_0 = const()[name = string("matrix_bd_79_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3879_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = string("op_3879_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<fp16, [1, 8, 188, 188]> _inversed_scores_77_cast_fp16 = mul(x = var_3879_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = string("_inversed_scores_77_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_79_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_15)[name = string("scores_79_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3885_cast_fp16 = softmax(axis = var_152, x = scores_79_cast_fp16)[name = string("op_3885_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1029_cast_fp16 = select(a = var_164_to_fp16, b = var_3885_cast_fp16, cond = mask_15)[name = string("input_1029_cast_fp16")];
+            bool x_451_transpose_x_0 = const()[name = string("x_451_transpose_x_0"), val = bool(false)];
+            bool x_451_transpose_y_0 = const()[name = string("x_451_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_39_cast_fp16)[name = string("transpose_176")];
+            tensor<fp16, [1, 8, 188, 128]> x_451_cast_fp16 = matmul(transpose_x = x_451_transpose_x_0, transpose_y = x_451_transpose_y_0, x = input_1029_cast_fp16, y = value_43_cast_fp16)[name = string("x_451_cast_fp16")];
+            tensor<int32, [4]> var_3889_perm_0 = const()[name = string("op_3889_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3890 = const()[name = string("op_3890"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3889_cast_fp16 = transpose(perm = var_3889_perm_0, x = x_451_cast_fp16)[name = string("transpose_175")];
+            tensor<fp16, [1, 188, 1024]> input_1031_cast_fp16 = reshape(shape = var_3890, x = var_3889_cast_fp16)[name = string("input_1031_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362849216))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363635712))))[name = string("encoder_module_layers_19_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363635904)))];
+            tensor<fp16, [1, 188, 1024]> linear_178_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_out_weight_to_fp16_palettized, x = input_1031_cast_fp16)[name = string("linear_178_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1035_cast_fp16 = add(x = input_1027_cast_fp16, y = linear_178_cast_fp16)[name = string("input_1035_cast_fp16")];
+            tensor<int32, [1]> x_455_axes_0 = const()[name = string("x_455_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363638016)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363640128)))];
+            tensor<fp16, [1, 188, 1024]> x_455_cast_fp16 = layer_norm(axes = x_455_axes_0, beta = encoder_module_layers_19_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_conv_weight_to_fp16, x = input_1035_cast_fp16)[name = string("x_455_cast_fp16")];
+            tensor<int32, [3]> input_1037_perm_0 = const()[name = string("input_1037_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1039_pad_type_0 = const()[name = string("input_1039_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1039_strides_0 = const()[name = string("input_1039_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1039_pad_0 = const()[name = string("input_1039_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1039_dilations_0 = const()[name = string("input_1039_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1039_groups_0 = const()[name = string("input_1039_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_19_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363642240))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365215168))))[name = string("encoder_module_layers_19_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365215360)))];
+            tensor<fp16, [1, 1024, 188]> input_1037_cast_fp16 = transpose(perm = input_1037_perm_0, x = x_455_cast_fp16)[name = string("transpose_174")];
+            tensor<fp16, [1, 2048, 188]> input_1039_cast_fp16 = conv(bias = encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_19_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1037_cast_fp16)[name = string("input_1039_cast_fp16")];
+            int32 x_457_split_num_splits_0 = const()[name = string("x_457_split_num_splits_0"), val = int32(2)];
+            int32 x_457_split_axis_0 = const()[name = string("x_457_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_457_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_457_split_cast_fp16_1 = split(axis = x_457_split_axis_0, num_splits = x_457_split_num_splits_0, x = input_1039_cast_fp16)[name = string("x_457_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_457_split_1_sigmoid_cast_fp16 = sigmoid(x = x_457_split_cast_fp16_1)[name = string("x_457_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_457_cast_fp16 = mul(x = x_457_split_cast_fp16_0, y = x_457_split_1_sigmoid_cast_fp16)[name = string("x_457_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1041_cast_fp16 = select(a = var_164_to_fp16, b = x_457_cast_fp16, cond = var_608)[name = string("input_1041_cast_fp16")];
+            tensor<int32, [6]> input_1043_pad_0 = const()[name = string("input_1043_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1043_mode_0 = const()[name = string("input_1043_mode_0"), val = string("constant")];
+            fp16 const_281_to_fp16 = const()[name = string("const_281_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1043_cast_fp16 = pad(constant_val = const_281_to_fp16, mode = input_1043_mode_0, pad = input_1043_pad_0, x = input_1041_cast_fp16)[name = string("input_1043_cast_fp16")];
+            string input_1045_pad_type_0 = const()[name = string("input_1045_pad_type_0"), val = string("valid")];
+            int32 input_1045_groups_0 = const()[name = string("input_1045_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1045_strides_0 = const()[name = string("input_1045_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1045_pad_0 = const()[name = string("input_1045_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1045_dilations_0 = const()[name = string("input_1045_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_360_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365219520))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365226496))))[name = string("const_360_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_361_to_fp16 = const()[name = string("const_361_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365226688)))];
+            tensor<fp16, [1, 1024, 188]> input_1047_cast_fp16 = conv(bias = const_361_to_fp16, dilations = input_1045_dilations_0, groups = input_1045_groups_0, pad = input_1045_pad_0, pad_type = input_1045_pad_type_0, strides = input_1045_strides_0, weight = const_360_to_fp16_palettized, x = input_1043_cast_fp16)[name = string("input_1047_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1049_cast_fp16 = silu(x = input_1047_cast_fp16)[name = string("input_1049_cast_fp16")];
+            string x_459_pad_type_0 = const()[name = string("x_459_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_459_strides_0 = const()[name = string("x_459_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_459_pad_0 = const()[name = string("x_459_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_459_dilations_0 = const()[name = string("x_459_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_459_groups_0 = const()[name = string("x_459_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365228800))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366015296))))[name = string("encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366015488)))];
+            tensor<fp16, [1, 1024, 188]> x_459_cast_fp16 = conv(bias = encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16, dilations = x_459_dilations_0, groups = x_459_groups_0, pad = x_459_pad_0, pad_type = x_459_pad_type_0, strides = x_459_strides_0, weight = encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1049_cast_fp16)[name = string("x_459_cast_fp16")];
+            tensor<int32, [3]> input_1051_perm_0 = const()[name = string("input_1051_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1051_cast_fp16 = transpose(perm = input_1051_perm_0, x = x_459_cast_fp16)[name = string("transpose_173")];
+            tensor<fp16, [1, 188, 1024]> input_1053_cast_fp16 = add(x = input_1035_cast_fp16, y = input_1051_cast_fp16)[name = string("input_1053_cast_fp16")];
+            tensor<int32, [1]> input_1055_axes_0 = const()[name = string("input_1055_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366017600)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366019712)))];
+            tensor<fp16, [1, 188, 1024]> input_1055_cast_fp16 = layer_norm(axes = input_1055_axes_0, beta = encoder_module_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1053_cast_fp16)[name = string("input_1055_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366021824))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369167616))))[name = string("encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369167808)))];
+            tensor<fp16, [1, 188, 4096]> linear_179_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1055_cast_fp16)[name = string("linear_179_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1059_cast_fp16 = silu(x = linear_179_cast_fp16)[name = string("input_1059_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369176064))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372321856))))[name = string("encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372322048)))];
+            tensor<fp16, [1, 188, 1024]> linear_180_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1059_cast_fp16)[name = string("linear_180_cast_fp16")];
+            fp16 var_3956_to_fp16 = const()[name = string("op_3956_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3957_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_3956_to_fp16)[name = string("op_3957_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1065_cast_fp16 = add(x = input_1053_cast_fp16, y = var_3957_cast_fp16)[name = string("input_1065_cast_fp16")];
+            tensor<int32, [1]> input_1067_axes_0 = const()[name = string("input_1067_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372324160)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372326272)))];
+            tensor<fp16, [1, 188, 1024]> input_1067_cast_fp16 = layer_norm(axes = input_1067_axes_0, beta = encoder_module_layers_19_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_out_weight_to_fp16, x = input_1065_cast_fp16)[name = string("input_1067_cast_fp16")];
+            tensor<int32, [1]> input_1069_axes_0 = const()[name = string("input_1069_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372328384)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372330496)))];
+            tensor<fp16, [1, 188, 1024]> input_1069_cast_fp16 = layer_norm(axes = input_1069_axes_0, beta = encoder_module_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1067_cast_fp16)[name = string("input_1069_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372332608))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375478400))))[name = string("encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375478592)))];
+            tensor<fp16, [1, 188, 4096]> linear_181_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1069_cast_fp16)[name = string("linear_181_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1073_cast_fp16 = silu(x = linear_181_cast_fp16)[name = string("input_1073_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(375486848))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(378632640))))[name = string("encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(378632832)))];
+            tensor<fp16, [1, 188, 1024]> linear_182_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1073_cast_fp16)[name = string("linear_182_cast_fp16")];
+            fp16 var_3987_to_fp16 = const()[name = string("op_3987_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3988_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3987_to_fp16)[name = string("op_3988_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1079_cast_fp16 = add(x = input_1067_cast_fp16, y = var_3988_cast_fp16)[name = string("input_1079_cast_fp16")];
+            tensor<int32, [1]> query_41_axes_0 = const()[name = string("query_41_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(378634944)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(378637056)))];
+            tensor<fp16, [1, 188, 1024]> query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = encoder_module_layers_20_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_self_att_weight_to_fp16, x = input_1079_cast_fp16)[name = string("query_41_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(378639168))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(379425664))))[name = string("encoder_module_layers_20_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(379425856)))];
+            tensor<fp16, [1, 188, 1024]> linear_183_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_q_weight_to_fp16_palettized, x = query_41_cast_fp16)[name = string("linear_183_cast_fp16")];
+            tensor<int32, [4]> var_4005 = const()[name = string("op_4005"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_121_cast_fp16 = reshape(shape = var_4005, x = linear_183_cast_fp16)[name = string("q_121_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(379427968))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(380214464))))[name = string("encoder_module_layers_20_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(380214656)))];
+            tensor<fp16, [1, 188, 1024]> linear_184_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_k_weight_to_fp16_palettized, x = query_41_cast_fp16)[name = string("linear_184_cast_fp16")];
+            tensor<int32, [4]> var_4010 = const()[name = string("op_4010"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_81_cast_fp16 = reshape(shape = var_4010, x = linear_184_cast_fp16)[name = string("k_81_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(380216768))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381003264))))[name = string("encoder_module_layers_20_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381003456)))];
+            tensor<fp16, [1, 188, 1024]> linear_185_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_v_weight_to_fp16_palettized, x = query_41_cast_fp16)[name = string("linear_185_cast_fp16")];
+            tensor<int32, [4]> var_4015 = const()[name = string("op_4015"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_41_cast_fp16 = reshape(shape = var_4015, x = linear_185_cast_fp16)[name = string("v_41_cast_fp16")];
+            tensor<int32, [4]> value_45_perm_0 = const()[name = string("value_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381005568)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4027_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_module_layers_20_self_attn_pos_bias_u_to_fp16)[name = string("op_4027_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381007680)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4029_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_module_layers_20_self_attn_pos_bias_v_to_fp16)[name = string("op_4029_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_41_perm_0 = const()[name = string("q_with_bias_v_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_467_transpose_x_0 = const()[name = string("x_467_transpose_x_0"), val = bool(false)];
+            bool x_467_transpose_y_0 = const()[name = string("x_467_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_4031_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381009792))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381297856))))[name = string("op_4031_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_4029_cast_fp16)[name = string("transpose_172")];
+            tensor<fp16, [1, 8, 188, 375]> x_467_cast_fp16 = matmul(transpose_x = x_467_transpose_x_0, transpose_y = x_467_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = op_4031_to_fp16_palettized)[name = string("x_467_cast_fp16")];
+            tensor<int32, [8]> x_469_pad_0 = const()[name = string("x_469_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_469_mode_0 = const()[name = string("x_469_mode_0"), val = string("constant")];
+            fp16 const_288_to_fp16 = const()[name = string("const_288_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_469_cast_fp16 = pad(constant_val = const_288_to_fp16, mode = x_469_mode_0, pad = x_469_pad_0, x = x_467_cast_fp16)[name = string("x_469_cast_fp16")];
+            tensor<int32, [4]> var_4039 = const()[name = string("op_4039"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_471_cast_fp16 = reshape(shape = var_4039, x = x_469_cast_fp16)[name = string("x_471_cast_fp16")];
+            tensor<int32, [4]> var_4043_begin_0 = const()[name = string("op_4043_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4043_end_0 = const()[name = string("op_4043_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4043_end_mask_0 = const()[name = string("op_4043_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4043_cast_fp16 = slice_by_index(begin = var_4043_begin_0, end = var_4043_end_0, end_mask = var_4043_end_mask_0, x = x_471_cast_fp16)[name = string("op_4043_cast_fp16")];
+            tensor<int32, [4]> var_4044 = const()[name = string("op_4044"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_81_cast_fp16 = reshape(shape = var_4044, x = var_4043_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<int32, [4]> transpose_136_perm_0 = const()[name = string("transpose_136_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_137_perm_0 = const()[name = string("transpose_137_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = string("transpose_170")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_136 = transpose(perm = transpose_136_perm_0, x = var_4027_cast_fp16)[name = string("transpose_171")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_83_begin_0 = const()[name = string("matrix_bd_83_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_83_end_0 = const()[name = string("matrix_bd_83_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_83_end_mask_0 = const()[name = string("matrix_bd_83_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4053_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = string("op_4053_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<fp16, [1, 8, 188, 188]> _inversed_scores_81_cast_fp16 = mul(x = var_4053_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = string("_inversed_scores_81_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_83_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_15)[name = string("scores_83_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4059_cast_fp16 = softmax(axis = var_152, x = scores_83_cast_fp16)[name = string("op_4059_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1081_cast_fp16 = select(a = var_164_to_fp16, b = var_4059_cast_fp16, cond = mask_15)[name = string("input_1081_cast_fp16")];
+            bool x_473_transpose_x_0 = const()[name = string("x_473_transpose_x_0"), val = bool(false)];
+            bool x_473_transpose_y_0 = const()[name = string("x_473_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_41_cast_fp16)[name = string("transpose_169")];
+            tensor<fp16, [1, 8, 188, 128]> x_473_cast_fp16 = matmul(transpose_x = x_473_transpose_x_0, transpose_y = x_473_transpose_y_0, x = input_1081_cast_fp16, y = value_45_cast_fp16)[name = string("x_473_cast_fp16")];
+            tensor<int32, [4]> var_4063_perm_0 = const()[name = string("op_4063_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4064 = const()[name = string("op_4064"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4063_cast_fp16 = transpose(perm = var_4063_perm_0, x = x_473_cast_fp16)[name = string("transpose_168")];
+            tensor<fp16, [1, 188, 1024]> input_1083_cast_fp16 = reshape(shape = var_4064, x = var_4063_cast_fp16)[name = string("input_1083_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381298048))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382084544))))[name = string("encoder_module_layers_20_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382084736)))];
+            tensor<fp16, [1, 188, 1024]> linear_187_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_out_weight_to_fp16_palettized, x = input_1083_cast_fp16)[name = string("linear_187_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1087_cast_fp16 = add(x = input_1079_cast_fp16, y = linear_187_cast_fp16)[name = string("input_1087_cast_fp16")];
+            tensor<int32, [1]> x_477_axes_0 = const()[name = string("x_477_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382086848)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382088960)))];
+            tensor<fp16, [1, 188, 1024]> x_477_cast_fp16 = layer_norm(axes = x_477_axes_0, beta = encoder_module_layers_20_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_conv_weight_to_fp16, x = input_1087_cast_fp16)[name = string("x_477_cast_fp16")];
+            tensor<int32, [3]> input_1089_perm_0 = const()[name = string("input_1089_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1091_pad_type_0 = const()[name = string("input_1091_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1091_strides_0 = const()[name = string("input_1091_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1091_pad_0 = const()[name = string("input_1091_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1091_dilations_0 = const()[name = string("input_1091_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1091_groups_0 = const()[name = string("input_1091_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_20_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382091072))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383664000))))[name = string("encoder_module_layers_20_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383664192)))];
+            tensor<fp16, [1, 1024, 188]> input_1089_cast_fp16 = transpose(perm = input_1089_perm_0, x = x_477_cast_fp16)[name = string("transpose_167")];
+            tensor<fp16, [1, 2048, 188]> input_1091_cast_fp16 = conv(bias = encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_20_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1089_cast_fp16)[name = string("input_1091_cast_fp16")];
+            int32 x_479_split_num_splits_0 = const()[name = string("x_479_split_num_splits_0"), val = int32(2)];
+            int32 x_479_split_axis_0 = const()[name = string("x_479_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_479_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_479_split_cast_fp16_1 = split(axis = x_479_split_axis_0, num_splits = x_479_split_num_splits_0, x = input_1091_cast_fp16)[name = string("x_479_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_479_split_1_sigmoid_cast_fp16 = sigmoid(x = x_479_split_cast_fp16_1)[name = string("x_479_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_479_cast_fp16 = mul(x = x_479_split_cast_fp16_0, y = x_479_split_1_sigmoid_cast_fp16)[name = string("x_479_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1093_cast_fp16 = select(a = var_164_to_fp16, b = x_479_cast_fp16, cond = var_608)[name = string("input_1093_cast_fp16")];
+            tensor<int32, [6]> input_1095_pad_0 = const()[name = string("input_1095_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1095_mode_0 = const()[name = string("input_1095_mode_0"), val = string("constant")];
+            fp16 const_291_to_fp16 = const()[name = string("const_291_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1095_cast_fp16 = pad(constant_val = const_291_to_fp16, mode = input_1095_mode_0, pad = input_1095_pad_0, x = input_1093_cast_fp16)[name = string("input_1095_cast_fp16")];
+            string input_1097_pad_type_0 = const()[name = string("input_1097_pad_type_0"), val = string("valid")];
+            int32 input_1097_groups_0 = const()[name = string("input_1097_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1097_strides_0 = const()[name = string("input_1097_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1097_pad_0 = const()[name = string("input_1097_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1097_dilations_0 = const()[name = string("input_1097_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_362_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383668352))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383675328))))[name = string("const_362_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_363_to_fp16 = const()[name = string("const_363_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383675520)))];
+            tensor<fp16, [1, 1024, 188]> input_1099_cast_fp16 = conv(bias = const_363_to_fp16, dilations = input_1097_dilations_0, groups = input_1097_groups_0, pad = input_1097_pad_0, pad_type = input_1097_pad_type_0, strides = input_1097_strides_0, weight = const_362_to_fp16_palettized, x = input_1095_cast_fp16)[name = string("input_1099_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1101_cast_fp16 = silu(x = input_1099_cast_fp16)[name = string("input_1101_cast_fp16")];
+            string x_481_pad_type_0 = const()[name = string("x_481_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_481_strides_0 = const()[name = string("x_481_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_481_pad_0 = const()[name = string("x_481_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_481_dilations_0 = const()[name = string("x_481_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_481_groups_0 = const()[name = string("x_481_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383677632))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384464128))))[name = string("encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384464320)))];
+            tensor<fp16, [1, 1024, 188]> x_481_cast_fp16 = conv(bias = encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16, dilations = x_481_dilations_0, groups = x_481_groups_0, pad = x_481_pad_0, pad_type = x_481_pad_type_0, strides = x_481_strides_0, weight = encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1101_cast_fp16)[name = string("x_481_cast_fp16")];
+            tensor<int32, [3]> input_1103_perm_0 = const()[name = string("input_1103_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1103_cast_fp16 = transpose(perm = input_1103_perm_0, x = x_481_cast_fp16)[name = string("transpose_166")];
+            tensor<fp16, [1, 188, 1024]> input_1105_cast_fp16 = add(x = input_1087_cast_fp16, y = input_1103_cast_fp16)[name = string("input_1105_cast_fp16")];
+            tensor<int32, [1]> input_1107_axes_0 = const()[name = string("input_1107_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384466432)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384468544)))];
+            tensor<fp16, [1, 188, 1024]> input_1107_cast_fp16 = layer_norm(axes = input_1107_axes_0, beta = encoder_module_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1105_cast_fp16)[name = string("input_1107_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384470656))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(387616448))))[name = string("encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(387616640)))];
+            tensor<fp16, [1, 188, 4096]> linear_188_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1107_cast_fp16)[name = string("linear_188_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1111_cast_fp16 = silu(x = linear_188_cast_fp16)[name = string("input_1111_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(387624896))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390770688))))[name = string("encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390770880)))];
+            tensor<fp16, [1, 188, 1024]> linear_189_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1111_cast_fp16)[name = string("linear_189_cast_fp16")];
+            fp16 var_4130_to_fp16 = const()[name = string("op_4130_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4131_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_4130_to_fp16)[name = string("op_4131_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1117_cast_fp16 = add(x = input_1105_cast_fp16, y = var_4131_cast_fp16)[name = string("input_1117_cast_fp16")];
+            tensor<int32, [1]> input_1119_axes_0 = const()[name = string("input_1119_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390772992)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390775104)))];
+            tensor<fp16, [1, 188, 1024]> input_1119_cast_fp16 = layer_norm(axes = input_1119_axes_0, beta = encoder_module_layers_20_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_out_weight_to_fp16, x = input_1117_cast_fp16)[name = string("input_1119_cast_fp16")];
+            tensor<int32, [1]> input_1121_axes_0 = const()[name = string("input_1121_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390777216)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390779328)))];
+            tensor<fp16, [1, 188, 1024]> input_1121_cast_fp16 = layer_norm(axes = input_1121_axes_0, beta = encoder_module_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1119_cast_fp16)[name = string("input_1121_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(390781440))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393927232))))[name = string("encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393927424)))];
+            tensor<fp16, [1, 188, 4096]> linear_190_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1121_cast_fp16)[name = string("linear_190_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1125_cast_fp16 = silu(x = linear_190_cast_fp16)[name = string("input_1125_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393935680))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397081472))))[name = string("encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397081664)))];
+            tensor<fp16, [1, 188, 1024]> linear_191_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1125_cast_fp16)[name = string("linear_191_cast_fp16")];
+            fp16 var_4161_to_fp16 = const()[name = string("op_4161_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4162_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_4161_to_fp16)[name = string("op_4162_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1131_cast_fp16 = add(x = input_1119_cast_fp16, y = var_4162_cast_fp16)[name = string("input_1131_cast_fp16")];
+            tensor<int32, [1]> query_43_axes_0 = const()[name = string("query_43_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397083776)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397085888)))];
+            tensor<fp16, [1, 188, 1024]> query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = encoder_module_layers_21_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_self_att_weight_to_fp16, x = input_1131_cast_fp16)[name = string("query_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397088000))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397874496))))[name = string("encoder_module_layers_21_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397874688)))];
+            tensor<fp16, [1, 188, 1024]> linear_192_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_q_weight_to_fp16_palettized, x = query_43_cast_fp16)[name = string("linear_192_cast_fp16")];
+            tensor<int32, [4]> var_4179 = const()[name = string("op_4179"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_127_cast_fp16 = reshape(shape = var_4179, x = linear_192_cast_fp16)[name = string("q_127_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397876800))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398663296))))[name = string("encoder_module_layers_21_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398663488)))];
+            tensor<fp16, [1, 188, 1024]> linear_193_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_k_weight_to_fp16_palettized, x = query_43_cast_fp16)[name = string("linear_193_cast_fp16")];
+            tensor<int32, [4]> var_4184 = const()[name = string("op_4184"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_85_cast_fp16 = reshape(shape = var_4184, x = linear_193_cast_fp16)[name = string("k_85_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398665600))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399452096))))[name = string("encoder_module_layers_21_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399452288)))];
+            tensor<fp16, [1, 188, 1024]> linear_194_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_v_weight_to_fp16_palettized, x = query_43_cast_fp16)[name = string("linear_194_cast_fp16")];
+            tensor<int32, [4]> var_4189 = const()[name = string("op_4189"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_43_cast_fp16 = reshape(shape = var_4189, x = linear_194_cast_fp16)[name = string("v_43_cast_fp16")];
+            tensor<int32, [4]> value_47_perm_0 = const()[name = string("value_47_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399454400)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4201_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_module_layers_21_self_attn_pos_bias_u_to_fp16)[name = string("op_4201_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399456512)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4203_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_module_layers_21_self_attn_pos_bias_v_to_fp16)[name = string("op_4203_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_43_perm_0 = const()[name = string("q_with_bias_v_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_489_transpose_x_0 = const()[name = string("x_489_transpose_x_0"), val = bool(false)];
+            bool x_489_transpose_y_0 = const()[name = string("x_489_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_4205_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399458624))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399746688))))[name = string("op_4205_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_4203_cast_fp16)[name = string("transpose_165")];
+            tensor<fp16, [1, 8, 188, 375]> x_489_cast_fp16 = matmul(transpose_x = x_489_transpose_x_0, transpose_y = x_489_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = op_4205_to_fp16_palettized)[name = string("x_489_cast_fp16")];
+            tensor<int32, [8]> x_491_pad_0 = const()[name = string("x_491_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_491_mode_0 = const()[name = string("x_491_mode_0"), val = string("constant")];
+            fp16 const_298_to_fp16 = const()[name = string("const_298_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_491_cast_fp16 = pad(constant_val = const_298_to_fp16, mode = x_491_mode_0, pad = x_491_pad_0, x = x_489_cast_fp16)[name = string("x_491_cast_fp16")];
+            tensor<int32, [4]> var_4213 = const()[name = string("op_4213"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_493_cast_fp16 = reshape(shape = var_4213, x = x_491_cast_fp16)[name = string("x_493_cast_fp16")];
+            tensor<int32, [4]> var_4217_begin_0 = const()[name = string("op_4217_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4217_end_0 = const()[name = string("op_4217_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4217_end_mask_0 = const()[name = string("op_4217_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4217_cast_fp16 = slice_by_index(begin = var_4217_begin_0, end = var_4217_end_0, end_mask = var_4217_end_mask_0, x = x_493_cast_fp16)[name = string("op_4217_cast_fp16")];
+            tensor<int32, [4]> var_4218 = const()[name = string("op_4218"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_85_cast_fp16 = reshape(shape = var_4218, x = var_4217_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<int32, [4]> transpose_138_perm_0 = const()[name = string("transpose_138_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_139_perm_0 = const()[name = string("transpose_139_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = string("transpose_163")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_138 = transpose(perm = transpose_138_perm_0, x = var_4201_cast_fp16)[name = string("transpose_164")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_87_begin_0 = const()[name = string("matrix_bd_87_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_87_end_0 = const()[name = string("matrix_bd_87_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_87_end_mask_0 = const()[name = string("matrix_bd_87_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4227_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = string("op_4227_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<fp16, [1, 8, 188, 188]> _inversed_scores_85_cast_fp16 = mul(x = var_4227_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = string("_inversed_scores_85_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_87_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_15)[name = string("scores_87_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4233_cast_fp16 = softmax(axis = var_152, x = scores_87_cast_fp16)[name = string("op_4233_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1133_cast_fp16 = select(a = var_164_to_fp16, b = var_4233_cast_fp16, cond = mask_15)[name = string("input_1133_cast_fp16")];
+            bool x_495_transpose_x_0 = const()[name = string("x_495_transpose_x_0"), val = bool(false)];
+            bool x_495_transpose_y_0 = const()[name = string("x_495_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_43_cast_fp16)[name = string("transpose_162")];
+            tensor<fp16, [1, 8, 188, 128]> x_495_cast_fp16 = matmul(transpose_x = x_495_transpose_x_0, transpose_y = x_495_transpose_y_0, x = input_1133_cast_fp16, y = value_47_cast_fp16)[name = string("x_495_cast_fp16")];
+            tensor<int32, [4]> var_4237_perm_0 = const()[name = string("op_4237_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4238 = const()[name = string("op_4238"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4237_cast_fp16 = transpose(perm = var_4237_perm_0, x = x_495_cast_fp16)[name = string("transpose_161")];
+            tensor<fp16, [1, 188, 1024]> input_1135_cast_fp16 = reshape(shape = var_4238, x = var_4237_cast_fp16)[name = string("input_1135_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(399746880))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(400533376))))[name = string("encoder_module_layers_21_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(400533568)))];
+            tensor<fp16, [1, 188, 1024]> linear_196_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_out_weight_to_fp16_palettized, x = input_1135_cast_fp16)[name = string("linear_196_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1139_cast_fp16 = add(x = input_1131_cast_fp16, y = linear_196_cast_fp16)[name = string("input_1139_cast_fp16")];
+            tensor<int32, [1]> x_499_axes_0 = const()[name = string("x_499_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(400535680)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(400537792)))];
+            tensor<fp16, [1, 188, 1024]> x_499_cast_fp16 = layer_norm(axes = x_499_axes_0, beta = encoder_module_layers_21_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_conv_weight_to_fp16, x = input_1139_cast_fp16)[name = string("x_499_cast_fp16")];
+            tensor<int32, [3]> input_1141_perm_0 = const()[name = string("input_1141_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1143_pad_type_0 = const()[name = string("input_1143_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1143_strides_0 = const()[name = string("input_1143_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1143_pad_0 = const()[name = string("input_1143_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1143_dilations_0 = const()[name = string("input_1143_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1143_groups_0 = const()[name = string("input_1143_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_21_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(400539904))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402112832))))[name = string("encoder_module_layers_21_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402113024)))];
+            tensor<fp16, [1, 1024, 188]> input_1141_cast_fp16 = transpose(perm = input_1141_perm_0, x = x_499_cast_fp16)[name = string("transpose_160")];
+            tensor<fp16, [1, 2048, 188]> input_1143_cast_fp16 = conv(bias = encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_21_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1141_cast_fp16)[name = string("input_1143_cast_fp16")];
+            int32 x_501_split_num_splits_0 = const()[name = string("x_501_split_num_splits_0"), val = int32(2)];
+            int32 x_501_split_axis_0 = const()[name = string("x_501_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_501_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_501_split_cast_fp16_1 = split(axis = x_501_split_axis_0, num_splits = x_501_split_num_splits_0, x = input_1143_cast_fp16)[name = string("x_501_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_501_split_1_sigmoid_cast_fp16 = sigmoid(x = x_501_split_cast_fp16_1)[name = string("x_501_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_501_cast_fp16 = mul(x = x_501_split_cast_fp16_0, y = x_501_split_1_sigmoid_cast_fp16)[name = string("x_501_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1145_cast_fp16 = select(a = var_164_to_fp16, b = x_501_cast_fp16, cond = var_608)[name = string("input_1145_cast_fp16")];
+            tensor<int32, [6]> input_1147_pad_0 = const()[name = string("input_1147_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1147_mode_0 = const()[name = string("input_1147_mode_0"), val = string("constant")];
+            fp16 const_301_to_fp16 = const()[name = string("const_301_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1147_cast_fp16 = pad(constant_val = const_301_to_fp16, mode = input_1147_mode_0, pad = input_1147_pad_0, x = input_1145_cast_fp16)[name = string("input_1147_cast_fp16")];
+            string input_1149_pad_type_0 = const()[name = string("input_1149_pad_type_0"), val = string("valid")];
+            int32 input_1149_groups_0 = const()[name = string("input_1149_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1149_strides_0 = const()[name = string("input_1149_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1149_pad_0 = const()[name = string("input_1149_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1149_dilations_0 = const()[name = string("input_1149_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_364_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402117184))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402124160))))[name = string("const_364_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_365_to_fp16 = const()[name = string("const_365_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402124352)))];
+            tensor<fp16, [1, 1024, 188]> input_1151_cast_fp16 = conv(bias = const_365_to_fp16, dilations = input_1149_dilations_0, groups = input_1149_groups_0, pad = input_1149_pad_0, pad_type = input_1149_pad_type_0, strides = input_1149_strides_0, weight = const_364_to_fp16_palettized, x = input_1147_cast_fp16)[name = string("input_1151_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1153_cast_fp16 = silu(x = input_1151_cast_fp16)[name = string("input_1153_cast_fp16")];
+            string x_503_pad_type_0 = const()[name = string("x_503_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_503_strides_0 = const()[name = string("x_503_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_503_pad_0 = const()[name = string("x_503_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_503_dilations_0 = const()[name = string("x_503_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_503_groups_0 = const()[name = string("x_503_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402126464))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402912960))))[name = string("encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402913152)))];
+            tensor<fp16, [1, 1024, 188]> x_503_cast_fp16 = conv(bias = encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16, dilations = x_503_dilations_0, groups = x_503_groups_0, pad = x_503_pad_0, pad_type = x_503_pad_type_0, strides = x_503_strides_0, weight = encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1153_cast_fp16)[name = string("x_503_cast_fp16")];
+            tensor<int32, [3]> input_1155_perm_0 = const()[name = string("input_1155_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1155_cast_fp16 = transpose(perm = input_1155_perm_0, x = x_503_cast_fp16)[name = string("transpose_159")];
+            tensor<fp16, [1, 188, 1024]> input_1157_cast_fp16 = add(x = input_1139_cast_fp16, y = input_1155_cast_fp16)[name = string("input_1157_cast_fp16")];
+            tensor<int32, [1]> input_1159_axes_0 = const()[name = string("input_1159_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402915264)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402917376)))];
+            tensor<fp16, [1, 188, 1024]> input_1159_cast_fp16 = layer_norm(axes = input_1159_axes_0, beta = encoder_module_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1157_cast_fp16)[name = string("input_1159_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402919488))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406065280))))[name = string("encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406065472)))];
+            tensor<fp16, [1, 188, 4096]> linear_197_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1159_cast_fp16)[name = string("linear_197_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1163_cast_fp16 = silu(x = linear_197_cast_fp16)[name = string("input_1163_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406073728))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409219520))))[name = string("encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409219712)))];
+            tensor<fp16, [1, 188, 1024]> linear_198_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1163_cast_fp16)[name = string("linear_198_cast_fp16")];
+            fp16 var_4304_to_fp16 = const()[name = string("op_4304_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4305_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_4304_to_fp16)[name = string("op_4305_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1169_cast_fp16 = add(x = input_1157_cast_fp16, y = var_4305_cast_fp16)[name = string("input_1169_cast_fp16")];
+            tensor<int32, [1]> input_1171_axes_0 = const()[name = string("input_1171_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409221824)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409223936)))];
+            tensor<fp16, [1, 188, 1024]> input_1171_cast_fp16 = layer_norm(axes = input_1171_axes_0, beta = encoder_module_layers_21_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_out_weight_to_fp16, x = input_1169_cast_fp16)[name = string("input_1171_cast_fp16")];
+            tensor<int32, [1]> input_1173_axes_0 = const()[name = string("input_1173_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409226048)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409228160)))];
+            tensor<fp16, [1, 188, 1024]> input_1173_cast_fp16 = layer_norm(axes = input_1173_axes_0, beta = encoder_module_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1171_cast_fp16)[name = string("input_1173_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409230272))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(412376064))))[name = string("encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(412376256)))];
+            tensor<fp16, [1, 188, 4096]> linear_199_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1173_cast_fp16)[name = string("linear_199_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1177_cast_fp16 = silu(x = linear_199_cast_fp16)[name = string("input_1177_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(412384512))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415530304))))[name = string("encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415530496)))];
+            tensor<fp16, [1, 188, 1024]> linear_200_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1177_cast_fp16)[name = string("linear_200_cast_fp16")];
+            fp16 var_4335_to_fp16 = const()[name = string("op_4335_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4336_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_4335_to_fp16)[name = string("op_4336_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1183_cast_fp16 = add(x = input_1171_cast_fp16, y = var_4336_cast_fp16)[name = string("input_1183_cast_fp16")];
+            tensor<int32, [1]> query_45_axes_0 = const()[name = string("query_45_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415532608)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415534720)))];
+            tensor<fp16, [1, 188, 1024]> query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = encoder_module_layers_22_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_self_att_weight_to_fp16, x = input_1183_cast_fp16)[name = string("query_45_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(415536832))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416323328))))[name = string("encoder_module_layers_22_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416323520)))];
+            tensor<fp16, [1, 188, 1024]> linear_201_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_q_weight_to_fp16_palettized, x = query_45_cast_fp16)[name = string("linear_201_cast_fp16")];
+            tensor<int32, [4]> var_4353 = const()[name = string("op_4353"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_133_cast_fp16 = reshape(shape = var_4353, x = linear_201_cast_fp16)[name = string("q_133_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(416325632))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417112128))))[name = string("encoder_module_layers_22_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417112320)))];
+            tensor<fp16, [1, 188, 1024]> linear_202_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_k_weight_to_fp16_palettized, x = query_45_cast_fp16)[name = string("linear_202_cast_fp16")];
+            tensor<int32, [4]> var_4358 = const()[name = string("op_4358"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_89_cast_fp16 = reshape(shape = var_4358, x = linear_202_cast_fp16)[name = string("k_89_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417114432))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417900928))))[name = string("encoder_module_layers_22_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417901120)))];
+            tensor<fp16, [1, 188, 1024]> linear_203_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_v_weight_to_fp16_palettized, x = query_45_cast_fp16)[name = string("linear_203_cast_fp16")];
+            tensor<int32, [4]> var_4363 = const()[name = string("op_4363"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_45_cast_fp16 = reshape(shape = var_4363, x = linear_203_cast_fp16)[name = string("v_45_cast_fp16")];
+            tensor<int32, [4]> value_49_perm_0 = const()[name = string("value_49_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417903232)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4375_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_module_layers_22_self_attn_pos_bias_u_to_fp16)[name = string("op_4375_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417905344)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4377_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_module_layers_22_self_attn_pos_bias_v_to_fp16)[name = string("op_4377_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_45_perm_0 = const()[name = string("q_with_bias_v_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_511_transpose_x_0 = const()[name = string("x_511_transpose_x_0"), val = bool(false)];
+            bool x_511_transpose_y_0 = const()[name = string("x_511_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_4379_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(417907456))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418195520))))[name = string("op_4379_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_4377_cast_fp16)[name = string("transpose_158")];
+            tensor<fp16, [1, 8, 188, 375]> x_511_cast_fp16 = matmul(transpose_x = x_511_transpose_x_0, transpose_y = x_511_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = op_4379_to_fp16_palettized)[name = string("x_511_cast_fp16")];
+            tensor<int32, [8]> x_513_pad_0 = const()[name = string("x_513_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_513_mode_0 = const()[name = string("x_513_mode_0"), val = string("constant")];
+            fp16 const_308_to_fp16 = const()[name = string("const_308_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_513_cast_fp16 = pad(constant_val = const_308_to_fp16, mode = x_513_mode_0, pad = x_513_pad_0, x = x_511_cast_fp16)[name = string("x_513_cast_fp16")];
+            tensor<int32, [4]> var_4387 = const()[name = string("op_4387"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_515_cast_fp16 = reshape(shape = var_4387, x = x_513_cast_fp16)[name = string("x_515_cast_fp16")];
+            tensor<int32, [4]> var_4391_begin_0 = const()[name = string("op_4391_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4391_end_0 = const()[name = string("op_4391_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4391_end_mask_0 = const()[name = string("op_4391_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4391_cast_fp16 = slice_by_index(begin = var_4391_begin_0, end = var_4391_end_0, end_mask = var_4391_end_mask_0, x = x_515_cast_fp16)[name = string("op_4391_cast_fp16")];
+            tensor<int32, [4]> var_4392 = const()[name = string("op_4392"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_89_cast_fp16 = reshape(shape = var_4392, x = var_4391_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<int32, [4]> transpose_140_perm_0 = const()[name = string("transpose_140_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_141_perm_0 = const()[name = string("transpose_141_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = string("transpose_156")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_140 = transpose(perm = transpose_140_perm_0, x = var_4375_cast_fp16)[name = string("transpose_157")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_91_begin_0 = const()[name = string("matrix_bd_91_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_91_end_0 = const()[name = string("matrix_bd_91_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_91_end_mask_0 = const()[name = string("matrix_bd_91_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4401_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = string("op_4401_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<fp16, [1, 8, 188, 188]> _inversed_scores_89_cast_fp16 = mul(x = var_4401_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = string("_inversed_scores_89_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_91_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_15)[name = string("scores_91_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4407_cast_fp16 = softmax(axis = var_152, x = scores_91_cast_fp16)[name = string("op_4407_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1185_cast_fp16 = select(a = var_164_to_fp16, b = var_4407_cast_fp16, cond = mask_15)[name = string("input_1185_cast_fp16")];
+            bool x_517_transpose_x_0 = const()[name = string("x_517_transpose_x_0"), val = bool(false)];
+            bool x_517_transpose_y_0 = const()[name = string("x_517_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_49_cast_fp16 = transpose(perm = value_49_perm_0, x = v_45_cast_fp16)[name = string("transpose_155")];
+            tensor<fp16, [1, 8, 188, 128]> x_517_cast_fp16 = matmul(transpose_x = x_517_transpose_x_0, transpose_y = x_517_transpose_y_0, x = input_1185_cast_fp16, y = value_49_cast_fp16)[name = string("x_517_cast_fp16")];
+            tensor<int32, [4]> var_4411_perm_0 = const()[name = string("op_4411_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4412 = const()[name = string("op_4412"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4411_cast_fp16 = transpose(perm = var_4411_perm_0, x = x_517_cast_fp16)[name = string("transpose_154")];
+            tensor<fp16, [1, 188, 1024]> input_1187_cast_fp16 = reshape(shape = var_4412, x = var_4411_cast_fp16)[name = string("input_1187_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418195712))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418982208))))[name = string("encoder_module_layers_22_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418982400)))];
+            tensor<fp16, [1, 188, 1024]> linear_205_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_out_weight_to_fp16_palettized, x = input_1187_cast_fp16)[name = string("linear_205_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1191_cast_fp16 = add(x = input_1183_cast_fp16, y = linear_205_cast_fp16)[name = string("input_1191_cast_fp16")];
+            tensor<int32, [1]> x_521_axes_0 = const()[name = string("x_521_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418984512)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418986624)))];
+            tensor<fp16, [1, 188, 1024]> x_521_cast_fp16 = layer_norm(axes = x_521_axes_0, beta = encoder_module_layers_22_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_conv_weight_to_fp16, x = input_1191_cast_fp16)[name = string("x_521_cast_fp16")];
+            tensor<int32, [3]> input_1193_perm_0 = const()[name = string("input_1193_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1195_pad_type_0 = const()[name = string("input_1195_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1195_strides_0 = const()[name = string("input_1195_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1195_pad_0 = const()[name = string("input_1195_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1195_dilations_0 = const()[name = string("input_1195_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1195_groups_0 = const()[name = string("input_1195_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_22_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418988736))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420561664))))[name = string("encoder_module_layers_22_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420561856)))];
+            tensor<fp16, [1, 1024, 188]> input_1193_cast_fp16 = transpose(perm = input_1193_perm_0, x = x_521_cast_fp16)[name = string("transpose_153")];
+            tensor<fp16, [1, 2048, 188]> input_1195_cast_fp16 = conv(bias = encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_22_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1193_cast_fp16)[name = string("input_1195_cast_fp16")];
+            int32 x_523_split_num_splits_0 = const()[name = string("x_523_split_num_splits_0"), val = int32(2)];
+            int32 x_523_split_axis_0 = const()[name = string("x_523_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_523_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_523_split_cast_fp16_1 = split(axis = x_523_split_axis_0, num_splits = x_523_split_num_splits_0, x = input_1195_cast_fp16)[name = string("x_523_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_523_split_1_sigmoid_cast_fp16 = sigmoid(x = x_523_split_cast_fp16_1)[name = string("x_523_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_523_cast_fp16 = mul(x = x_523_split_cast_fp16_0, y = x_523_split_1_sigmoid_cast_fp16)[name = string("x_523_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1197_cast_fp16 = select(a = var_164_to_fp16, b = x_523_cast_fp16, cond = var_608)[name = string("input_1197_cast_fp16")];
+            tensor<int32, [6]> input_1199_pad_0 = const()[name = string("input_1199_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1199_mode_0 = const()[name = string("input_1199_mode_0"), val = string("constant")];
+            fp16 const_311_to_fp16 = const()[name = string("const_311_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1199_cast_fp16 = pad(constant_val = const_311_to_fp16, mode = input_1199_mode_0, pad = input_1199_pad_0, x = input_1197_cast_fp16)[name = string("input_1199_cast_fp16")];
+            string input_1201_pad_type_0 = const()[name = string("input_1201_pad_type_0"), val = string("valid")];
+            int32 input_1201_groups_0 = const()[name = string("input_1201_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1201_strides_0 = const()[name = string("input_1201_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1201_pad_0 = const()[name = string("input_1201_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1201_dilations_0 = const()[name = string("input_1201_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_366_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420566016))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420572992))))[name = string("const_366_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_367_to_fp16 = const()[name = string("const_367_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420573184)))];
+            tensor<fp16, [1, 1024, 188]> input_1203_cast_fp16 = conv(bias = const_367_to_fp16, dilations = input_1201_dilations_0, groups = input_1201_groups_0, pad = input_1201_pad_0, pad_type = input_1201_pad_type_0, strides = input_1201_strides_0, weight = const_366_to_fp16_palettized, x = input_1199_cast_fp16)[name = string("input_1203_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1205_cast_fp16 = silu(x = input_1203_cast_fp16)[name = string("input_1205_cast_fp16")];
+            string x_525_pad_type_0 = const()[name = string("x_525_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_525_strides_0 = const()[name = string("x_525_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_525_pad_0 = const()[name = string("x_525_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_525_dilations_0 = const()[name = string("x_525_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_525_groups_0 = const()[name = string("x_525_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(420575296))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421361792))))[name = string("encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421361984)))];
+            tensor<fp16, [1, 1024, 188]> x_525_cast_fp16 = conv(bias = encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16, dilations = x_525_dilations_0, groups = x_525_groups_0, pad = x_525_pad_0, pad_type = x_525_pad_type_0, strides = x_525_strides_0, weight = encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1205_cast_fp16)[name = string("x_525_cast_fp16")];
+            tensor<int32, [3]> input_1207_perm_0 = const()[name = string("input_1207_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1207_cast_fp16 = transpose(perm = input_1207_perm_0, x = x_525_cast_fp16)[name = string("transpose_152")];
+            tensor<fp16, [1, 188, 1024]> input_1209_cast_fp16 = add(x = input_1191_cast_fp16, y = input_1207_cast_fp16)[name = string("input_1209_cast_fp16")];
+            tensor<int32, [1]> input_1211_axes_0 = const()[name = string("input_1211_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421364096)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421366208)))];
+            tensor<fp16, [1, 188, 1024]> input_1211_cast_fp16 = layer_norm(axes = input_1211_axes_0, beta = encoder_module_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1209_cast_fp16)[name = string("input_1211_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(421368320))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(424514112))))[name = string("encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(424514304)))];
+            tensor<fp16, [1, 188, 4096]> linear_206_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1211_cast_fp16)[name = string("linear_206_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1215_cast_fp16 = silu(x = linear_206_cast_fp16)[name = string("input_1215_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(424522560))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427668352))))[name = string("encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427668544)))];
+            tensor<fp16, [1, 188, 1024]> linear_207_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1215_cast_fp16)[name = string("linear_207_cast_fp16")];
+            fp16 var_4478_to_fp16 = const()[name = string("op_4478_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4479_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_4478_to_fp16)[name = string("op_4479_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1221_cast_fp16 = add(x = input_1209_cast_fp16, y = var_4479_cast_fp16)[name = string("input_1221_cast_fp16")];
+            tensor<int32, [1]> input_1223_axes_0 = const()[name = string("input_1223_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427670656)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427672768)))];
+            tensor<fp16, [1, 188, 1024]> input_1223_cast_fp16 = layer_norm(axes = input_1223_axes_0, beta = encoder_module_layers_22_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_out_weight_to_fp16, x = input_1221_cast_fp16)[name = string("input_1223_cast_fp16")];
+            tensor<int32, [1]> input_1225_axes_0 = const()[name = string("input_1225_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427674880)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427676992)))];
+            tensor<fp16, [1, 188, 1024]> input_1225_cast_fp16 = layer_norm(axes = input_1225_axes_0, beta = encoder_module_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1223_cast_fp16)[name = string("input_1225_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427679104))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(430824896))))[name = string("encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(430825088)))];
+            tensor<fp16, [1, 188, 4096]> linear_208_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16_palettized, x = input_1225_cast_fp16)[name = string("linear_208_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1229_cast_fp16 = silu(x = linear_208_cast_fp16)[name = string("input_1229_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(430833344))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433979136))))[name = string("encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433979328)))];
+            tensor<fp16, [1, 188, 1024]> linear_209_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16_palettized, x = input_1229_cast_fp16)[name = string("linear_209_cast_fp16")];
+            fp16 var_4509_to_fp16 = const()[name = string("op_4509_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4510_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_4509_to_fp16)[name = string("op_4510_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1235_cast_fp16 = add(x = input_1223_cast_fp16, y = var_4510_cast_fp16)[name = string("input_1235_cast_fp16")];
+            tensor<int32, [1]> query_axes_0 = const()[name = string("query_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433981440)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433983552)))];
+            tensor<fp16, [1, 188, 1024]> query_cast_fp16 = layer_norm(axes = query_axes_0, beta = encoder_module_layers_23_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_self_att_weight_to_fp16, x = input_1235_cast_fp16)[name = string("query_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_q_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433985664))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434772160))))[name = string("encoder_module_layers_23_self_attn_linear_q_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434772352)))];
+            tensor<fp16, [1, 188, 1024]> linear_210_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_q_weight_to_fp16_palettized, x = query_cast_fp16)[name = string("linear_210_cast_fp16")];
+            tensor<int32, [4]> var_4527 = const()[name = string("op_4527"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_139_cast_fp16 = reshape(shape = var_4527, x = linear_210_cast_fp16)[name = string("q_139_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_k_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434774464))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435560960))))[name = string("encoder_module_layers_23_self_attn_linear_k_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435561152)))];
+            tensor<fp16, [1, 188, 1024]> linear_211_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_k_weight_to_fp16_palettized, x = query_cast_fp16)[name = string("linear_211_cast_fp16")];
+            tensor<int32, [4]> var_4532 = const()[name = string("op_4532"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_93_cast_fp16 = reshape(shape = var_4532, x = linear_211_cast_fp16)[name = string("k_93_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_v_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435563264))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436349760))))[name = string("encoder_module_layers_23_self_attn_linear_v_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436349952)))];
+            tensor<fp16, [1, 188, 1024]> linear_212_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_v_weight_to_fp16_palettized, x = query_cast_fp16)[name = string("linear_212_cast_fp16")];
+            tensor<int32, [4]> var_4537 = const()[name = string("op_4537"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_cast_fp16 = reshape(shape = var_4537, x = linear_212_cast_fp16)[name = string("v_cast_fp16")];
+            tensor<int32, [4]> value_perm_0 = const()[name = string("value_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436352064)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4549_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_module_layers_23_self_attn_pos_bias_u_to_fp16)[name = string("op_4549_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436354176)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4551_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_module_layers_23_self_attn_pos_bias_v_to_fp16)[name = string("op_4551_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_perm_0 = const()[name = string("q_with_bias_v_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            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<fp16, [1, 8, 128, 375]> op_4553_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436356288))), lut = tensor<fp16, [1, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436644352))))[name = string("op_4553_to_fp16_palettized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4551_cast_fp16)[name = string("transpose_151")];
+            tensor<fp16, [1, 8, 188, 375]> x_533_cast_fp16 = matmul(transpose_x = x_533_transpose_x_0, transpose_y = x_533_transpose_y_0, x = q_with_bias_v_cast_fp16, y = op_4553_to_fp16_palettized)[name = string("x_533_cast_fp16")];
+            tensor<int32, [8]> x_535_pad_0 = const()[name = string("x_535_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_535_mode_0 = const()[name = string("x_535_mode_0"), val = string("constant")];
+            fp16 const_318_to_fp16 = const()[name = string("const_318_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_535_cast_fp16 = pad(constant_val = const_318_to_fp16, mode = x_535_mode_0, pad = x_535_pad_0, x = x_533_cast_fp16)[name = string("x_535_cast_fp16")];
+            tensor<int32, [4]> var_4561 = const()[name = string("op_4561"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_537_cast_fp16 = reshape(shape = var_4561, x = x_535_cast_fp16)[name = string("x_537_cast_fp16")];
+            tensor<int32, [4]> var_4565_begin_0 = const()[name = string("op_4565_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4565_end_0 = const()[name = string("op_4565_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4565_end_mask_0 = const()[name = string("op_4565_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4565_cast_fp16 = slice_by_index(begin = var_4565_begin_0, end = var_4565_end_0, end_mask = var_4565_end_mask_0, x = x_537_cast_fp16)[name = string("op_4565_cast_fp16")];
+            tensor<int32, [4]> var_4566 = const()[name = string("op_4566"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_93_cast_fp16 = reshape(shape = var_4566, x = var_4565_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<int32, [4]> transpose_142_perm_0 = const()[name = string("transpose_142_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_143_perm_0 = const()[name = string("transpose_143_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = string("transpose_149")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_142 = transpose(perm = transpose_142_perm_0, x = var_4549_cast_fp16)[name = string("transpose_150")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_end_0 = const()[name = string("matrix_bd_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4575_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = string("op_4575_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<fp16, [1, 8, 188, 188]> _inversed_scores_93_cast_fp16 = mul(x = var_4575_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = string("_inversed_scores_93_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_15)[name = string("scores_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4581_cast_fp16 = softmax(axis = var_152, x = scores_cast_fp16)[name = string("op_4581_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1237_cast_fp16 = select(a = var_164_to_fp16, b = var_4581_cast_fp16, cond = mask_15)[name = string("input_1237_cast_fp16")];
+            bool x_539_transpose_x_0 = const()[name = string("x_539_transpose_x_0"), val = bool(false)];
+            bool x_539_transpose_y_0 = const()[name = string("x_539_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = string("transpose_148")];
+            tensor<fp16, [1, 8, 188, 128]> x_539_cast_fp16 = matmul(transpose_x = x_539_transpose_x_0, transpose_y = x_539_transpose_y_0, x = input_1237_cast_fp16, y = value_cast_fp16)[name = string("x_539_cast_fp16")];
+            tensor<int32, [4]> var_4585_perm_0 = const()[name = string("op_4585_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4586 = const()[name = string("op_4586"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4585_cast_fp16 = transpose(perm = var_4585_perm_0, x = x_539_cast_fp16)[name = string("transpose_147")];
+            tensor<fp16, [1, 188, 1024]> input_1239_cast_fp16 = reshape(shape = var_4586, x = var_4585_cast_fp16)[name = string("input_1239_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_out_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436644544))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437431040))))[name = string("encoder_module_layers_23_self_attn_linear_out_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437431232)))];
+            tensor<fp16, [1, 188, 1024]> linear_214_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_out_weight_to_fp16_palettized, x = input_1239_cast_fp16)[name = string("linear_214_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1243_cast_fp16 = add(x = input_1235_cast_fp16, y = linear_214_cast_fp16)[name = string("input_1243_cast_fp16")];
+            tensor<int32, [1]> x_543_axes_0 = const()[name = string("x_543_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437433344)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437435456)))];
+            tensor<fp16, [1, 188, 1024]> x_543_cast_fp16 = layer_norm(axes = x_543_axes_0, beta = encoder_module_layers_23_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_conv_weight_to_fp16, x = input_1243_cast_fp16)[name = string("x_543_cast_fp16")];
+            tensor<int32, [3]> input_1245_perm_0 = const()[name = string("input_1245_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1247_pad_type_0 = const()[name = string("input_1247_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1247_strides_0 = const()[name = string("input_1247_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1247_pad_0 = const()[name = string("input_1247_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1247_dilations_0 = const()[name = string("input_1247_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1247_groups_0 = const()[name = string("input_1247_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_23_conv_pointwise_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437437568))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439010496))))[name = string("encoder_module_layers_23_conv_pointwise_conv1_weight_to_fp16_palettized")];
+            tensor<fp16, [2048]> encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439010688)))];
+            tensor<fp16, [1, 1024, 188]> input_1245_cast_fp16 = transpose(perm = input_1245_perm_0, x = x_543_cast_fp16)[name = string("transpose_146")];
+            tensor<fp16, [1, 2048, 188]> input_1247_cast_fp16 = conv(bias = encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_23_conv_pointwise_conv1_weight_to_fp16_palettized, x = input_1245_cast_fp16)[name = string("input_1247_cast_fp16")];
+            int32 x_545_split_num_splits_0 = const()[name = string("x_545_split_num_splits_0"), val = int32(2)];
+            int32 x_545_split_axis_0 = const()[name = string("x_545_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_545_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_545_split_cast_fp16_1 = split(axis = x_545_split_axis_0, num_splits = x_545_split_num_splits_0, x = input_1247_cast_fp16)[name = string("x_545_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_545_split_1_sigmoid_cast_fp16 = sigmoid(x = x_545_split_cast_fp16_1)[name = string("x_545_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_545_cast_fp16 = mul(x = x_545_split_cast_fp16_0, y = x_545_split_1_sigmoid_cast_fp16)[name = string("x_545_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1249_cast_fp16 = select(a = var_164_to_fp16, b = x_545_cast_fp16, cond = var_608)[name = string("input_1249_cast_fp16")];
+            tensor<int32, [6]> input_1251_pad_0 = const()[name = string("input_1251_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1251_mode_0 = const()[name = string("input_1251_mode_0"), val = string("constant")];
+            fp16 const_321_to_fp16 = const()[name = string("const_321_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1251_cast_fp16 = pad(constant_val = const_321_to_fp16, mode = input_1251_mode_0, pad = input_1251_pad_0, x = input_1249_cast_fp16)[name = string("input_1251_cast_fp16")];
+            string input_1253_pad_type_0 = const()[name = string("input_1253_pad_type_0"), val = string("valid")];
+            int32 input_1253_groups_0 = const()[name = string("input_1253_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1253_strides_0 = const()[name = string("input_1253_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1253_pad_0 = const()[name = string("input_1253_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1253_dilations_0 = const()[name = string("input_1253_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_368_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439014848))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439021824))))[name = string("const_368_to_fp16_palettized")];
+            tensor<fp16, [1024]> const_369_to_fp16 = const()[name = string("const_369_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439022016)))];
+            tensor<fp16, [1, 1024, 188]> input_1255_cast_fp16 = conv(bias = const_369_to_fp16, dilations = input_1253_dilations_0, groups = input_1253_groups_0, pad = input_1253_pad_0, pad_type = input_1253_pad_type_0, strides = input_1253_strides_0, weight = const_368_to_fp16_palettized, x = input_1251_cast_fp16)[name = string("input_1255_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1257_cast_fp16 = silu(x = input_1255_cast_fp16)[name = string("input_1257_cast_fp16")];
+            string x_547_pad_type_0 = const()[name = string("x_547_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_547_strides_0 = const()[name = string("x_547_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_547_pad_0 = const()[name = string("x_547_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_547_dilations_0 = const()[name = string("x_547_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_547_groups_0 = const()[name = string("x_547_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439024128))), lut = tensor<fp16, [1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439810624))))[name = string("encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439810816)))];
+            tensor<fp16, [1, 1024, 188]> x_547_cast_fp16 = conv(bias = encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16, dilations = x_547_dilations_0, groups = x_547_groups_0, pad = x_547_pad_0, pad_type = x_547_pad_type_0, strides = x_547_strides_0, weight = encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16_palettized, x = input_1257_cast_fp16)[name = string("x_547_cast_fp16")];
+            tensor<int32, [3]> input_1259_perm_0 = const()[name = string("input_1259_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1259_cast_fp16 = transpose(perm = input_1259_perm_0, x = x_547_cast_fp16)[name = string("transpose_145")];
+            tensor<fp16, [1, 188, 1024]> input_1261_cast_fp16 = add(x = input_1243_cast_fp16, y = input_1259_cast_fp16)[name = string("input_1261_cast_fp16")];
+            tensor<int32, [1]> input_1263_axes_0 = const()[name = string("input_1263_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439812928)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439815040)))];
+            tensor<fp16, [1, 188, 1024]> input_1263_cast_fp16 = layer_norm(axes = input_1263_axes_0, beta = encoder_module_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1261_cast_fp16)[name = string("input_1263_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439817152))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442962944))))[name = string("encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16_palettized")];
+            tensor<fp16, [4096]> encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442963136)))];
+            tensor<fp16, [1, 188, 4096]> linear_215_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16_palettized, x = input_1263_cast_fp16)[name = string("linear_215_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1267_cast_fp16 = silu(x = linear_215_cast_fp16)[name = string("input_1267_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442971392))), lut = tensor<fp16, [1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(446117184))))[name = string("encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16_palettized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(446117376)))];
+            tensor<fp16, [1, 188, 1024]> linear_216_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16_palettized, x = input_1267_cast_fp16)[name = string("linear_216_cast_fp16")];
+            fp16 var_4652_to_fp16 = const()[name = string("op_4652_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4653_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_4652_to_fp16)[name = string("op_4653_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_cast_fp16 = add(x = input_1261_cast_fp16, y = var_4653_cast_fp16)[name = string("input_cast_fp16")];
+            tensor<int32, [1]> audio_signal_axes_0 = const()[name = string("audio_signal_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(446119488)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(446121600)))];
+            tensor<fp16, [1, 188, 1024]> audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = encoder_module_layers_23_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = string("audio_signal_cast_fp16")];
+            tensor<int32, [3]> obj_3_perm_0 = const()[name = string("obj_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp16, [1, 1024, 188]> obj_3_cast_fp16 = transpose(perm = obj_3_perm_0, x = audio_signal_cast_fp16)[name = string("transpose_144")];
+            tensor<fp32, [1, 1024, 188]> encoder = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_0")];
+        } -> (encoder, encoder_length);
+}
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/weights/weight.bin b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..f9ee8dd419974cc75183e57df9da3aa2808a476f
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/6bit_palettize/parakeet_ctc_mel_encoder.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac7e95814789c229224f8971023134c2175a2634685014a25bff941539870953
+size 446123712
diff --git a/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/analytics/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/analytics/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..777b9471688525fc542e670300ae19c795b50f89
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+++ b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
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+size 243
diff --git a/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..6aa32acadea3fba87630f5bd3e3ef2b00deea938
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:279d53e635ca4b9da206fbaad927a63def64702e23913df3c0b02e8dcf4edf15
+size 463
diff --git a/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/metadata.json b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..1689890683f99f383af3dbf613fa0b4ac166d078
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/metadata.json
@@ -0,0 +1,70 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "shortDescription" : "Parakeet CTC decoder head (encoder -> log_probs)",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "log_probs",
+        "type" : "MultiArray"
+      }
+    ],
+    "storagePrecision" : "Mixed (Float16, Int4)",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.transpose" : 1,
+      "Ios18.log" : 1,
+      "Ios18.cast" : 2,
+      "Ios18.conv" : 1,
+      "Ios18.softmax" : 1,
+      "Ios18.constexprBlockwiseShiftScale" : 1
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 1024 × 1...188",
+        "shapeRange" : "[[1, 1], [1024, 1024], [1, 188]]",
+        "formattedType" : "MultiArray (Float32 1 × 1024 × 1)",
+        "type" : "MultiArray",
+        "shape" : "[1, 1024, 1]",
+        "name" : "encoder",
+        "shortDescription" : ""
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-02-27",
+      "com.github.apple.coremltools.source" : "torch==2.10.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "parakeet_ctc_decoder",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/model.mil b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..84d0f067156d805839ff8cd0224c635747971a1d
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/model.mil
@@ -0,0 +1,24 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 1024, ?]> encoder) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"encoder", [1, 1024, 1]}}), ("RangeDims", {{"encoder", [[1, 1], [1024, 1024], [1, 188]]}})))] {
+            int32 var_4 = const()[name = string("op_4"), val = int32(-1)];
+            string var_18_pad_type_0 = const()[name = string("op_18_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_18_strides_0 = const()[name = string("op_18_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_18_pad_0 = const()[name = string("op_18_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_18_dilations_0 = const()[name = string("op_18_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_18_groups_0 = const()[name = string("op_18_groups_0"), val = int32(1)];
+            string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1025, 1024, 1]> module_decoder_layers_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1025, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor<fp16, [1025, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524928))))[name = string("module_decoder_layers_0_weight_to_fp16_quantized")];
+            tensor<fp16, [1025]> module_decoder_layers_0_bias_to_fp16 = const()[name = string("module_decoder_layers_0_bias_to_fp16"), val = tensor<fp16, [1025]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(590592)))];
+            tensor<fp16, [1, 1024, ?]> encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_1")];
+            tensor<fp16, [1, 1025, ?]> var_18_cast_fp16 = conv(bias = module_decoder_layers_0_bias_to_fp16, dilations = var_18_dilations_0, groups = var_18_groups_0, pad = var_18_pad_0, pad_type = var_18_pad_type_0, strides = var_18_strides_0, weight = module_decoder_layers_0_weight_to_fp16_quantized, x = encoder_to_fp16)[name = string("op_18_cast_fp16")];
+            tensor<int32, [3]> input_perm_0 = const()[name = string("input_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, ?, 1025]> input_cast_fp16 = transpose(perm = input_perm_0, x = var_18_cast_fp16)[name = string("transpose_0")];
+            tensor<fp16, [1, ?, 1025]> out_objects_softmax_cast_fp16 = softmax(axis = var_4, x = input_cast_fp16)[name = string("out_objects_softmax_cast_fp16")];
+            fp32 out_objects_epsilon_0 = const()[name = string("out_objects_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [1, ?, 1025]> out_objects_cast_fp16 = log(epsilon = out_objects_epsilon_0, x = out_objects_softmax_cast_fp16)[name = string("out_objects_cast_fp16")];
+            string out_objects_cast_fp16_to_fp32_dtype_0 = const()[name = string("out_objects_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [1, ?, 1025]> log_probs = cast(dtype = out_objects_cast_fp16_to_fp32_dtype_0, x = out_objects_cast_fp16)[name = string("cast_0")];
+        } -> (log_probs);
+}
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/weights/weight.bin b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..2d452e147ba7f33f75ffc4f9d34cd674eabad1d4
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_decoder.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f0e1e88ab1787121c69b1f505d5789f627d95336cd0b0a69079fb5ddec59950b
+size 592706
diff --git a/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..efb2a3977b43f10c89e8e50e85a1413e8d459f0a
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3e1ff6f1f3c5a8ccac117bd945c0bf771bf7e298df6c9a5363ddbf7d5ee8cd0
+size 243
diff --git a/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..240b14395a3e6d23f31b329208d46994e0aa48ea
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e49899cd89d6a7d8137a46cd91e7a9b266c26649e3fc0ff6a45f2d352e4bc4f6
+size 496
diff --git a/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/metadata.json b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..e5f35eba9b31649ba31b9486507466b03988f120
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/metadata.json
@@ -0,0 +1,117 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "shortDescription" : "Parakeet CTC Mel+Encoder (15.0s window)",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 1024 × 188)",
+        "shortDescription" : "",
+        "shape" : "[1, 1024, 188]",
+        "name" : "encoder",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "encoder_length",
+        "type" : "MultiArray"
+      }
+    ],
+    "storagePrecision" : "Mixed (Float16, Int32, Int4)",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.less" : 6,
+      "Stack" : 1,
+      "Ios18.conv" : 79,
+      "Ios18.mul" : 107,
+      "Ios18.cast" : 12,
+      "Ios18.layerNorm" : 120,
+      "Ios18.log" : 1,
+      "Ios18.equal" : 1,
+      "Ios18.floorDiv" : 4,
+      "Ios16.reduceSum" : 4,
+      "Ios18.logicalNot" : 3,
+      "Ios18.reshape" : 147,
+      "Pad" : 49,
+      "Ios18.concat" : 1,
+      "Ios18.add" : 180,
+      "Ios18.realDiv" : 3,
+      "Ios18.greaterEqual" : 1,
+      "Ios18.notEqual" : 1,
+      "Ios18.relu" : 3,
+      "Ios18.sub" : 7,
+      "Ios18.matmul" : 73,
+      "Ios18.silu" : 72,
+      "Ios18.expandDims" : 25,
+      "Ios18.linear" : 193,
+      "Ios18.sigmoid" : 24,
+      "Ios18.sliceByIndex" : 51,
+      "Ios18.transpose" : 172,
+      "Split" : 24,
+      "Ios18.sqrt" : 1,
+      "Ios18.softmax" : 24,
+      "Ios18.pow" : 2,
+      "Select" : 78,
+      "Ios18.constexprBlockwiseShiftScale" : 267,
+      "Ios18.logicalAnd" : 2,
+      "Tile" : 9
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 240000)",
+        "shortDescription" : "",
+        "shape" : "[1, 240000]",
+        "name" : "audio_signal",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "audio_length",
+        "type" : "MultiArray"
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-02-27",
+      "com.github.apple.coremltools.source" : "torch==2.10.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "parakeet_ctc_mel_encoder",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/model.mil b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..fe1533bc8e39a76ce016ac833c1046d2d84cee01
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/model.mil
@@ -0,0 +1,3838 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}})]
+{
+    func main<ios18>(tensor<int32, [1]> audio_length, tensor<fp32, [1, 240000]> audio_signal) {
+            int32 var_20 = const()[name = string("op_20"), val = int32(0)];
+            int32 var_21 = const()[name = string("op_21"), val = int32(160)];
+            int32 var_22 = const()[name = string("op_22"), val = int32(1)];
+            int32 var_32 = const()[name = string("op_32"), val = int32(512)];
+            tensor<int32, [1]> var_33 = add(x = audio_length, y = var_32)[name = string("op_33")];
+            int32 var_34 = const()[name = string("op_34"), val = int32(512)];
+            tensor<int32, [1]> var_35 = sub(x = var_33, y = var_34)[name = string("op_35")];
+            tensor<int32, [1]> floor_div_0 = floor_div(x = var_35, y = var_21)[name = string("floor_div_0")];
+            tensor<bool, [1]> var_38 = equal(x = audio_length, y = var_20)[name = string("op_38")];
+            tensor<int32, [1]> var_39 = const()[name = string("op_39"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1]> seq_len = select(a = var_39, b = floor_div_0, cond = var_38)[name = string("seq_len")];
+            tensor<int32, [1, 240000]> var_43 = const()[name = string("op_43"), val = tensor<int32, [1, 240000]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<int32, [1]> var_44_axes_0 = const()[name = string("op_44_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1]> var_44 = expand_dims(axes = var_44_axes_0, x = audio_length)[name = string("op_44")];
+            tensor<bool, [1, 240000]> timemask = less(x = var_43, y = var_44)[name = string("timemask")];
+            tensor<int32, [2]> var_47_begin_0 = const()[name = string("op_47_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_47_end_0 = const()[name = string("op_47_end_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<bool, [2]> var_47_end_mask_0 = const()[name = string("op_47_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<bool, [2]> var_47_squeeze_mask_0 = const()[name = string("op_47_squeeze_mask_0"), val = tensor<bool, [2]>([false, true])];
+            string audio_signal_to_fp16_dtype_0 = const()[name = string("audio_signal_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 240000]> audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = string("cast_11")];
+            tensor<fp16, [1]> var_47_cast_fp16 = slice_by_index(begin = var_47_begin_0, end = var_47_end_0, end_mask = var_47_end_mask_0, squeeze_mask = var_47_squeeze_mask_0, x = audio_signal_to_fp16)[name = string("op_47_cast_fp16")];
+            tensor<int32, [1]> var_48_axes_0 = const()[name = string("op_48_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_48_cast_fp16 = expand_dims(axes = var_48_axes_0, x = var_47_cast_fp16)[name = string("op_48_cast_fp16")];
+            tensor<int32, [2]> var_50_begin_0 = const()[name = string("op_50_begin_0"), val = tensor<int32, [2]>([0, 1])];
+            tensor<int32, [2]> var_50_end_0 = const()[name = string("op_50_end_0"), val = tensor<int32, [2]>([1, 240000])];
+            tensor<bool, [2]> var_50_end_mask_0 = const()[name = string("op_50_end_mask_0"), val = tensor<bool, [2]>([true, true])];
+            tensor<fp16, [1, 239999]> var_50_cast_fp16 = slice_by_index(begin = var_50_begin_0, end = var_50_end_0, end_mask = var_50_end_mask_0, x = audio_signal_to_fp16)[name = string("op_50_cast_fp16")];
+            tensor<int32, [2]> var_52_begin_0 = const()[name = string("op_52_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_52_end_0 = const()[name = string("op_52_end_0"), val = tensor<int32, [2]>([1, 239999])];
+            tensor<bool, [2]> var_52_end_mask_0 = const()[name = string("op_52_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<fp16, [1, 239999]> var_52_cast_fp16 = slice_by_index(begin = var_52_begin_0, end = var_52_end_0, end_mask = var_52_end_mask_0, x = audio_signal_to_fp16)[name = string("op_52_cast_fp16")];
+            fp16 var_53_to_fp16 = const()[name = string("op_53_to_fp16"), val = fp16(0x1.f0cp-1)];
+            tensor<fp16, [1, 239999]> var_54_cast_fp16 = mul(x = var_52_cast_fp16, y = var_53_to_fp16)[name = string("op_54_cast_fp16")];
+            tensor<fp16, [1, 239999]> var_55_cast_fp16 = sub(x = var_50_cast_fp16, y = var_54_cast_fp16)[name = string("op_55_cast_fp16")];
+            bool x_3_interleave_0 = const()[name = string("x_3_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 240000]> x_3_cast_fp16 = concat(axis = var_22, interleave = x_3_interleave_0, values = (var_48_cast_fp16, var_55_cast_fp16))[name = string("x_3_cast_fp16")];
+            tensor<bool, [1, 240000]> var_58 = logical_not(x = timemask)[name = string("op_58")];
+            fp16 var_16_to_fp16 = const()[name = string("op_16_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 240000]> input_1_cast_fp16 = select(a = var_16_to_fp16, b = x_3_cast_fp16, cond = var_58)[name = string("input_1_cast_fp16")];
+            tensor<int32, [3]> var_63 = const()[name = string("op_63"), val = tensor<int32, [3]>([1, 1, 240000])];
+            tensor<fp16, [1, 1, 240000]> input_3_cast_fp16 = reshape(shape = var_63, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")];
+            tensor<int32, [6]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 256])];
+            string input_5_mode_0 = const()[name = string("input_5_mode_0"), val = string("constant")];
+            fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1, 240512]> input_5_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")];
+            tensor<int32, [2]> var_69 = const()[name = string("op_69"), val = tensor<int32, [2]>([1, 240512])];
+            tensor<fp16, [1, 240512]> input_7_cast_fp16 = reshape(shape = var_69, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")];
+            tensor<int32, [1]> expand_dims_10 = const()[name = string("expand_dims_10"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [1]> expand_dims_11_axes_0 = const()[name = string("expand_dims_11_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 240512]> expand_dims_11_cast_fp16 = expand_dims(axes = expand_dims_11_axes_0, x = input_7_cast_fp16)[name = string("expand_dims_11_cast_fp16")];
+            string conv_0_pad_type_0 = const()[name = string("conv_0_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv_0_pad_0 = const()[name = string("conv_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_0_dilations_0 = const()[name = string("conv_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 conv_0_groups_0 = const()[name = string("conv_0_groups_0"), val = int32(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_8_to_fp16 = const()[name = string("expand_dims_8_to_fp16"), val = tensor<fp16, [257, 1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(960128)))];
+            tensor<fp16, [1, 257, 1501]> 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_10, weight = expand_dims_8_to_fp16, x = expand_dims_11_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<int32, [2]> conv_1_pad_0 = const()[name = string("conv_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_1_dilations_0 = const()[name = string("conv_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 conv_1_groups_0 = const()[name = string("conv_1_groups_0"), val = int32(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_9_to_fp16 = const()[name = string("expand_dims_9_to_fp16"), val = tensor<fp16, [257, 1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1223360)))];
+            tensor<fp16, [1, 257, 1501]> 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_10, weight = expand_dims_9_to_fp16, x = expand_dims_11_cast_fp16)[name = string("conv_1_cast_fp16")];
+            int32 stack_0_axis_0 = const()[name = string("stack_0_axis_0"), val = int32(-1)];
+            tensor<fp16, [1, 257, 1501, 2]> 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_13_promoted_to_fp16 = const()[name = string("op_13_promoted_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1, 257, 1501, 2]> var_73_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_13_promoted_to_fp16)[name = string("op_73_cast_fp16")];
+            tensor<int32, [1]> var_75_axes_0 = const()[name = string("op_75_axes_0"), val = tensor<int32, [1]>([-1])];
+            bool var_75_keep_dims_0 = const()[name = string("op_75_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 257, 1501]> var_75_cast_fp16 = reduce_sum(axes = var_75_axes_0, keep_dims = var_75_keep_dims_0, x = var_73_cast_fp16)[name = string("op_75_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<fp16, [1, 80, 257]> const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = tensor<fp16, [1, 80, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1486592)))];
+            tensor<fp16, [1, 80, 1501]> x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = const_9_to_fp16, y = var_75_cast_fp16)[name = string("x_13_cast_fp16")];
+            fp16 var_82_to_fp16 = const()[name = string("op_82_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [1, 80, 1501]> var_83_cast_fp16 = add(x = x_13_cast_fp16, y = var_82_to_fp16)[name = string("op_83_cast_fp16")];
+            fp32 x_15_epsilon_0 = const()[name = string("x_15_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [1, 80, 1501]> x_15_cast_fp16 = log(epsilon = x_15_epsilon_0, x = var_83_cast_fp16)[name = string("x_15_cast_fp16")];
+            tensor<int32, [1, 1501]> var_88 = const()[name = string("op_88"), val = tensor<int32, [1, 1501]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1527808)))];
+            tensor<int32, [1]> var_91_axes_0 = const()[name = string("op_91_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1]> var_91 = expand_dims(axes = var_91_axes_0, x = seq_len)[name = string("op_91")];
+            tensor<bool, [1, 1501]> valid_mask = less(x = var_88, y = var_91)[name = string("valid_mask")];
+            tensor<int32, [1]> var_93_axes_0 = const()[name = string("op_93_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_93 = expand_dims(axes = var_93_axes_0, x = valid_mask)[name = string("op_93")];
+            tensor<int32, [3]> var_93_after_broadcast_reps_0 = const()[name = string("op_93_after_broadcast_reps_0"), val = tensor<int32, [3]>([1, 80, 1])];
+            tensor<bool, [1, 80, 1501]> var_93_after_broadcast = tile(reps = var_93_after_broadcast_reps_0, x = var_93)[name = string("op_93_after_broadcast")];
+            tensor<fp16, [1, 80, 1501]> var_16_after_broadcast_to_fp16 = const()[name = string("op_16_after_broadcast_to_fp16"), val = tensor<fp16, [1, 80, 1501]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533888)))];
+            tensor<fp16, [1, 80, 1501]> var_94_cast_fp16 = select(a = x_15_cast_fp16, b = var_16_after_broadcast_to_fp16, cond = var_93_after_broadcast)[name = string("op_94_cast_fp16")];
+            tensor<int32, [1]> x_mean_numerator_axes_0 = const()[name = string("x_mean_numerator_axes_0"), val = tensor<int32, [1]>([2])];
+            bool x_mean_numerator_keep_dims_0 = const()[name = string("x_mean_numerator_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 80]> x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_94_cast_fp16)[name = string("x_mean_numerator_cast_fp16")];
+            tensor<int32, [1]> x_mean_denominator_axes_0 = const()[name = string("x_mean_denominator_axes_0"), val = tensor<int32, [1]>([1])];
+            bool x_mean_denominator_keep_dims_0 = const()[name = string("x_mean_denominator_keep_dims_0"), val = bool(false)];
+            string cast_2_to_fp16_dtype_0 = const()[name = string("cast_2_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 1501]> valid_mask_to_fp16 = cast(dtype = cast_2_to_fp16_dtype_0, x = valid_mask)[name = string("cast_10")];
+            tensor<fp16, [1]> x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = string("x_mean_denominator_cast_fp16")];
+            tensor<int32, [1]> var_99_axes_0 = const()[name = string("op_99_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_99_cast_fp16 = expand_dims(axes = var_99_axes_0, x = x_mean_denominator_cast_fp16)[name = string("op_99_cast_fp16")];
+            tensor<fp16, [1, 80]> x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_99_cast_fp16)[name = string("x_mean_cast_fp16")];
+            tensor<int32, [1]> var_102_axes_0 = const()[name = string("op_102_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 80, 1]> var_102_cast_fp16 = expand_dims(axes = var_102_axes_0, x = x_mean_cast_fp16)[name = string("op_102_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> var_103_cast_fp16 = sub(x = x_15_cast_fp16, y = var_102_cast_fp16)[name = string("op_103_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> var_104_cast_fp16 = select(a = var_103_cast_fp16, b = var_16_after_broadcast_to_fp16, cond = var_93_after_broadcast)[name = string("op_104_cast_fp16")];
+            fp16 var_13_promoted_1_to_fp16 = const()[name = string("op_13_promoted_1_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1, 80, 1501]> var_105_cast_fp16 = pow(x = var_104_cast_fp16, y = var_13_promoted_1_to_fp16)[name = string("op_105_cast_fp16")];
+            tensor<int32, [1]> var_107_axes_0 = const()[name = string("op_107_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_107_keep_dims_0 = const()[name = string("op_107_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 80]> var_107_cast_fp16 = reduce_sum(axes = var_107_axes_0, keep_dims = var_107_keep_dims_0, x = var_105_cast_fp16)[name = string("op_107_cast_fp16")];
+            fp16 var_109_to_fp16 = const()[name = string("op_109_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1]> var_110_cast_fp16 = sub(x = var_99_cast_fp16, y = var_109_to_fp16)[name = string("op_110_cast_fp16")];
+            tensor<fp16, [1, 80]> var_111_cast_fp16 = real_div(x = var_107_cast_fp16, y = var_110_cast_fp16)[name = string("op_111_cast_fp16")];
+            tensor<fp16, [1, 80]> x_std_1_cast_fp16 = sqrt(x = var_111_cast_fp16)[name = string("x_std_1_cast_fp16")];
+            tensor<bool, [1, 80]> var_113_cast_fp16 = not_equal(x = x_std_1_cast_fp16, y = x_std_1_cast_fp16)[name = string("op_113_cast_fp16")];
+            tensor<fp16, [1, 80]> x_std_3_cast_fp16 = select(a = var_16_to_fp16, b = x_std_1_cast_fp16, cond = var_113_cast_fp16)[name = string("x_std_3_cast_fp16")];
+            fp16 var_7_to_fp16 = const()[name = string("op_7_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 80]> x_std_cast_fp16 = add(x = x_std_3_cast_fp16, y = var_7_to_fp16)[name = string("x_std_cast_fp16")];
+            tensor<int32, [1]> var_118_axes_0 = const()[name = string("op_118_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 80, 1]> var_118_cast_fp16 = expand_dims(axes = var_118_axes_0, x = x_std_cast_fp16)[name = string("op_118_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> x_17_cast_fp16 = real_div(x = var_103_cast_fp16, y = var_118_cast_fp16)[name = string("x_17_cast_fp16")];
+            tensor<bool, [1, 1501]> mask_3 = greater_equal(x = var_88, y = var_91)[name = string("mask_3")];
+            tensor<int32, [1]> var_127_axes_0 = const()[name = string("op_127_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_127 = expand_dims(axes = var_127_axes_0, x = mask_3)[name = string("op_127")];
+            tensor<fp16, [1, 80, 1501]> processed_signal_cast_fp16 = select(a = var_16_to_fp16, b = x_17_cast_fp16, cond = var_127)[name = string("processed_signal_cast_fp16")];
+            int32 var_152 = const()[name = string("op_152"), val = int32(-1)];
+            tensor<int32, [3]> x_19_perm_0 = const()[name = string("x_19_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> tensor_1_axes_0 = const()[name = string("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1501, 80]> x_19_cast_fp16 = transpose(perm = x_19_perm_0, x = processed_signal_cast_fp16)[name = string("transpose_315")];
+            tensor<fp16, [1, 1, 1501, 80]> tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_19_cast_fp16)[name = string("tensor_1_cast_fp16")];
+            tensor<int32, [1]> var_242_axes_0 = const()[name = string("op_242_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 1501, 1]> var_242 = expand_dims(axes = var_242_axes_0, x = valid_mask)[name = string("op_242")];
+            tensor<int32, [3]> var_244_reps_0 = const()[name = string("op_244_reps_0"), val = tensor<int32, [3]>([1, 1, 80])];
+            tensor<bool, [1, 1501, 80]> var_244 = tile(reps = var_244_reps_0, x = var_242)[name = string("op_244")];
+            tensor<int32, [1]> var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_5_to_fp16_dtype_0 = const()[name = string("mask_5_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 1501, 80]> var_244_to_fp16 = cast(dtype = mask_5_to_fp16_dtype_0, x = var_244)[name = string("cast_9")];
+            tensor<fp16, [1, 1, 1501, 80]> var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = var_244_to_fp16)[name = string("op_250_cast_fp16")];
+            tensor<fp16, [1, 1, 1501, 80]> input_9_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_250_cast_fp16)[name = string("input_9_cast_fp16")];
+            string tensor_3_pad_type_0 = const()[name = string("tensor_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_3_pad_0 = const()[name = string("tensor_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_3_strides_0 = const()[name = string("tensor_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> tensor_3_dilations_0 = const()[name = string("tensor_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_3_groups_0 = const()[name = string("tensor_3_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_0_weight_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_0_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1774144)))];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_0_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1778816)))];
+            tensor<fp16, [1, 256, 751, 40]> tensor_3_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_0_weight_to_fp16, x = input_9_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_261_promoted_to_fp16 = const()[name = string("op_261_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> seq_len_to_fp16 = cast(dtype = current_lengths_1_to_fp16_dtype_0, x = seq_len)[name = string("cast_8")];
+            tensor<fp16, [1]> var_262_cast_fp16 = add(x = seq_len_to_fp16, y = var_261_promoted_to_fp16)[name = string("op_262_cast_fp16")];
+            fp16 var_263_promoted_to_fp16 = const()[name = string("op_263_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_264_cast_fp16 = add(x = var_262_cast_fp16, y = var_263_promoted_to_fp16)[name = string("op_264_cast_fp16")];
+            fp16 var_265_promoted_to_fp16 = const()[name = string("op_265_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_266_cast_fp16 = sub(x = var_264_cast_fp16, y = var_265_promoted_to_fp16)[name = string("op_266_cast_fp16")];
+            fp16 var_154_promoted_to_fp16 = const()[name = string("op_154_promoted_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_1_cast_fp16 = floor_div(x = var_266_cast_fp16, y = var_154_promoted_to_fp16)[name = string("floor_div_1_cast_fp16")];
+            fp16 var_268_promoted_to_fp16 = const()[name = string("op_268_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_3_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_268_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<int32, [1, 751]> expand_dims_4 = const()[name = string("expand_dims_4"), val = tensor<int32, [1, 751]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1779392)))];
+            tensor<int32, [1]> var_277_axes_0 = const()[name = string("op_277_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_3_cast_fp16_to_int32 = cast(dtype = lengths_19_dtype_0, x = current_lengths_3_cast_fp16)[name = string("cast_7")];
+            tensor<int32, [1, 1]> var_277 = expand_dims(axes = var_277_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = string("op_277")];
+            tensor<bool, [1, 751]> time_mask_3 = less(x = expand_dims_4, y = var_277)[name = string("time_mask_3")];
+            tensor<int32, [1]> var_279_axes_0 = const()[name = string("op_279_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 751, 1]> var_279 = expand_dims(axes = var_279_axes_0, x = time_mask_3)[name = string("op_279")];
+            tensor<int32, [3]> var_281_reps_0 = const()[name = string("op_281_reps_0"), val = tensor<int32, [3]>([1, 1, 40])];
+            tensor<bool, [1, 751, 40]> var_281 = tile(reps = var_281_reps_0, x = var_279)[name = string("op_281")];
+            tensor<int32, [1]> var_287_axes_0 = const()[name = string("op_287_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_7_to_fp16_dtype_0 = const()[name = string("mask_7_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 751, 40]> var_281_to_fp16 = cast(dtype = mask_7_to_fp16_dtype_0, x = var_281)[name = string("cast_6")];
+            tensor<fp16, [1, 1, 751, 40]> var_287_cast_fp16 = expand_dims(axes = var_287_axes_0, x = var_281_to_fp16)[name = string("op_287_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_3_reps_0 = const()[name = string("expanded_mask_3_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 751, 40]> expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_287_cast_fp16)[name = string("expanded_mask_3_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> input_11_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_11_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> tensor_5_cast_fp16 = relu(x = input_11_cast_fp16)[name = string("tensor_5_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> input_13_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_13_cast_fp16")];
+            string tensor_7_pad_type_0 = const()[name = string("tensor_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_7_pad_0 = const()[name = string("tensor_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_7_strides_0 = const()[name = string("tensor_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 tensor_7_groups_0 = const()[name = string("tensor_7_groups_0"), val = int32(256)];
+            tensor<int32, [2]> tensor_7_dilations_0 = const()[name = string("tensor_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_2_weight_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_2_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1782464)))];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_2_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1787136)))];
+            tensor<fp16, [1, 256, 376, 20]> tensor_7_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_2_weight_to_fp16, x = input_13_cast_fp16)[name = string("tensor_7_cast_fp16")];
+            fp16 var_307_promoted_to_fp16 = const()[name = string("op_307_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_308_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_307_promoted_to_fp16)[name = string("op_308_cast_fp16")];
+            fp16 var_309_promoted_to_fp16 = const()[name = string("op_309_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_310_cast_fp16 = add(x = var_308_cast_fp16, y = var_309_promoted_to_fp16)[name = string("op_310_cast_fp16")];
+            fp16 var_311_promoted_to_fp16 = const()[name = string("op_311_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_312_cast_fp16 = sub(x = var_310_cast_fp16, y = var_311_promoted_to_fp16)[name = string("op_312_cast_fp16")];
+            fp16 var_154_promoted_1_to_fp16 = const()[name = string("op_154_promoted_1_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_2_cast_fp16 = floor_div(x = var_312_cast_fp16, y = var_154_promoted_1_to_fp16)[name = string("floor_div_2_cast_fp16")];
+            fp16 var_314_promoted_to_fp16 = const()[name = string("op_314_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_5_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_314_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<int32, [1, 376]> expand_dims_5 = const()[name = string("expand_dims_5"), val = tensor<int32, [1, 376]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1787712)))];
+            tensor<int32, [1]> var_323_axes_0 = const()[name = string("op_323_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_5_cast_fp16_to_int32 = cast(dtype = lengths_21_dtype_0, x = current_lengths_5_cast_fp16)[name = string("cast_5")];
+            tensor<int32, [1, 1]> var_323 = expand_dims(axes = var_323_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = string("op_323")];
+            tensor<bool, [1, 376]> time_mask_5 = less(x = expand_dims_5, y = var_323)[name = string("time_mask_5")];
+            tensor<int32, [1]> var_325_axes_0 = const()[name = string("op_325_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 376, 1]> var_325 = expand_dims(axes = var_325_axes_0, x = time_mask_5)[name = string("op_325")];
+            tensor<int32, [3]> var_327_reps_0 = const()[name = string("op_327_reps_0"), val = tensor<int32, [3]>([1, 1, 20])];
+            tensor<bool, [1, 376, 20]> var_327 = tile(reps = var_327_reps_0, x = var_325)[name = string("op_327")];
+            tensor<int32, [1]> var_333_axes_0 = const()[name = string("op_333_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_9_to_fp16_dtype_0 = const()[name = string("mask_9_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 376, 20]> var_327_to_fp16 = cast(dtype = mask_9_to_fp16_dtype_0, x = var_327)[name = string("cast_4")];
+            tensor<fp16, [1, 1, 376, 20]> var_333_cast_fp16 = expand_dims(axes = var_333_axes_0, x = var_327_to_fp16)[name = string("op_333_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_7_reps_0 = const()[name = string("expanded_mask_7_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 376, 20]> expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_333_cast_fp16)[name = string("expanded_mask_7_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_15_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_15_cast_fp16")];
+            string tensor_9_pad_type_0 = const()[name = string("tensor_9_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> tensor_9_strides_0 = const()[name = string("tensor_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> tensor_9_pad_0 = const()[name = string("tensor_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> tensor_9_dilations_0 = const()[name = string("tensor_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_9_groups_0 = const()[name = string("tensor_9_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 1, 1]> encoder_module_pre_encode_conv_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1789312))), scale = tensor<fp16, [256, 8, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1822144))))[name = string("encoder_module_pre_encode_conv_3_weight_to_fp16_quantized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_3_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_3_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1826304)))];
+            tensor<fp16, [1, 256, 376, 20]> tensor_9_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_3_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("tensor_9_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_17_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_17_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> tensor_11_cast_fp16 = relu(x = input_17_cast_fp16)[name = string("tensor_11_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_19_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_19_cast_fp16")];
+            string tensor_13_pad_type_0 = const()[name = string("tensor_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_13_pad_0 = const()[name = string("tensor_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_13_strides_0 = const()[name = string("tensor_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 tensor_13_groups_0 = const()[name = string("tensor_13_groups_0"), val = int32(256)];
+            tensor<int32, [2]> tensor_13_dilations_0 = const()[name = string("tensor_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_5_weight_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_5_weight_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1826880)))];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_5_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_5_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1831552)))];
+            tensor<fp16, [1, 256, 188, 10]> tensor_13_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_5_weight_to_fp16, x = input_19_cast_fp16)[name = string("tensor_13_cast_fp16")];
+            fp16 var_368_promoted_to_fp16 = const()[name = string("op_368_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_369_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_368_promoted_to_fp16)[name = string("op_369_cast_fp16")];
+            fp16 var_370_promoted_to_fp16 = const()[name = string("op_370_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_371_cast_fp16 = add(x = var_369_cast_fp16, y = var_370_promoted_to_fp16)[name = string("op_371_cast_fp16")];
+            fp16 var_372_promoted_to_fp16 = const()[name = string("op_372_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_373_cast_fp16 = sub(x = var_371_cast_fp16, y = var_372_promoted_to_fp16)[name = string("op_373_cast_fp16")];
+            fp16 var_154_promoted_2_to_fp16 = const()[name = string("op_154_promoted_2_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_3_cast_fp16 = floor_div(x = var_373_cast_fp16, y = var_154_promoted_2_to_fp16)[name = string("floor_div_3_cast_fp16")];
+            fp16 var_375_promoted_to_fp16 = const()[name = string("op_375_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_cast_fp16 = add(x = floor_div_3_cast_fp16, y = var_375_promoted_to_fp16)[name = string("current_lengths_cast_fp16")];
+            string lengths_dtype_0 = const()[name = string("lengths_dtype_0"), val = string("int32")];
+            tensor<int32, [1, 188]> expand_dims_6 = const()[name = string("expand_dims_6"), val = tensor<int32, [1, 188]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1832128)))];
+            tensor<int32, [1]> var_384_axes_0 = const()[name = string("op_384_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_cast_fp16_to_int32 = cast(dtype = lengths_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_3")];
+            tensor<int32, [1, 1]> var_384 = expand_dims(axes = var_384_axes_0, x = current_lengths_cast_fp16_to_int32)[name = string("op_384")];
+            tensor<bool, [1, 188]> time_mask = less(x = expand_dims_6, y = var_384)[name = string("time_mask")];
+            tensor<int32, [1]> var_386_axes_0 = const()[name = string("op_386_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 188, 1]> var_386 = expand_dims(axes = var_386_axes_0, x = time_mask)[name = string("op_386")];
+            tensor<int32, [3]> var_388_reps_0 = const()[name = string("op_388_reps_0"), val = tensor<int32, [3]>([1, 1, 10])];
+            tensor<bool, [1, 188, 10]> var_388 = tile(reps = var_388_reps_0, x = var_386)[name = string("op_388")];
+            tensor<int32, [1]> var_394_axes_0 = const()[name = string("op_394_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_11_to_fp16_dtype_0 = const()[name = string("mask_11_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 188, 10]> var_388_to_fp16 = cast(dtype = mask_11_to_fp16_dtype_0, x = var_388)[name = string("cast_2")];
+            tensor<fp16, [1, 1, 188, 10]> var_394_cast_fp16 = expand_dims(axes = var_394_axes_0, x = var_388_to_fp16)[name = string("op_394_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_13_reps_0 = const()[name = string("expanded_mask_13_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 188, 10]> expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_394_cast_fp16)[name = string("expanded_mask_13_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> input_21_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_21_cast_fp16")];
+            string tensor_15_pad_type_0 = const()[name = string("tensor_15_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> tensor_15_strides_0 = const()[name = string("tensor_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> tensor_15_pad_0 = const()[name = string("tensor_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> tensor_15_dilations_0 = const()[name = string("tensor_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_15_groups_0 = const()[name = string("tensor_15_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 1, 1]> encoder_module_pre_encode_conv_6_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1832960))), scale = tensor<fp16, [256, 8, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1865792))))[name = string("encoder_module_pre_encode_conv_6_weight_to_fp16_quantized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_6_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_6_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1869952)))];
+            tensor<fp16, [1, 256, 188, 10]> tensor_15_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_6_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("tensor_15_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> input_23_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_23_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> tensor_cast_fp16 = relu(x = input_23_cast_fp16)[name = string("tensor_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> x_21_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("x_21_cast_fp16")];
+            tensor<int32, [4]> var_428_perm_0 = const()[name = string("op_428_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_429 = const()[name = string("op_429"), val = tensor<int32, [3]>([1, 188, -1])];
+            tensor<fp16, [1, 188, 256, 10]> var_428_cast_fp16 = transpose(perm = var_428_perm_0, x = x_21_cast_fp16)[name = string("transpose_314")];
+            tensor<fp16, [1, 188, 2560]> input_25_cast_fp16 = reshape(shape = var_429, x = var_428_cast_fp16)[name = string("input_25_cast_fp16")];
+            tensor<fp16, [1024, 2560]> encoder_module_pre_encode_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1870528))), scale = tensor<fp16, [1024, 80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3181312))))[name = string("encoder_module_pre_encode_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_pre_encode_out_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3345216)))];
+            tensor<fp16, [1, 188, 1024]> linear_0_cast_fp16 = linear(bias = encoder_module_pre_encode_out_bias_to_fp16, weight = encoder_module_pre_encode_out_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_0_cast_fp16")];
+            string padding_length_dtype_0 = const()[name = string("padding_length_dtype_0"), val = string("int32")];
+            fp16 var_440_to_fp16 = const()[name = string("op_440_to_fp16"), val = fp16(0x1p+5)];
+            tensor<fp16, [1, 188, 1024]> x_23_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_440_to_fp16)[name = string("x_23_cast_fp16")];
+            tensor<int32, [1]> var_469_axes_0 = const()[name = string("op_469_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<int32, [1]> encoder_length = cast(dtype = padding_length_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_1")];
+            tensor<int32, [1, 1]> var_469 = expand_dims(axes = var_469_axes_0, x = encoder_length)[name = string("op_469")];
+            tensor<bool, [1, 188]> pad_mask_1 = less(x = expand_dims_6, y = var_469)[name = string("pad_mask_1")];
+            tensor<int32, [1]> var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188]> var_471 = expand_dims(axes = var_471_axes_0, x = pad_mask_1)[name = string("op_471")];
+            tensor<int32, [3]> var_472 = const()[name = string("op_472"), val = tensor<int32, [3]>([1, 188, 1])];
+            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask_1 = tile(reps = var_472, x = var_471)[name = string("pad_mask_for_att_mask_1")];
+            tensor<int32, [3]> var_474_perm_0 = const()[name = string("op_474_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<bool, [1, 188, 188]> var_474 = transpose(perm = var_474_perm_0, x = pad_mask_for_att_mask_1)[name = string("transpose_313")];
+            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_474)[name = string("pad_mask_for_att_mask")];
+            tensor<bool, [1, 188, 188]> const_81 = const()[name = string("const_81"), val = tensor<bool, [1, 188, 188]>([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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true, true, true, true, true, true]]])];
+            tensor<bool, [1, 188, 188]> att_mask = logical_and(x = pad_mask_for_att_mask, y = const_81)[name = string("att_mask")];
+            tensor<bool, [1, 188, 188]> mask_13 = logical_not(x = att_mask)[name = string("mask_13")];
+            tensor<bool, [1, 188]> pad_mask = logical_not(x = pad_mask_1)[name = string("pad_mask")];
+            tensor<int32, [1]> input_29_axes_0 = const()[name = string("input_29_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3347328)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3349440)))];
+            fp16 var_166_to_fp16 = const()[name = string("op_166_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 188, 1024]> input_29_cast_fp16 = layer_norm(axes = input_29_axes_0, beta = encoder_module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_feed_forward1_weight_to_fp16, x = x_23_cast_fp16)[name = string("input_29_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3351552))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5448768))))[name = string("encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5710976)))];
+            tensor<fp16, [1, 188, 4096]> linear_1_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("linear_1_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_33_cast_fp16 = silu(x = linear_1_cast_fp16)[name = string("input_33_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5719232))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7816448))))[name = string("encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8078656)))];
+            tensor<fp16, [1, 188, 1024]> linear_2_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_2_cast_fp16")];
+            fp16 var_507_to_fp16 = const()[name = string("op_507_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_508_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_507_to_fp16)[name = string("op_508_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_39_cast_fp16 = add(x = x_23_cast_fp16, y = var_508_cast_fp16)[name = string("input_39_cast_fp16")];
+            tensor<int32, [1]> query_1_axes_0 = const()[name = string("query_1_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8080768)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8082880)))];
+            tensor<fp16, [1, 188, 1024]> query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = encoder_module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_self_att_weight_to_fp16, x = input_39_cast_fp16)[name = string("query_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8084992))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8609344))))[name = string("encoder_module_layers_0_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8674944)))];
+            tensor<fp16, [1, 188, 1024]> linear_3_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_q_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = string("linear_3_cast_fp16")];
+            tensor<int32, [4]> var_525 = const()[name = string("op_525"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_1_cast_fp16 = reshape(shape = var_525, x = linear_3_cast_fp16)[name = string("q_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8677056))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9201408))))[name = string("encoder_module_layers_0_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9267008)))];
+            tensor<fp16, [1, 188, 1024]> linear_4_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_k_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = string("linear_4_cast_fp16")];
+            tensor<int32, [4]> var_530 = const()[name = string("op_530"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_1_cast_fp16 = reshape(shape = var_530, x = linear_4_cast_fp16)[name = string("k_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9269120))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9793472))))[name = string("encoder_module_layers_0_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9859072)))];
+            tensor<fp16, [1, 188, 1024]> linear_5_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_v_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = string("linear_5_cast_fp16")];
+            tensor<int32, [4]> var_535 = const()[name = string("op_535"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_1_cast_fp16 = reshape(shape = var_535, x = linear_5_cast_fp16)[name = string("v_1_cast_fp16")];
+            tensor<int32, [4]> value_5_perm_0 = const()[name = string("value_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9861184)))];
+            tensor<fp16, [1, 188, 8, 128]> var_547_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_module_layers_0_self_attn_pos_bias_u_to_fp16)[name = string("op_547_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9863296)))];
+            tensor<fp16, [1, 188, 8, 128]> var_549_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_module_layers_0_self_attn_pos_bias_v_to_fp16)[name = string("op_549_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_1_perm_0 = const()[name = string("q_with_bias_v_1_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_27_transpose_x_0 = const()[name = string("x_27_transpose_x_0"), val = bool(false)];
+            bool x_27_transpose_y_0 = const()[name = string("x_27_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_551_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9865408))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10057472))))[name = string("op_551_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_549_cast_fp16)[name = string("transpose_312")];
+            tensor<fp16, [1, 8, 188, 375]> x_27_cast_fp16 = matmul(transpose_x = x_27_transpose_x_0, transpose_y = x_27_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = op_551_to_fp16_quantized)[name = string("x_27_cast_fp16")];
+            tensor<int32, [8]> x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_29_mode_0 = const()[name = string("x_29_mode_0"), val = string("constant")];
+            fp16 const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_29_cast_fp16 = pad(constant_val = const_88_to_fp16, mode = x_29_mode_0, pad = x_29_pad_0, x = x_27_cast_fp16)[name = string("x_29_cast_fp16")];
+            tensor<int32, [4]> var_559 = const()[name = string("op_559"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_31_cast_fp16 = reshape(shape = var_559, x = x_29_cast_fp16)[name = string("x_31_cast_fp16")];
+            tensor<int32, [4]> var_563_begin_0 = const()[name = string("op_563_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_563_end_0 = const()[name = string("op_563_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_563_end_mask_0 = const()[name = string("op_563_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_563_cast_fp16 = slice_by_index(begin = var_563_begin_0, end = var_563_end_0, end_mask = var_563_end_mask_0, x = x_31_cast_fp16)[name = string("op_563_cast_fp16")];
+            tensor<int32, [4]> var_564 = const()[name = string("op_564"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_1_cast_fp16 = reshape(shape = var_564, x = var_563_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<int32, [4]> transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = string("transpose_310")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_96 = transpose(perm = transpose_96_perm_0, x = var_547_cast_fp16)[name = string("transpose_311")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_3_end_0 = const()[name = string("matrix_bd_3_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_573_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = string("op_573_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<fp16, [1, 8, 188, 188]> _inversed_scores_1_cast_fp16 = mul(x = var_573_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")];
+            tensor<int32, [1]> mask_15_axes_0 = const()[name = string("mask_15_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188, 188]> mask_15 = expand_dims(axes = mask_15_axes_0, x = mask_13)[name = string("mask_15")];
+            fp16 var_163_to_fp16 = const()[name = string("op_163_to_fp16"), val = fp16(-0x1.388p+13)];
+            tensor<fp16, [1, 8, 188, 188]> scores_3_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_15)[name = string("scores_3_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_579_cast_fp16 = softmax(axis = var_152, x = scores_3_cast_fp16)[name = string("op_579_cast_fp16")];
+            fp16 var_164_to_fp16 = const()[name = string("op_164_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 188]> input_41_cast_fp16 = select(a = var_164_to_fp16, b = var_579_cast_fp16, cond = mask_15)[name = string("input_41_cast_fp16")];
+            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<fp16, [1, 8, 188, 128]> value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_1_cast_fp16)[name = string("transpose_309")];
+            tensor<fp16, [1, 8, 188, 128]> x_33_cast_fp16 = matmul(transpose_x = x_33_transpose_x_0, transpose_y = x_33_transpose_y_0, x = input_41_cast_fp16, y = value_5_cast_fp16)[name = string("x_33_cast_fp16")];
+            tensor<int32, [4]> var_583_perm_0 = const()[name = string("op_583_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_584 = const()[name = string("op_584"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_583_cast_fp16 = transpose(perm = var_583_perm_0, x = x_33_cast_fp16)[name = string("transpose_308")];
+            tensor<fp16, [1, 188, 1024]> input_43_cast_fp16 = reshape(shape = var_584, x = var_583_cast_fp16)[name = string("input_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10060544))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10584896))))[name = string("encoder_module_layers_0_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10650496)))];
+            tensor<fp16, [1, 188, 1024]> linear_7_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_out_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("linear_7_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_47_cast_fp16 = add(x = input_39_cast_fp16, y = linear_7_cast_fp16)[name = string("input_47_cast_fp16")];
+            tensor<int32, [1]> x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10652608)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10654720)))];
+            tensor<fp16, [1, 188, 1024]> x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, beta = encoder_module_layers_0_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_conv_weight_to_fp16, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")];
+            tensor<int32, [3]> input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10656832))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11705472))))[name = string("encoder_module_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11836608)))];
+            tensor<fp16, [1, 1024, 188]> input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_37_cast_fp16)[name = string("transpose_307")];
+            tensor<fp16, [1, 2048, 188]> input_51_cast_fp16 = conv(bias = encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")];
+            int32 x_39_split_num_splits_0 = const()[name = string("x_39_split_num_splits_0"), val = int32(2)];
+            int32 x_39_split_axis_0 = const()[name = string("x_39_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_39_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_39_split_cast_fp16_1 = split(axis = x_39_split_axis_0, num_splits = x_39_split_num_splits_0, x = input_51_cast_fp16)[name = string("x_39_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_39_split_1_sigmoid_cast_fp16 = sigmoid(x = x_39_split_cast_fp16_1)[name = string("x_39_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_39_cast_fp16 = mul(x = x_39_split_cast_fp16_0, y = x_39_split_1_sigmoid_cast_fp16)[name = string("x_39_cast_fp16")];
+            tensor<int32, [1]> var_608_axes_0 = const()[name = string("op_608_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188]> var_608 = expand_dims(axes = var_608_axes_0, x = pad_mask)[name = string("op_608")];
+            tensor<fp16, [1, 1024, 188]> input_53_cast_fp16 = select(a = var_164_to_fp16, b = x_39_cast_fp16, cond = var_608)[name = string("input_53_cast_fp16")];
+            tensor<int32, [6]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("constant")];
+            fp16 const_91_to_fp16 = const()[name = string("const_91_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_55_cast_fp16 = pad(constant_val = const_91_to_fp16, mode = input_55_mode_0, pad = input_55_pad_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")];
+            string input_57_pad_type_0 = const()[name = string("input_57_pad_type_0"), val = string("valid")];
+            int32 input_57_groups_0 = const()[name = string("input_57_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_57_strides_0 = const()[name = string("input_57_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_57_dilations_0 = const()[name = string("input_57_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_322_to_fp16 = const()[name = string("const_322_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11840768)))];
+            tensor<fp16, [1024]> const_323_to_fp16 = const()[name = string("const_323_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11859264)))];
+            tensor<fp16, [1, 1024, 188]> input_59_cast_fp16 = conv(bias = const_323_to_fp16, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_322_to_fp16, x = input_55_cast_fp16)[name = string("input_59_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_61_cast_fp16 = silu(x = input_59_cast_fp16)[name = string("input_61_cast_fp16")];
+            string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11861376))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12385728))))[name = string("encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12451328)))];
+            tensor<fp16, [1, 1024, 188]> x_41_cast_fp16 = conv(bias = encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_61_cast_fp16)[name = string("x_41_cast_fp16")];
+            tensor<int32, [3]> input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_41_cast_fp16)[name = string("transpose_306")];
+            tensor<fp16, [1, 188, 1024]> input_65_cast_fp16 = add(x = input_47_cast_fp16, y = input_63_cast_fp16)[name = string("input_65_cast_fp16")];
+            tensor<int32, [1]> input_67_axes_0 = const()[name = string("input_67_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12453440)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12455552)))];
+            tensor<fp16, [1, 188, 1024]> input_67_cast_fp16 = layer_norm(axes = input_67_axes_0, beta = encoder_module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12457664))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14554880))))[name = string("encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14817088)))];
+            tensor<fp16, [1, 188, 4096]> linear_8_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16_quantized, x = input_67_cast_fp16)[name = string("linear_8_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_71_cast_fp16 = silu(x = linear_8_cast_fp16)[name = string("input_71_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14825344))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16922560))))[name = string("encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17184768)))];
+            tensor<fp16, [1, 188, 1024]> linear_9_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_9_cast_fp16")];
+            fp16 var_650_to_fp16 = const()[name = string("op_650_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_651_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_650_to_fp16)[name = string("op_651_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_77_cast_fp16 = add(x = input_65_cast_fp16, y = var_651_cast_fp16)[name = string("input_77_cast_fp16")];
+            tensor<int32, [1]> input_79_axes_0 = const()[name = string("input_79_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17186880)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17188992)))];
+            tensor<fp16, [1, 188, 1024]> input_79_cast_fp16 = layer_norm(axes = input_79_axes_0, beta = encoder_module_layers_0_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_out_weight_to_fp16, x = input_77_cast_fp16)[name = string("input_79_cast_fp16")];
+            tensor<int32, [1]> input_81_axes_0 = const()[name = string("input_81_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17191104)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17193216)))];
+            tensor<fp16, [1, 188, 1024]> input_81_cast_fp16 = layer_norm(axes = input_81_axes_0, beta = encoder_module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_79_cast_fp16)[name = string("input_81_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17195328))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19292544))))[name = string("encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19554752)))];
+            tensor<fp16, [1, 188, 4096]> linear_10_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16_quantized, x = input_81_cast_fp16)[name = string("linear_10_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_85_cast_fp16 = silu(x = linear_10_cast_fp16)[name = string("input_85_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19563008))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21660224))))[name = string("encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21922432)))];
+            tensor<fp16, [1, 188, 1024]> linear_11_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_11_cast_fp16")];
+            fp16 var_681_to_fp16 = const()[name = string("op_681_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_682_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_681_to_fp16)[name = string("op_682_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_91_cast_fp16 = add(x = input_79_cast_fp16, y = var_682_cast_fp16)[name = string("input_91_cast_fp16")];
+            tensor<int32, [1]> query_3_axes_0 = const()[name = string("query_3_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21924544)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21926656)))];
+            tensor<fp16, [1, 188, 1024]> query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = encoder_module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_self_att_weight_to_fp16, x = input_91_cast_fp16)[name = string("query_3_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21928768))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22453120))))[name = string("encoder_module_layers_1_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22518720)))];
+            tensor<fp16, [1, 188, 1024]> linear_12_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_q_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = string("linear_12_cast_fp16")];
+            tensor<int32, [4]> var_699 = const()[name = string("op_699"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_7_cast_fp16 = reshape(shape = var_699, x = linear_12_cast_fp16)[name = string("q_7_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22520832))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23045184))))[name = string("encoder_module_layers_1_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23110784)))];
+            tensor<fp16, [1, 188, 1024]> linear_13_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_k_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = string("linear_13_cast_fp16")];
+            tensor<int32, [4]> var_704 = const()[name = string("op_704"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_5_cast_fp16 = reshape(shape = var_704, x = linear_13_cast_fp16)[name = string("k_5_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23112896))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23637248))))[name = string("encoder_module_layers_1_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23702848)))];
+            tensor<fp16, [1, 188, 1024]> linear_14_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_v_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = string("linear_14_cast_fp16")];
+            tensor<int32, [4]> var_709 = const()[name = string("op_709"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_3_cast_fp16 = reshape(shape = var_709, x = linear_14_cast_fp16)[name = string("v_3_cast_fp16")];
+            tensor<int32, [4]> value_7_perm_0 = const()[name = string("value_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23704960)))];
+            tensor<fp16, [1, 188, 8, 128]> var_721_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_module_layers_1_self_attn_pos_bias_u_to_fp16)[name = string("op_721_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23707072)))];
+            tensor<fp16, [1, 188, 8, 128]> var_723_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_module_layers_1_self_attn_pos_bias_v_to_fp16)[name = string("op_723_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_3_perm_0 = const()[name = string("q_with_bias_v_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_49_transpose_x_0 = const()[name = string("x_49_transpose_x_0"), val = bool(false)];
+            bool x_49_transpose_y_0 = const()[name = string("x_49_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_725_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23709184))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23901248))))[name = string("op_725_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_723_cast_fp16)[name = string("transpose_305")];
+            tensor<fp16, [1, 8, 188, 375]> x_49_cast_fp16 = matmul(transpose_x = x_49_transpose_x_0, transpose_y = x_49_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = op_725_to_fp16_quantized)[name = string("x_49_cast_fp16")];
+            tensor<int32, [8]> x_51_pad_0 = const()[name = string("x_51_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_51_mode_0 = const()[name = string("x_51_mode_0"), val = string("constant")];
+            fp16 const_98_to_fp16 = const()[name = string("const_98_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_51_cast_fp16 = pad(constant_val = const_98_to_fp16, mode = x_51_mode_0, pad = x_51_pad_0, x = x_49_cast_fp16)[name = string("x_51_cast_fp16")];
+            tensor<int32, [4]> var_733 = const()[name = string("op_733"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_53_cast_fp16 = reshape(shape = var_733, x = x_51_cast_fp16)[name = string("x_53_cast_fp16")];
+            tensor<int32, [4]> var_737_begin_0 = const()[name = string("op_737_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_737_end_0 = const()[name = string("op_737_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_737_end_mask_0 = const()[name = string("op_737_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_737_cast_fp16 = slice_by_index(begin = var_737_begin_0, end = var_737_end_0, end_mask = var_737_end_mask_0, x = x_53_cast_fp16)[name = string("op_737_cast_fp16")];
+            tensor<int32, [4]> var_738 = const()[name = string("op_738"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_5_cast_fp16 = reshape(shape = var_738, x = var_737_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<int32, [4]> transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = string("transpose_303")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_98 = transpose(perm = transpose_98_perm_0, x = var_721_cast_fp16)[name = string("transpose_304")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_7_end_0 = const()[name = string("matrix_bd_7_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_747_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = string("op_747_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<fp16, [1, 8, 188, 188]> _inversed_scores_5_cast_fp16 = mul(x = var_747_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_7_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_15)[name = string("scores_7_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_753_cast_fp16 = softmax(axis = var_152, x = scores_7_cast_fp16)[name = string("op_753_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_93_cast_fp16 = select(a = var_164_to_fp16, b = var_753_cast_fp16, cond = mask_15)[name = string("input_93_cast_fp16")];
+            bool x_55_transpose_x_0 = const()[name = string("x_55_transpose_x_0"), val = bool(false)];
+            bool x_55_transpose_y_0 = const()[name = string("x_55_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_3_cast_fp16)[name = string("transpose_302")];
+            tensor<fp16, [1, 8, 188, 128]> x_55_cast_fp16 = matmul(transpose_x = x_55_transpose_x_0, transpose_y = x_55_transpose_y_0, x = input_93_cast_fp16, y = value_7_cast_fp16)[name = string("x_55_cast_fp16")];
+            tensor<int32, [4]> var_757_perm_0 = const()[name = string("op_757_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_758 = const()[name = string("op_758"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_757_cast_fp16 = transpose(perm = var_757_perm_0, x = x_55_cast_fp16)[name = string("transpose_301")];
+            tensor<fp16, [1, 188, 1024]> input_95_cast_fp16 = reshape(shape = var_758, x = var_757_cast_fp16)[name = string("input_95_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23904320))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24428672))))[name = string("encoder_module_layers_1_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24494272)))];
+            tensor<fp16, [1, 188, 1024]> linear_16_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_out_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_16_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_99_cast_fp16 = add(x = input_91_cast_fp16, y = linear_16_cast_fp16)[name = string("input_99_cast_fp16")];
+            tensor<int32, [1]> x_59_axes_0 = const()[name = string("x_59_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24496384)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24498496)))];
+            tensor<fp16, [1, 188, 1024]> x_59_cast_fp16 = layer_norm(axes = x_59_axes_0, beta = encoder_module_layers_1_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_conv_weight_to_fp16, x = input_99_cast_fp16)[name = string("x_59_cast_fp16")];
+            tensor<int32, [3]> input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24500608))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25549248))))[name = string("encoder_module_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25680384)))];
+            tensor<fp16, [1, 1024, 188]> input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_59_cast_fp16)[name = string("transpose_300")];
+            tensor<fp16, [1, 2048, 188]> input_103_cast_fp16 = conv(bias = encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")];
+            int32 x_61_split_num_splits_0 = const()[name = string("x_61_split_num_splits_0"), val = int32(2)];
+            int32 x_61_split_axis_0 = const()[name = string("x_61_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_61_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_61_split_cast_fp16_1 = split(axis = x_61_split_axis_0, num_splits = x_61_split_num_splits_0, x = input_103_cast_fp16)[name = string("x_61_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_61_split_1_sigmoid_cast_fp16 = sigmoid(x = x_61_split_cast_fp16_1)[name = string("x_61_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_61_cast_fp16 = mul(x = x_61_split_cast_fp16_0, y = x_61_split_1_sigmoid_cast_fp16)[name = string("x_61_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_105_cast_fp16 = select(a = var_164_to_fp16, b = x_61_cast_fp16, cond = var_608)[name = string("input_105_cast_fp16")];
+            tensor<int32, [6]> input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("constant")];
+            fp16 const_101_to_fp16 = const()[name = string("const_101_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_107_cast_fp16 = pad(constant_val = const_101_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")];
+            string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("valid")];
+            int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_324_to_fp16 = const()[name = string("const_324_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25684544)))];
+            tensor<fp16, [1024]> const_325_to_fp16 = const()[name = string("const_325_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25703040)))];
+            tensor<fp16, [1, 1024, 188]> input_111_cast_fp16 = conv(bias = const_325_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_324_to_fp16, x = input_107_cast_fp16)[name = string("input_111_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_113_cast_fp16 = silu(x = input_111_cast_fp16)[name = string("input_113_cast_fp16")];
+            string x_63_pad_type_0 = const()[name = string("x_63_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_63_strides_0 = const()[name = string("x_63_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_63_pad_0 = const()[name = string("x_63_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_63_dilations_0 = const()[name = string("x_63_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_63_groups_0 = const()[name = string("x_63_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25705152))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26229504))))[name = string("encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26295104)))];
+            tensor<fp16, [1, 1024, 188]> x_63_cast_fp16 = conv(bias = encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16, dilations = x_63_dilations_0, groups = x_63_groups_0, pad = x_63_pad_0, pad_type = x_63_pad_type_0, strides = x_63_strides_0, weight = encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_113_cast_fp16)[name = string("x_63_cast_fp16")];
+            tensor<int32, [3]> input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_63_cast_fp16)[name = string("transpose_299")];
+            tensor<fp16, [1, 188, 1024]> input_117_cast_fp16 = add(x = input_99_cast_fp16, y = input_115_cast_fp16)[name = string("input_117_cast_fp16")];
+            tensor<int32, [1]> input_119_axes_0 = const()[name = string("input_119_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26297216)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26299328)))];
+            tensor<fp16, [1, 188, 1024]> input_119_cast_fp16 = layer_norm(axes = input_119_axes_0, beta = encoder_module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26301440))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28398656))))[name = string("encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28660864)))];
+            tensor<fp16, [1, 188, 4096]> linear_17_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_17_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_123_cast_fp16 = silu(x = linear_17_cast_fp16)[name = string("input_123_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28669120))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30766336))))[name = string("encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31028544)))];
+            tensor<fp16, [1, 188, 1024]> linear_18_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16_quantized, x = input_123_cast_fp16)[name = string("linear_18_cast_fp16")];
+            fp16 var_824_to_fp16 = const()[name = string("op_824_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_825_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_824_to_fp16)[name = string("op_825_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_129_cast_fp16 = add(x = input_117_cast_fp16, y = var_825_cast_fp16)[name = string("input_129_cast_fp16")];
+            tensor<int32, [1]> input_131_axes_0 = const()[name = string("input_131_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31030656)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31032768)))];
+            tensor<fp16, [1, 188, 1024]> input_131_cast_fp16 = layer_norm(axes = input_131_axes_0, beta = encoder_module_layers_1_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_out_weight_to_fp16, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")];
+            tensor<int32, [1]> input_133_axes_0 = const()[name = string("input_133_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31034880)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31036992)))];
+            tensor<fp16, [1, 188, 1024]> input_133_cast_fp16 = layer_norm(axes = input_133_axes_0, beta = encoder_module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_131_cast_fp16)[name = string("input_133_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31039104))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33136320))))[name = string("encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33398528)))];
+            tensor<fp16, [1, 188, 4096]> linear_19_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16_quantized, x = input_133_cast_fp16)[name = string("linear_19_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_137_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("input_137_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33406784))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35504000))))[name = string("encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35766208)))];
+            tensor<fp16, [1, 188, 1024]> linear_20_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16_quantized, x = input_137_cast_fp16)[name = string("linear_20_cast_fp16")];
+            fp16 var_855_to_fp16 = const()[name = string("op_855_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_856_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_855_to_fp16)[name = string("op_856_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_143_cast_fp16 = add(x = input_131_cast_fp16, y = var_856_cast_fp16)[name = string("input_143_cast_fp16")];
+            tensor<int32, [1]> query_5_axes_0 = const()[name = string("query_5_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35768320)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35770432)))];
+            tensor<fp16, [1, 188, 1024]> query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = encoder_module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_self_att_weight_to_fp16, x = input_143_cast_fp16)[name = string("query_5_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35772544))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36296896))))[name = string("encoder_module_layers_2_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36362496)))];
+            tensor<fp16, [1, 188, 1024]> linear_21_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_q_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = string("linear_21_cast_fp16")];
+            tensor<int32, [4]> var_873 = const()[name = string("op_873"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_13_cast_fp16 = reshape(shape = var_873, x = linear_21_cast_fp16)[name = string("q_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36364608))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36888960))))[name = string("encoder_module_layers_2_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36954560)))];
+            tensor<fp16, [1, 188, 1024]> linear_22_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_k_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = string("linear_22_cast_fp16")];
+            tensor<int32, [4]> var_878 = const()[name = string("op_878"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_9_cast_fp16 = reshape(shape = var_878, x = linear_22_cast_fp16)[name = string("k_9_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36956672))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37481024))))[name = string("encoder_module_layers_2_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37546624)))];
+            tensor<fp16, [1, 188, 1024]> linear_23_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_v_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = string("linear_23_cast_fp16")];
+            tensor<int32, [4]> var_883 = const()[name = string("op_883"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_5_cast_fp16 = reshape(shape = var_883, x = linear_23_cast_fp16)[name = string("v_5_cast_fp16")];
+            tensor<int32, [4]> value_9_perm_0 = const()[name = string("value_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37548736)))];
+            tensor<fp16, [1, 188, 8, 128]> var_895_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_module_layers_2_self_attn_pos_bias_u_to_fp16)[name = string("op_895_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37550848)))];
+            tensor<fp16, [1, 188, 8, 128]> var_897_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_module_layers_2_self_attn_pos_bias_v_to_fp16)[name = string("op_897_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_5_perm_0 = const()[name = string("q_with_bias_v_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_71_transpose_x_0 = const()[name = string("x_71_transpose_x_0"), val = bool(false)];
+            bool x_71_transpose_y_0 = const()[name = string("x_71_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_899_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37552960))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37745024))))[name = string("op_899_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_897_cast_fp16)[name = string("transpose_298")];
+            tensor<fp16, [1, 8, 188, 375]> x_71_cast_fp16 = matmul(transpose_x = x_71_transpose_x_0, transpose_y = x_71_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = op_899_to_fp16_quantized)[name = string("x_71_cast_fp16")];
+            tensor<int32, [8]> x_73_pad_0 = const()[name = string("x_73_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("constant")];
+            fp16 const_108_to_fp16 = const()[name = string("const_108_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_73_cast_fp16 = pad(constant_val = const_108_to_fp16, mode = x_73_mode_0, pad = x_73_pad_0, x = x_71_cast_fp16)[name = string("x_73_cast_fp16")];
+            tensor<int32, [4]> var_907 = const()[name = string("op_907"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_75_cast_fp16 = reshape(shape = var_907, x = x_73_cast_fp16)[name = string("x_75_cast_fp16")];
+            tensor<int32, [4]> var_911_begin_0 = const()[name = string("op_911_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_911_end_0 = const()[name = string("op_911_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_911_end_mask_0 = const()[name = string("op_911_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_911_cast_fp16 = slice_by_index(begin = var_911_begin_0, end = var_911_end_0, end_mask = var_911_end_mask_0, x = x_75_cast_fp16)[name = string("op_911_cast_fp16")];
+            tensor<int32, [4]> var_912 = const()[name = string("op_912"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_9_cast_fp16 = reshape(shape = var_912, x = var_911_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<int32, [4]> transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = string("transpose_296")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_100 = transpose(perm = transpose_100_perm_0, x = var_895_cast_fp16)[name = string("transpose_297")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_11_end_0 = const()[name = string("matrix_bd_11_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_921_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = string("op_921_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<fp16, [1, 8, 188, 188]> _inversed_scores_9_cast_fp16 = mul(x = var_921_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_11_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_15)[name = string("scores_11_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_927_cast_fp16 = softmax(axis = var_152, x = scores_11_cast_fp16)[name = string("op_927_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_145_cast_fp16 = select(a = var_164_to_fp16, b = var_927_cast_fp16, cond = mask_15)[name = string("input_145_cast_fp16")];
+            bool x_77_transpose_x_0 = const()[name = string("x_77_transpose_x_0"), val = bool(false)];
+            bool x_77_transpose_y_0 = const()[name = string("x_77_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_5_cast_fp16)[name = string("transpose_295")];
+            tensor<fp16, [1, 8, 188, 128]> x_77_cast_fp16 = matmul(transpose_x = x_77_transpose_x_0, transpose_y = x_77_transpose_y_0, x = input_145_cast_fp16, y = value_9_cast_fp16)[name = string("x_77_cast_fp16")];
+            tensor<int32, [4]> var_931_perm_0 = const()[name = string("op_931_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_932 = const()[name = string("op_932"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_931_cast_fp16 = transpose(perm = var_931_perm_0, x = x_77_cast_fp16)[name = string("transpose_294")];
+            tensor<fp16, [1, 188, 1024]> input_147_cast_fp16 = reshape(shape = var_932, x = var_931_cast_fp16)[name = string("input_147_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37748096))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38272448))))[name = string("encoder_module_layers_2_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38338048)))];
+            tensor<fp16, [1, 188, 1024]> linear_25_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_out_weight_to_fp16_quantized, x = input_147_cast_fp16)[name = string("linear_25_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_151_cast_fp16 = add(x = input_143_cast_fp16, y = linear_25_cast_fp16)[name = string("input_151_cast_fp16")];
+            tensor<int32, [1]> x_81_axes_0 = const()[name = string("x_81_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38340160)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38342272)))];
+            tensor<fp16, [1, 188, 1024]> x_81_cast_fp16 = layer_norm(axes = x_81_axes_0, beta = encoder_module_layers_2_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_conv_weight_to_fp16, x = input_151_cast_fp16)[name = string("x_81_cast_fp16")];
+            tensor<int32, [3]> input_153_perm_0 = const()[name = string("input_153_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38344384))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39393024))))[name = string("encoder_module_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39524160)))];
+            tensor<fp16, [1, 1024, 188]> input_153_cast_fp16 = transpose(perm = input_153_perm_0, x = x_81_cast_fp16)[name = string("transpose_293")];
+            tensor<fp16, [1, 2048, 188]> input_155_cast_fp16 = conv(bias = encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_153_cast_fp16)[name = string("input_155_cast_fp16")];
+            int32 x_83_split_num_splits_0 = const()[name = string("x_83_split_num_splits_0"), val = int32(2)];
+            int32 x_83_split_axis_0 = const()[name = string("x_83_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_83_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_83_split_cast_fp16_1 = split(axis = x_83_split_axis_0, num_splits = x_83_split_num_splits_0, x = input_155_cast_fp16)[name = string("x_83_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_83_split_1_sigmoid_cast_fp16 = sigmoid(x = x_83_split_cast_fp16_1)[name = string("x_83_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_83_cast_fp16 = mul(x = x_83_split_cast_fp16_0, y = x_83_split_1_sigmoid_cast_fp16)[name = string("x_83_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_157_cast_fp16 = select(a = var_164_to_fp16, b = x_83_cast_fp16, cond = var_608)[name = string("input_157_cast_fp16")];
+            tensor<int32, [6]> input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_159_mode_0 = const()[name = string("input_159_mode_0"), val = string("constant")];
+            fp16 const_111_to_fp16 = const()[name = string("const_111_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_159_cast_fp16 = pad(constant_val = const_111_to_fp16, mode = input_159_mode_0, pad = input_159_pad_0, x = input_157_cast_fp16)[name = string("input_159_cast_fp16")];
+            string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("valid")];
+            int32 input_161_groups_0 = const()[name = string("input_161_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_161_strides_0 = const()[name = string("input_161_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_161_dilations_0 = const()[name = string("input_161_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_326_to_fp16 = const()[name = string("const_326_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39528320)))];
+            tensor<fp16, [1024]> const_327_to_fp16 = const()[name = string("const_327_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39546816)))];
+            tensor<fp16, [1, 1024, 188]> input_163_cast_fp16 = conv(bias = const_327_to_fp16, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_326_to_fp16, x = input_159_cast_fp16)[name = string("input_163_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_165_cast_fp16 = silu(x = input_163_cast_fp16)[name = string("input_165_cast_fp16")];
+            string x_85_pad_type_0 = const()[name = string("x_85_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_85_strides_0 = const()[name = string("x_85_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_85_pad_0 = const()[name = string("x_85_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_85_dilations_0 = const()[name = string("x_85_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_85_groups_0 = const()[name = string("x_85_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39548928))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40073280))))[name = string("encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40138880)))];
+            tensor<fp16, [1, 1024, 188]> x_85_cast_fp16 = conv(bias = encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16, dilations = x_85_dilations_0, groups = x_85_groups_0, pad = x_85_pad_0, pad_type = x_85_pad_type_0, strides = x_85_strides_0, weight = encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("x_85_cast_fp16")];
+            tensor<int32, [3]> input_167_perm_0 = const()[name = string("input_167_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_167_cast_fp16 = transpose(perm = input_167_perm_0, x = x_85_cast_fp16)[name = string("transpose_292")];
+            tensor<fp16, [1, 188, 1024]> input_169_cast_fp16 = add(x = input_151_cast_fp16, y = input_167_cast_fp16)[name = string("input_169_cast_fp16")];
+            tensor<int32, [1]> input_171_axes_0 = const()[name = string("input_171_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40140992)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40143104)))];
+            tensor<fp16, [1, 188, 1024]> input_171_cast_fp16 = layer_norm(axes = input_171_axes_0, beta = encoder_module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_169_cast_fp16)[name = string("input_171_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40145216))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42242432))))[name = string("encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42504640)))];
+            tensor<fp16, [1, 188, 4096]> linear_26_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_26_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_175_cast_fp16 = silu(x = linear_26_cast_fp16)[name = string("input_175_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42512896))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44610112))))[name = string("encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44872320)))];
+            tensor<fp16, [1, 188, 1024]> linear_27_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_27_cast_fp16")];
+            fp16 var_998_to_fp16 = const()[name = string("op_998_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_999_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_998_to_fp16)[name = string("op_999_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_181_cast_fp16 = add(x = input_169_cast_fp16, y = var_999_cast_fp16)[name = string("input_181_cast_fp16")];
+            tensor<int32, [1]> input_183_axes_0 = const()[name = string("input_183_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44874432)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44876544)))];
+            tensor<fp16, [1, 188, 1024]> input_183_cast_fp16 = layer_norm(axes = input_183_axes_0, beta = encoder_module_layers_2_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_out_weight_to_fp16, x = input_181_cast_fp16)[name = string("input_183_cast_fp16")];
+            tensor<int32, [1]> input_185_axes_0 = const()[name = string("input_185_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44878656)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44880768)))];
+            tensor<fp16, [1, 188, 1024]> input_185_cast_fp16 = layer_norm(axes = input_185_axes_0, beta = encoder_module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_183_cast_fp16)[name = string("input_185_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44882880))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46980096))))[name = string("encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47242304)))];
+            tensor<fp16, [1, 188, 4096]> linear_28_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_28_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_189_cast_fp16 = silu(x = linear_28_cast_fp16)[name = string("input_189_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47250560))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49347776))))[name = string("encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49609984)))];
+            tensor<fp16, [1, 188, 1024]> linear_29_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_29_cast_fp16")];
+            fp16 var_1029_to_fp16 = const()[name = string("op_1029_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1030_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_1029_to_fp16)[name = string("op_1030_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_195_cast_fp16 = add(x = input_183_cast_fp16, y = var_1030_cast_fp16)[name = string("input_195_cast_fp16")];
+            tensor<int32, [1]> query_7_axes_0 = const()[name = string("query_7_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49612096)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49614208)))];
+            tensor<fp16, [1, 188, 1024]> query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = encoder_module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_self_att_weight_to_fp16, x = input_195_cast_fp16)[name = string("query_7_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49616320))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50140672))))[name = string("encoder_module_layers_3_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50206272)))];
+            tensor<fp16, [1, 188, 1024]> linear_30_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_q_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = string("linear_30_cast_fp16")];
+            tensor<int32, [4]> var_1047 = const()[name = string("op_1047"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_19_cast_fp16 = reshape(shape = var_1047, x = linear_30_cast_fp16)[name = string("q_19_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50208384))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50732736))))[name = string("encoder_module_layers_3_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50798336)))];
+            tensor<fp16, [1, 188, 1024]> linear_31_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_k_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = string("linear_31_cast_fp16")];
+            tensor<int32, [4]> var_1052 = const()[name = string("op_1052"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_13_cast_fp16 = reshape(shape = var_1052, x = linear_31_cast_fp16)[name = string("k_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50800448))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51324800))))[name = string("encoder_module_layers_3_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51390400)))];
+            tensor<fp16, [1, 188, 1024]> linear_32_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_v_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = string("linear_32_cast_fp16")];
+            tensor<int32, [4]> var_1057 = const()[name = string("op_1057"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_7_cast_fp16 = reshape(shape = var_1057, x = linear_32_cast_fp16)[name = string("v_7_cast_fp16")];
+            tensor<int32, [4]> value_11_perm_0 = const()[name = string("value_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51392512)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1069_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_module_layers_3_self_attn_pos_bias_u_to_fp16)[name = string("op_1069_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51394624)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1071_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_module_layers_3_self_attn_pos_bias_v_to_fp16)[name = string("op_1071_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_7_perm_0 = const()[name = string("q_with_bias_v_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_93_transpose_x_0 = const()[name = string("x_93_transpose_x_0"), val = bool(false)];
+            bool x_93_transpose_y_0 = const()[name = string("x_93_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1073_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51396736))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51588800))))[name = string("op_1073_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_1071_cast_fp16)[name = string("transpose_291")];
+            tensor<fp16, [1, 8, 188, 375]> x_93_cast_fp16 = matmul(transpose_x = x_93_transpose_x_0, transpose_y = x_93_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = op_1073_to_fp16_quantized)[name = string("x_93_cast_fp16")];
+            tensor<int32, [8]> x_95_pad_0 = const()[name = string("x_95_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("constant")];
+            fp16 const_118_to_fp16 = const()[name = string("const_118_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_95_cast_fp16 = pad(constant_val = const_118_to_fp16, mode = x_95_mode_0, pad = x_95_pad_0, x = x_93_cast_fp16)[name = string("x_95_cast_fp16")];
+            tensor<int32, [4]> var_1081 = const()[name = string("op_1081"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_97_cast_fp16 = reshape(shape = var_1081, x = x_95_cast_fp16)[name = string("x_97_cast_fp16")];
+            tensor<int32, [4]> var_1085_begin_0 = const()[name = string("op_1085_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1085_end_0 = const()[name = string("op_1085_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1085_end_mask_0 = const()[name = string("op_1085_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1085_cast_fp16 = slice_by_index(begin = var_1085_begin_0, end = var_1085_end_0, end_mask = var_1085_end_mask_0, x = x_97_cast_fp16)[name = string("op_1085_cast_fp16")];
+            tensor<int32, [4]> var_1086 = const()[name = string("op_1086"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_13_cast_fp16 = reshape(shape = var_1086, x = var_1085_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<int32, [4]> transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = string("transpose_289")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1069_cast_fp16)[name = string("transpose_290")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_15_end_0 = const()[name = string("matrix_bd_15_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1095_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = string("op_1095_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<fp16, [1, 8, 188, 188]> _inversed_scores_13_cast_fp16 = mul(x = var_1095_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = string("_inversed_scores_13_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_15_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_15)[name = string("scores_15_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1101_cast_fp16 = softmax(axis = var_152, x = scores_15_cast_fp16)[name = string("op_1101_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_197_cast_fp16 = select(a = var_164_to_fp16, b = var_1101_cast_fp16, cond = mask_15)[name = string("input_197_cast_fp16")];
+            bool x_99_transpose_x_0 = const()[name = string("x_99_transpose_x_0"), val = bool(false)];
+            bool x_99_transpose_y_0 = const()[name = string("x_99_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_7_cast_fp16)[name = string("transpose_288")];
+            tensor<fp16, [1, 8, 188, 128]> x_99_cast_fp16 = matmul(transpose_x = x_99_transpose_x_0, transpose_y = x_99_transpose_y_0, x = input_197_cast_fp16, y = value_11_cast_fp16)[name = string("x_99_cast_fp16")];
+            tensor<int32, [4]> var_1105_perm_0 = const()[name = string("op_1105_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1106 = const()[name = string("op_1106"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1105_cast_fp16 = transpose(perm = var_1105_perm_0, x = x_99_cast_fp16)[name = string("transpose_287")];
+            tensor<fp16, [1, 188, 1024]> input_199_cast_fp16 = reshape(shape = var_1106, x = var_1105_cast_fp16)[name = string("input_199_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51591872))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52116224))))[name = string("encoder_module_layers_3_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52181824)))];
+            tensor<fp16, [1, 188, 1024]> linear_34_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_out_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_34_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_203_cast_fp16 = add(x = input_195_cast_fp16, y = linear_34_cast_fp16)[name = string("input_203_cast_fp16")];
+            tensor<int32, [1]> x_103_axes_0 = const()[name = string("x_103_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52183936)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52186048)))];
+            tensor<fp16, [1, 188, 1024]> x_103_cast_fp16 = layer_norm(axes = x_103_axes_0, beta = encoder_module_layers_3_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_conv_weight_to_fp16, x = input_203_cast_fp16)[name = string("x_103_cast_fp16")];
+            tensor<int32, [3]> input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_207_pad_type_0 = const()[name = string("input_207_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_207_strides_0 = const()[name = string("input_207_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_207_pad_0 = const()[name = string("input_207_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_207_dilations_0 = const()[name = string("input_207_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_207_groups_0 = const()[name = string("input_207_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52188160))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53236800))))[name = string("encoder_module_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53367936)))];
+            tensor<fp16, [1, 1024, 188]> input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = x_103_cast_fp16)[name = string("transpose_286")];
+            tensor<fp16, [1, 2048, 188]> input_207_cast_fp16 = conv(bias = encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("input_207_cast_fp16")];
+            int32 x_105_split_num_splits_0 = const()[name = string("x_105_split_num_splits_0"), val = int32(2)];
+            int32 x_105_split_axis_0 = const()[name = string("x_105_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_105_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_105_split_cast_fp16_1 = split(axis = x_105_split_axis_0, num_splits = x_105_split_num_splits_0, x = input_207_cast_fp16)[name = string("x_105_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_105_split_1_sigmoid_cast_fp16 = sigmoid(x = x_105_split_cast_fp16_1)[name = string("x_105_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_105_cast_fp16 = mul(x = x_105_split_cast_fp16_0, y = x_105_split_1_sigmoid_cast_fp16)[name = string("x_105_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_209_cast_fp16 = select(a = var_164_to_fp16, b = x_105_cast_fp16, cond = var_608)[name = string("input_209_cast_fp16")];
+            tensor<int32, [6]> input_211_pad_0 = const()[name = string("input_211_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_211_mode_0 = const()[name = string("input_211_mode_0"), val = string("constant")];
+            fp16 const_121_to_fp16 = const()[name = string("const_121_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_211_cast_fp16 = pad(constant_val = const_121_to_fp16, mode = input_211_mode_0, pad = input_211_pad_0, x = input_209_cast_fp16)[name = string("input_211_cast_fp16")];
+            string input_213_pad_type_0 = const()[name = string("input_213_pad_type_0"), val = string("valid")];
+            int32 input_213_groups_0 = const()[name = string("input_213_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_213_strides_0 = const()[name = string("input_213_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_213_dilations_0 = const()[name = string("input_213_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_328_to_fp16 = const()[name = string("const_328_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53372096)))];
+            tensor<fp16, [1024]> const_329_to_fp16 = const()[name = string("const_329_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53390592)))];
+            tensor<fp16, [1, 1024, 188]> input_215_cast_fp16 = conv(bias = const_329_to_fp16, dilations = input_213_dilations_0, groups = input_213_groups_0, pad = input_213_pad_0, pad_type = input_213_pad_type_0, strides = input_213_strides_0, weight = const_328_to_fp16, x = input_211_cast_fp16)[name = string("input_215_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_217_cast_fp16 = silu(x = input_215_cast_fp16)[name = string("input_217_cast_fp16")];
+            string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53392704))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53917056))))[name = string("encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53982656)))];
+            tensor<fp16, [1, 1024, 188]> x_107_cast_fp16 = conv(bias = encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_217_cast_fp16)[name = string("x_107_cast_fp16")];
+            tensor<int32, [3]> input_219_perm_0 = const()[name = string("input_219_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_219_cast_fp16 = transpose(perm = input_219_perm_0, x = x_107_cast_fp16)[name = string("transpose_285")];
+            tensor<fp16, [1, 188, 1024]> input_221_cast_fp16 = add(x = input_203_cast_fp16, y = input_219_cast_fp16)[name = string("input_221_cast_fp16")];
+            tensor<int32, [1]> input_223_axes_0 = const()[name = string("input_223_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53984768)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53986880)))];
+            tensor<fp16, [1, 188, 1024]> input_223_cast_fp16 = layer_norm(axes = input_223_axes_0, beta = encoder_module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_221_cast_fp16)[name = string("input_223_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53988992))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086208))))[name = string("encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56348416)))];
+            tensor<fp16, [1, 188, 4096]> linear_35_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16_quantized, x = input_223_cast_fp16)[name = string("linear_35_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_227_cast_fp16 = silu(x = linear_35_cast_fp16)[name = string("input_227_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56356672))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58453888))))[name = string("encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58716096)))];
+            tensor<fp16, [1, 188, 1024]> linear_36_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16_quantized, x = input_227_cast_fp16)[name = string("linear_36_cast_fp16")];
+            fp16 var_1172_to_fp16 = const()[name = string("op_1172_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1173_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_1172_to_fp16)[name = string("op_1173_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_233_cast_fp16 = add(x = input_221_cast_fp16, y = var_1173_cast_fp16)[name = string("input_233_cast_fp16")];
+            tensor<int32, [1]> input_235_axes_0 = const()[name = string("input_235_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58718208)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58720320)))];
+            tensor<fp16, [1, 188, 1024]> input_235_cast_fp16 = layer_norm(axes = input_235_axes_0, beta = encoder_module_layers_3_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_out_weight_to_fp16, x = input_233_cast_fp16)[name = string("input_235_cast_fp16")];
+            tensor<int32, [1]> input_237_axes_0 = const()[name = string("input_237_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58722432)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58724544)))];
+            tensor<fp16, [1, 188, 1024]> input_237_cast_fp16 = layer_norm(axes = input_237_axes_0, beta = encoder_module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58726656))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60823872))))[name = string("encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61086080)))];
+            tensor<fp16, [1, 188, 4096]> linear_37_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16_quantized, x = input_237_cast_fp16)[name = string("linear_37_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_241_cast_fp16 = silu(x = linear_37_cast_fp16)[name = string("input_241_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61094336))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63191552))))[name = string("encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63453760)))];
+            tensor<fp16, [1, 188, 1024]> linear_38_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16_quantized, x = input_241_cast_fp16)[name = string("linear_38_cast_fp16")];
+            fp16 var_1203_to_fp16 = const()[name = string("op_1203_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1204_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_1203_to_fp16)[name = string("op_1204_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_247_cast_fp16 = add(x = input_235_cast_fp16, y = var_1204_cast_fp16)[name = string("input_247_cast_fp16")];
+            tensor<int32, [1]> query_9_axes_0 = const()[name = string("query_9_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63455872)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63457984)))];
+            tensor<fp16, [1, 188, 1024]> query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = encoder_module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_self_att_weight_to_fp16, x = input_247_cast_fp16)[name = string("query_9_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63460096))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63984448))))[name = string("encoder_module_layers_4_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64050048)))];
+            tensor<fp16, [1, 188, 1024]> linear_39_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_q_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = string("linear_39_cast_fp16")];
+            tensor<int32, [4]> var_1221 = const()[name = string("op_1221"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_25_cast_fp16 = reshape(shape = var_1221, x = linear_39_cast_fp16)[name = string("q_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64052160))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64576512))))[name = string("encoder_module_layers_4_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64642112)))];
+            tensor<fp16, [1, 188, 1024]> linear_40_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_k_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = string("linear_40_cast_fp16")];
+            tensor<int32, [4]> var_1226 = const()[name = string("op_1226"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_17_cast_fp16 = reshape(shape = var_1226, x = linear_40_cast_fp16)[name = string("k_17_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64644224))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65168576))))[name = string("encoder_module_layers_4_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65234176)))];
+            tensor<fp16, [1, 188, 1024]> linear_41_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_v_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = string("linear_41_cast_fp16")];
+            tensor<int32, [4]> var_1231 = const()[name = string("op_1231"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_9_cast_fp16 = reshape(shape = var_1231, x = linear_41_cast_fp16)[name = string("v_9_cast_fp16")];
+            tensor<int32, [4]> value_13_perm_0 = const()[name = string("value_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65236288)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1243_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_module_layers_4_self_attn_pos_bias_u_to_fp16)[name = string("op_1243_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65238400)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1245_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_module_layers_4_self_attn_pos_bias_v_to_fp16)[name = string("op_1245_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_9_perm_0 = const()[name = string("q_with_bias_v_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_115_transpose_x_0 = const()[name = string("x_115_transpose_x_0"), val = bool(false)];
+            bool x_115_transpose_y_0 = const()[name = string("x_115_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1247_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65240512))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65432576))))[name = string("op_1247_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_1245_cast_fp16)[name = string("transpose_284")];
+            tensor<fp16, [1, 8, 188, 375]> x_115_cast_fp16 = matmul(transpose_x = x_115_transpose_x_0, transpose_y = x_115_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = op_1247_to_fp16_quantized)[name = string("x_115_cast_fp16")];
+            tensor<int32, [8]> x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_117_mode_0 = const()[name = string("x_117_mode_0"), val = string("constant")];
+            fp16 const_128_to_fp16 = const()[name = string("const_128_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_117_cast_fp16 = pad(constant_val = const_128_to_fp16, mode = x_117_mode_0, pad = x_117_pad_0, x = x_115_cast_fp16)[name = string("x_117_cast_fp16")];
+            tensor<int32, [4]> var_1255 = const()[name = string("op_1255"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_119_cast_fp16 = reshape(shape = var_1255, x = x_117_cast_fp16)[name = string("x_119_cast_fp16")];
+            tensor<int32, [4]> var_1259_begin_0 = const()[name = string("op_1259_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1259_end_0 = const()[name = string("op_1259_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1259_end_mask_0 = const()[name = string("op_1259_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1259_cast_fp16 = slice_by_index(begin = var_1259_begin_0, end = var_1259_end_0, end_mask = var_1259_end_mask_0, x = x_119_cast_fp16)[name = string("op_1259_cast_fp16")];
+            tensor<int32, [4]> var_1260 = const()[name = string("op_1260"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_17_cast_fp16 = reshape(shape = var_1260, x = var_1259_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<int32, [4]> transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = string("transpose_282")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_104 = transpose(perm = transpose_104_perm_0, x = var_1243_cast_fp16)[name = string("transpose_283")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_19_end_0 = const()[name = string("matrix_bd_19_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1269_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = string("op_1269_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<fp16, [1, 8, 188, 188]> _inversed_scores_17_cast_fp16 = mul(x = var_1269_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = string("_inversed_scores_17_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_19_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_15)[name = string("scores_19_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1275_cast_fp16 = softmax(axis = var_152, x = scores_19_cast_fp16)[name = string("op_1275_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_249_cast_fp16 = select(a = var_164_to_fp16, b = var_1275_cast_fp16, cond = mask_15)[name = string("input_249_cast_fp16")];
+            bool x_121_transpose_x_0 = const()[name = string("x_121_transpose_x_0"), val = bool(false)];
+            bool x_121_transpose_y_0 = const()[name = string("x_121_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_9_cast_fp16)[name = string("transpose_281")];
+            tensor<fp16, [1, 8, 188, 128]> x_121_cast_fp16 = matmul(transpose_x = x_121_transpose_x_0, transpose_y = x_121_transpose_y_0, x = input_249_cast_fp16, y = value_13_cast_fp16)[name = string("x_121_cast_fp16")];
+            tensor<int32, [4]> var_1279_perm_0 = const()[name = string("op_1279_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1280 = const()[name = string("op_1280"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1279_cast_fp16 = transpose(perm = var_1279_perm_0, x = x_121_cast_fp16)[name = string("transpose_280")];
+            tensor<fp16, [1, 188, 1024]> input_251_cast_fp16 = reshape(shape = var_1280, x = var_1279_cast_fp16)[name = string("input_251_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65435648))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65960000))))[name = string("encoder_module_layers_4_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66025600)))];
+            tensor<fp16, [1, 188, 1024]> linear_43_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_out_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_43_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_255_cast_fp16 = add(x = input_247_cast_fp16, y = linear_43_cast_fp16)[name = string("input_255_cast_fp16")];
+            tensor<int32, [1]> x_125_axes_0 = const()[name = string("x_125_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66027712)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66029824)))];
+            tensor<fp16, [1, 188, 1024]> x_125_cast_fp16 = layer_norm(axes = x_125_axes_0, beta = encoder_module_layers_4_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_conv_weight_to_fp16, x = input_255_cast_fp16)[name = string("x_125_cast_fp16")];
+            tensor<int32, [3]> input_257_perm_0 = const()[name = string("input_257_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_259_pad_type_0 = const()[name = string("input_259_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_259_strides_0 = const()[name = string("input_259_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_259_pad_0 = const()[name = string("input_259_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_259_dilations_0 = const()[name = string("input_259_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_259_groups_0 = const()[name = string("input_259_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66031936))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67080576))))[name = string("encoder_module_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67211712)))];
+            tensor<fp16, [1, 1024, 188]> input_257_cast_fp16 = transpose(perm = input_257_perm_0, x = x_125_cast_fp16)[name = string("transpose_279")];
+            tensor<fp16, [1, 2048, 188]> input_259_cast_fp16 = conv(bias = encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_257_cast_fp16)[name = string("input_259_cast_fp16")];
+            int32 x_127_split_num_splits_0 = const()[name = string("x_127_split_num_splits_0"), val = int32(2)];
+            int32 x_127_split_axis_0 = const()[name = string("x_127_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_127_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_127_split_cast_fp16_1 = split(axis = x_127_split_axis_0, num_splits = x_127_split_num_splits_0, x = input_259_cast_fp16)[name = string("x_127_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_127_split_1_sigmoid_cast_fp16 = sigmoid(x = x_127_split_cast_fp16_1)[name = string("x_127_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_127_cast_fp16 = mul(x = x_127_split_cast_fp16_0, y = x_127_split_1_sigmoid_cast_fp16)[name = string("x_127_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_261_cast_fp16 = select(a = var_164_to_fp16, b = x_127_cast_fp16, cond = var_608)[name = string("input_261_cast_fp16")];
+            tensor<int32, [6]> input_263_pad_0 = const()[name = string("input_263_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_263_mode_0 = const()[name = string("input_263_mode_0"), val = string("constant")];
+            fp16 const_131_to_fp16 = const()[name = string("const_131_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_263_cast_fp16 = pad(constant_val = const_131_to_fp16, mode = input_263_mode_0, pad = input_263_pad_0, x = input_261_cast_fp16)[name = string("input_263_cast_fp16")];
+            string input_265_pad_type_0 = const()[name = string("input_265_pad_type_0"), val = string("valid")];
+            int32 input_265_groups_0 = const()[name = string("input_265_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_265_strides_0 = const()[name = string("input_265_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_265_pad_0 = const()[name = string("input_265_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_265_dilations_0 = const()[name = string("input_265_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_330_to_fp16 = const()[name = string("const_330_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67215872)))];
+            tensor<fp16, [1024]> const_331_to_fp16 = const()[name = string("const_331_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67234368)))];
+            tensor<fp16, [1, 1024, 188]> input_267_cast_fp16 = conv(bias = const_331_to_fp16, dilations = input_265_dilations_0, groups = input_265_groups_0, pad = input_265_pad_0, pad_type = input_265_pad_type_0, strides = input_265_strides_0, weight = const_330_to_fp16, x = input_263_cast_fp16)[name = string("input_267_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_269_cast_fp16 = silu(x = input_267_cast_fp16)[name = string("input_269_cast_fp16")];
+            string x_129_pad_type_0 = const()[name = string("x_129_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_129_strides_0 = const()[name = string("x_129_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_129_pad_0 = const()[name = string("x_129_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_129_dilations_0 = const()[name = string("x_129_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_129_groups_0 = const()[name = string("x_129_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67236480))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67760832))))[name = string("encoder_module_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67826432)))];
+            tensor<fp16, [1, 1024, 188]> x_129_cast_fp16 = conv(bias = encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_269_cast_fp16)[name = string("x_129_cast_fp16")];
+            tensor<int32, [3]> input_271_perm_0 = const()[name = string("input_271_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_271_cast_fp16 = transpose(perm = input_271_perm_0, x = x_129_cast_fp16)[name = string("transpose_278")];
+            tensor<fp16, [1, 188, 1024]> input_273_cast_fp16 = add(x = input_255_cast_fp16, y = input_271_cast_fp16)[name = string("input_273_cast_fp16")];
+            tensor<int32, [1]> input_275_axes_0 = const()[name = string("input_275_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67828544)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67830656)))];
+            tensor<fp16, [1, 188, 1024]> input_275_cast_fp16 = layer_norm(axes = input_275_axes_0, beta = encoder_module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67832768))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69929984))))[name = string("encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70192192)))];
+            tensor<fp16, [1, 188, 4096]> linear_44_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_44_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_279_cast_fp16 = silu(x = linear_44_cast_fp16)[name = string("input_279_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70200448))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72297664))))[name = string("encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72559872)))];
+            tensor<fp16, [1, 188, 1024]> linear_45_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16_quantized, x = input_279_cast_fp16)[name = string("linear_45_cast_fp16")];
+            fp16 var_1346_to_fp16 = const()[name = string("op_1346_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1347_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1346_to_fp16)[name = string("op_1347_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_285_cast_fp16 = add(x = input_273_cast_fp16, y = var_1347_cast_fp16)[name = string("input_285_cast_fp16")];
+            tensor<int32, [1]> input_287_axes_0 = const()[name = string("input_287_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72561984)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72564096)))];
+            tensor<fp16, [1, 188, 1024]> input_287_cast_fp16 = layer_norm(axes = input_287_axes_0, beta = encoder_module_layers_4_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_out_weight_to_fp16, x = input_285_cast_fp16)[name = string("input_287_cast_fp16")];
+            tensor<int32, [1]> input_289_axes_0 = const()[name = string("input_289_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72566208)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72568320)))];
+            tensor<fp16, [1, 188, 1024]> input_289_cast_fp16 = layer_norm(axes = input_289_axes_0, beta = encoder_module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_287_cast_fp16)[name = string("input_289_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72570432))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74667648))))[name = string("encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74929856)))];
+            tensor<fp16, [1, 188, 4096]> linear_46_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_46_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_293_cast_fp16 = silu(x = linear_46_cast_fp16)[name = string("input_293_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(74938112))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77035328))))[name = string("encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77297536)))];
+            tensor<fp16, [1, 188, 1024]> linear_47_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16_quantized, x = input_293_cast_fp16)[name = string("linear_47_cast_fp16")];
+            fp16 var_1377_to_fp16 = const()[name = string("op_1377_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1378_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1377_to_fp16)[name = string("op_1378_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_299_cast_fp16 = add(x = input_287_cast_fp16, y = var_1378_cast_fp16)[name = string("input_299_cast_fp16")];
+            tensor<int32, [1]> query_11_axes_0 = const()[name = string("query_11_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77299648)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77301760)))];
+            tensor<fp16, [1, 188, 1024]> query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = encoder_module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_self_att_weight_to_fp16, x = input_299_cast_fp16)[name = string("query_11_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77303872))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77828224))))[name = string("encoder_module_layers_5_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77893824)))];
+            tensor<fp16, [1, 188, 1024]> linear_48_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_q_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = string("linear_48_cast_fp16")];
+            tensor<int32, [4]> var_1395 = const()[name = string("op_1395"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_31_cast_fp16 = reshape(shape = var_1395, x = linear_48_cast_fp16)[name = string("q_31_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77895936))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78420288))))[name = string("encoder_module_layers_5_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78485888)))];
+            tensor<fp16, [1, 188, 1024]> linear_49_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_k_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = string("linear_49_cast_fp16")];
+            tensor<int32, [4]> var_1400 = const()[name = string("op_1400"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_21_cast_fp16 = reshape(shape = var_1400, x = linear_49_cast_fp16)[name = string("k_21_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78488000))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79012352))))[name = string("encoder_module_layers_5_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79077952)))];
+            tensor<fp16, [1, 188, 1024]> linear_50_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_v_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = string("linear_50_cast_fp16")];
+            tensor<int32, [4]> var_1405 = const()[name = string("op_1405"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_11_cast_fp16 = reshape(shape = var_1405, x = linear_50_cast_fp16)[name = string("v_11_cast_fp16")];
+            tensor<int32, [4]> value_15_perm_0 = const()[name = string("value_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79080064)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1417_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_module_layers_5_self_attn_pos_bias_u_to_fp16)[name = string("op_1417_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79082176)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1419_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_module_layers_5_self_attn_pos_bias_v_to_fp16)[name = string("op_1419_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_11_perm_0 = const()[name = string("q_with_bias_v_11_perm_0"), val = tensor<int32, [4]>([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<fp16, [1, 8, 128, 375]> op_1421_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79084288))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79276352))))[name = string("op_1421_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1419_cast_fp16)[name = string("transpose_277")];
+            tensor<fp16, [1, 8, 188, 375]> 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_1421_to_fp16_quantized)[name = string("x_137_cast_fp16")];
+            tensor<int32, [8]> x_139_pad_0 = const()[name = string("x_139_pad_0"), val = tensor<int32, [8]>([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_138_to_fp16 = const()[name = string("const_138_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_139_cast_fp16 = pad(constant_val = const_138_to_fp16, mode = x_139_mode_0, pad = x_139_pad_0, x = x_137_cast_fp16)[name = string("x_139_cast_fp16")];
+            tensor<int32, [4]> var_1429 = const()[name = string("op_1429"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_141_cast_fp16 = reshape(shape = var_1429, x = x_139_cast_fp16)[name = string("x_141_cast_fp16")];
+            tensor<int32, [4]> var_1433_begin_0 = const()[name = string("op_1433_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1433_end_0 = const()[name = string("op_1433_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1433_end_mask_0 = const()[name = string("op_1433_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1433_cast_fp16 = slice_by_index(begin = var_1433_begin_0, end = var_1433_end_0, end_mask = var_1433_end_mask_0, x = x_141_cast_fp16)[name = string("op_1433_cast_fp16")];
+            tensor<int32, [4]> var_1434 = const()[name = string("op_1434"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_21_cast_fp16 = reshape(shape = var_1434, x = var_1433_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<int32, [4]> transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = string("transpose_275")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1417_cast_fp16)[name = string("transpose_276")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_23_end_0 = const()[name = string("matrix_bd_23_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1443_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = string("op_1443_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<fp16, [1, 8, 188, 188]> _inversed_scores_21_cast_fp16 = mul(x = var_1443_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = string("_inversed_scores_21_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_23_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_15)[name = string("scores_23_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1449_cast_fp16 = softmax(axis = var_152, x = scores_23_cast_fp16)[name = string("op_1449_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_301_cast_fp16 = select(a = var_164_to_fp16, b = var_1449_cast_fp16, cond = mask_15)[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<fp16, [1, 8, 188, 128]> value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_11_cast_fp16)[name = string("transpose_274")];
+            tensor<fp16, [1, 8, 188, 128]> 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_15_cast_fp16)[name = string("x_143_cast_fp16")];
+            tensor<int32, [4]> var_1453_perm_0 = const()[name = string("op_1453_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1454 = const()[name = string("op_1454"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1453_cast_fp16 = transpose(perm = var_1453_perm_0, x = x_143_cast_fp16)[name = string("transpose_273")];
+            tensor<fp16, [1, 188, 1024]> input_303_cast_fp16 = reshape(shape = var_1454, x = var_1453_cast_fp16)[name = string("input_303_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79279424))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79803776))))[name = string("encoder_module_layers_5_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79869376)))];
+            tensor<fp16, [1, 188, 1024]> linear_52_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_out_weight_to_fp16_quantized, x = input_303_cast_fp16)[name = string("linear_52_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_307_cast_fp16 = add(x = input_299_cast_fp16, y = linear_52_cast_fp16)[name = string("input_307_cast_fp16")];
+            tensor<int32, [1]> x_147_axes_0 = const()[name = string("x_147_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79871488)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79873600)))];
+            tensor<fp16, [1, 188, 1024]> x_147_cast_fp16 = layer_norm(axes = x_147_axes_0, beta = encoder_module_layers_5_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_conv_weight_to_fp16, x = input_307_cast_fp16)[name = string("x_147_cast_fp16")];
+            tensor<int32, [3]> input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_311_pad_type_0 = const()[name = string("input_311_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_311_strides_0 = const()[name = string("input_311_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_311_pad_0 = const()[name = string("input_311_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_311_dilations_0 = const()[name = string("input_311_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_311_groups_0 = const()[name = string("input_311_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79875712))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80924352))))[name = string("encoder_module_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81055488)))];
+            tensor<fp16, [1, 1024, 188]> input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_147_cast_fp16)[name = string("transpose_272")];
+            tensor<fp16, [1, 2048, 188]> input_311_cast_fp16 = conv(bias = encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16, 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_module_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<fp16, [1, 1024, 188]> x_149_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> input_313_cast_fp16 = select(a = var_164_to_fp16, b = x_149_cast_fp16, cond = var_608)[name = string("input_313_cast_fp16")];
+            tensor<int32, [6]> input_315_pad_0 = const()[name = string("input_315_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_315_mode_0 = const()[name = string("input_315_mode_0"), val = string("constant")];
+            fp16 const_141_to_fp16 = const()[name = string("const_141_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_315_cast_fp16 = pad(constant_val = const_141_to_fp16, mode = input_315_mode_0, pad = input_315_pad_0, x = input_313_cast_fp16)[name = string("input_315_cast_fp16")];
+            string input_317_pad_type_0 = const()[name = string("input_317_pad_type_0"), val = string("valid")];
+            int32 input_317_groups_0 = const()[name = string("input_317_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_317_strides_0 = const()[name = string("input_317_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_317_pad_0 = const()[name = string("input_317_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_317_dilations_0 = const()[name = string("input_317_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_332_to_fp16 = const()[name = string("const_332_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81059648)))];
+            tensor<fp16, [1024]> const_333_to_fp16 = const()[name = string("const_333_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81078144)))];
+            tensor<fp16, [1, 1024, 188]> input_319_cast_fp16 = conv(bias = const_333_to_fp16, dilations = input_317_dilations_0, groups = input_317_groups_0, pad = input_317_pad_0, pad_type = input_317_pad_type_0, strides = input_317_strides_0, weight = const_332_to_fp16, x = input_315_cast_fp16)[name = string("input_319_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_321_cast_fp16 = silu(x = input_319_cast_fp16)[name = string("input_321_cast_fp16")];
+            string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81080256))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81604608))))[name = string("encoder_module_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81670208)))];
+            tensor<fp16, [1, 1024, 188]> x_151_cast_fp16 = conv(bias = encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_321_cast_fp16)[name = string("x_151_cast_fp16")];
+            tensor<int32, [3]> input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = x_151_cast_fp16)[name = string("transpose_271")];
+            tensor<fp16, [1, 188, 1024]> input_325_cast_fp16 = add(x = input_307_cast_fp16, y = input_323_cast_fp16)[name = string("input_325_cast_fp16")];
+            tensor<int32, [1]> input_327_axes_0 = const()[name = string("input_327_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81672320)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81674432)))];
+            tensor<fp16, [1, 188, 1024]> input_327_cast_fp16 = layer_norm(axes = input_327_axes_0, beta = encoder_module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_325_cast_fp16)[name = string("input_327_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81676544))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83773760))))[name = string("encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84035968)))];
+            tensor<fp16, [1, 188, 4096]> linear_53_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16_quantized, x = input_327_cast_fp16)[name = string("linear_53_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_331_cast_fp16 = silu(x = linear_53_cast_fp16)[name = string("input_331_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84044224))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86141440))))[name = string("encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86403648)))];
+            tensor<fp16, [1, 188, 1024]> linear_54_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16_quantized, x = input_331_cast_fp16)[name = string("linear_54_cast_fp16")];
+            fp16 var_1520_to_fp16 = const()[name = string("op_1520_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1521_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1520_to_fp16)[name = string("op_1521_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_337_cast_fp16 = add(x = input_325_cast_fp16, y = var_1521_cast_fp16)[name = string("input_337_cast_fp16")];
+            tensor<int32, [1]> input_339_axes_0 = const()[name = string("input_339_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86405760)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86407872)))];
+            tensor<fp16, [1, 188, 1024]> input_339_cast_fp16 = layer_norm(axes = input_339_axes_0, beta = encoder_module_layers_5_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_out_weight_to_fp16, x = input_337_cast_fp16)[name = string("input_339_cast_fp16")];
+            tensor<int32, [1]> input_341_axes_0 = const()[name = string("input_341_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86409984)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86412096)))];
+            tensor<fp16, [1, 188, 1024]> input_341_cast_fp16 = layer_norm(axes = input_341_axes_0, beta = encoder_module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_339_cast_fp16)[name = string("input_341_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86414208))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88511424))))[name = string("encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88773632)))];
+            tensor<fp16, [1, 188, 4096]> linear_55_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16_quantized, x = input_341_cast_fp16)[name = string("linear_55_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_345_cast_fp16 = silu(x = linear_55_cast_fp16)[name = string("input_345_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88781888))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90879104))))[name = string("encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91141312)))];
+            tensor<fp16, [1, 188, 1024]> linear_56_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16_quantized, x = input_345_cast_fp16)[name = string("linear_56_cast_fp16")];
+            fp16 var_1551_to_fp16 = const()[name = string("op_1551_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1552_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1551_to_fp16)[name = string("op_1552_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_351_cast_fp16 = add(x = input_339_cast_fp16, y = var_1552_cast_fp16)[name = string("input_351_cast_fp16")];
+            tensor<int32, [1]> query_13_axes_0 = const()[name = string("query_13_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91143424)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91145536)))];
+            tensor<fp16, [1, 188, 1024]> query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = encoder_module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_self_att_weight_to_fp16, x = input_351_cast_fp16)[name = string("query_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91147648))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91672000))))[name = string("encoder_module_layers_6_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91737600)))];
+            tensor<fp16, [1, 188, 1024]> linear_57_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_q_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = string("linear_57_cast_fp16")];
+            tensor<int32, [4]> var_1569 = const()[name = string("op_1569"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_37_cast_fp16 = reshape(shape = var_1569, x = linear_57_cast_fp16)[name = string("q_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91739712))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92264064))))[name = string("encoder_module_layers_6_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92329664)))];
+            tensor<fp16, [1, 188, 1024]> linear_58_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_k_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = string("linear_58_cast_fp16")];
+            tensor<int32, [4]> var_1574 = const()[name = string("op_1574"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_25_cast_fp16 = reshape(shape = var_1574, x = linear_58_cast_fp16)[name = string("k_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92331776))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92856128))))[name = string("encoder_module_layers_6_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92921728)))];
+            tensor<fp16, [1, 188, 1024]> linear_59_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_v_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = string("linear_59_cast_fp16")];
+            tensor<int32, [4]> var_1579 = const()[name = string("op_1579"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_13_cast_fp16 = reshape(shape = var_1579, x = linear_59_cast_fp16)[name = string("v_13_cast_fp16")];
+            tensor<int32, [4]> value_17_perm_0 = const()[name = string("value_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92923840)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1591_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_module_layers_6_self_attn_pos_bias_u_to_fp16)[name = string("op_1591_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92925952)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1593_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_module_layers_6_self_attn_pos_bias_v_to_fp16)[name = string("op_1593_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_13_perm_0 = const()[name = string("q_with_bias_v_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_159_transpose_x_0 = const()[name = string("x_159_transpose_x_0"), val = bool(false)];
+            bool x_159_transpose_y_0 = const()[name = string("x_159_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1595_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92928064))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93120128))))[name = string("op_1595_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1593_cast_fp16)[name = string("transpose_270")];
+            tensor<fp16, [1, 8, 188, 375]> x_159_cast_fp16 = matmul(transpose_x = x_159_transpose_x_0, transpose_y = x_159_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = op_1595_to_fp16_quantized)[name = string("x_159_cast_fp16")];
+            tensor<int32, [8]> x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_161_mode_0 = const()[name = string("x_161_mode_0"), val = string("constant")];
+            fp16 const_148_to_fp16 = const()[name = string("const_148_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_161_cast_fp16 = pad(constant_val = const_148_to_fp16, mode = x_161_mode_0, pad = x_161_pad_0, x = x_159_cast_fp16)[name = string("x_161_cast_fp16")];
+            tensor<int32, [4]> var_1603 = const()[name = string("op_1603"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_163_cast_fp16 = reshape(shape = var_1603, x = x_161_cast_fp16)[name = string("x_163_cast_fp16")];
+            tensor<int32, [4]> var_1607_begin_0 = const()[name = string("op_1607_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1607_end_0 = const()[name = string("op_1607_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1607_end_mask_0 = const()[name = string("op_1607_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1607_cast_fp16 = slice_by_index(begin = var_1607_begin_0, end = var_1607_end_0, end_mask = var_1607_end_mask_0, x = x_163_cast_fp16)[name = string("op_1607_cast_fp16")];
+            tensor<int32, [4]> var_1608 = const()[name = string("op_1608"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_25_cast_fp16 = reshape(shape = var_1608, x = var_1607_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<int32, [4]> transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = string("transpose_268")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1591_cast_fp16)[name = string("transpose_269")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_27_end_0 = const()[name = string("matrix_bd_27_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1617_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = string("op_1617_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<fp16, [1, 8, 188, 188]> _inversed_scores_25_cast_fp16 = mul(x = var_1617_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = string("_inversed_scores_25_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_27_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_15)[name = string("scores_27_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1623_cast_fp16 = softmax(axis = var_152, x = scores_27_cast_fp16)[name = string("op_1623_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_353_cast_fp16 = select(a = var_164_to_fp16, b = var_1623_cast_fp16, cond = mask_15)[name = string("input_353_cast_fp16")];
+            bool x_165_transpose_x_0 = const()[name = string("x_165_transpose_x_0"), val = bool(false)];
+            bool x_165_transpose_y_0 = const()[name = string("x_165_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_13_cast_fp16)[name = string("transpose_267")];
+            tensor<fp16, [1, 8, 188, 128]> x_165_cast_fp16 = matmul(transpose_x = x_165_transpose_x_0, transpose_y = x_165_transpose_y_0, x = input_353_cast_fp16, y = value_17_cast_fp16)[name = string("x_165_cast_fp16")];
+            tensor<int32, [4]> var_1627_perm_0 = const()[name = string("op_1627_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1628 = const()[name = string("op_1628"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1627_cast_fp16 = transpose(perm = var_1627_perm_0, x = x_165_cast_fp16)[name = string("transpose_266")];
+            tensor<fp16, [1, 188, 1024]> input_355_cast_fp16 = reshape(shape = var_1628, x = var_1627_cast_fp16)[name = string("input_355_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93123200))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93647552))))[name = string("encoder_module_layers_6_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93713152)))];
+            tensor<fp16, [1, 188, 1024]> linear_61_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_out_weight_to_fp16_quantized, x = input_355_cast_fp16)[name = string("linear_61_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_359_cast_fp16 = add(x = input_351_cast_fp16, y = linear_61_cast_fp16)[name = string("input_359_cast_fp16")];
+            tensor<int32, [1]> x_169_axes_0 = const()[name = string("x_169_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93715264)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93717376)))];
+            tensor<fp16, [1, 188, 1024]> x_169_cast_fp16 = layer_norm(axes = x_169_axes_0, beta = encoder_module_layers_6_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_conv_weight_to_fp16, x = input_359_cast_fp16)[name = string("x_169_cast_fp16")];
+            tensor<int32, [3]> input_361_perm_0 = const()[name = string("input_361_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_363_pad_type_0 = const()[name = string("input_363_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_363_strides_0 = const()[name = string("input_363_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_363_pad_0 = const()[name = string("input_363_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_363_dilations_0 = const()[name = string("input_363_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_363_groups_0 = const()[name = string("input_363_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93719488))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94768128))))[name = string("encoder_module_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94899264)))];
+            tensor<fp16, [1, 1024, 188]> input_361_cast_fp16 = transpose(perm = input_361_perm_0, x = x_169_cast_fp16)[name = string("transpose_265")];
+            tensor<fp16, [1, 2048, 188]> input_363_cast_fp16 = conv(bias = encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_361_cast_fp16)[name = string("input_363_cast_fp16")];
+            int32 x_171_split_num_splits_0 = const()[name = string("x_171_split_num_splits_0"), val = int32(2)];
+            int32 x_171_split_axis_0 = const()[name = string("x_171_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_171_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_171_split_cast_fp16_1 = split(axis = x_171_split_axis_0, num_splits = x_171_split_num_splits_0, x = input_363_cast_fp16)[name = string("x_171_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_171_split_1_sigmoid_cast_fp16 = sigmoid(x = x_171_split_cast_fp16_1)[name = string("x_171_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_171_cast_fp16 = mul(x = x_171_split_cast_fp16_0, y = x_171_split_1_sigmoid_cast_fp16)[name = string("x_171_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_365_cast_fp16 = select(a = var_164_to_fp16, b = x_171_cast_fp16, cond = var_608)[name = string("input_365_cast_fp16")];
+            tensor<int32, [6]> input_367_pad_0 = const()[name = string("input_367_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_367_mode_0 = const()[name = string("input_367_mode_0"), val = string("constant")];
+            fp16 const_151_to_fp16 = const()[name = string("const_151_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_367_cast_fp16 = pad(constant_val = const_151_to_fp16, mode = input_367_mode_0, pad = input_367_pad_0, x = input_365_cast_fp16)[name = string("input_367_cast_fp16")];
+            string input_369_pad_type_0 = const()[name = string("input_369_pad_type_0"), val = string("valid")];
+            int32 input_369_groups_0 = const()[name = string("input_369_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_369_strides_0 = const()[name = string("input_369_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_369_dilations_0 = const()[name = string("input_369_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_334_to_fp16 = const()[name = string("const_334_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94903424)))];
+            tensor<fp16, [1024]> const_335_to_fp16 = const()[name = string("const_335_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94921920)))];
+            tensor<fp16, [1, 1024, 188]> input_371_cast_fp16 = conv(bias = const_335_to_fp16, dilations = input_369_dilations_0, groups = input_369_groups_0, pad = input_369_pad_0, pad_type = input_369_pad_type_0, strides = input_369_strides_0, weight = const_334_to_fp16, x = input_367_cast_fp16)[name = string("input_371_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_373_cast_fp16 = silu(x = input_371_cast_fp16)[name = string("input_373_cast_fp16")];
+            string x_173_pad_type_0 = const()[name = string("x_173_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_173_strides_0 = const()[name = string("x_173_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_173_pad_0 = const()[name = string("x_173_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_173_dilations_0 = const()[name = string("x_173_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_173_groups_0 = const()[name = string("x_173_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94924032))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95448384))))[name = string("encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95513984)))];
+            tensor<fp16, [1, 1024, 188]> x_173_cast_fp16 = conv(bias = encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16, dilations = x_173_dilations_0, groups = x_173_groups_0, pad = x_173_pad_0, pad_type = x_173_pad_type_0, strides = x_173_strides_0, weight = encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_373_cast_fp16)[name = string("x_173_cast_fp16")];
+            tensor<int32, [3]> input_375_perm_0 = const()[name = string("input_375_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_375_cast_fp16 = transpose(perm = input_375_perm_0, x = x_173_cast_fp16)[name = string("transpose_264")];
+            tensor<fp16, [1, 188, 1024]> input_377_cast_fp16 = add(x = input_359_cast_fp16, y = input_375_cast_fp16)[name = string("input_377_cast_fp16")];
+            tensor<int32, [1]> input_379_axes_0 = const()[name = string("input_379_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95516096)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95518208)))];
+            tensor<fp16, [1, 188, 1024]> input_379_cast_fp16 = layer_norm(axes = input_379_axes_0, beta = encoder_module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_377_cast_fp16)[name = string("input_379_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95520320))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97617536))))[name = string("encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97879744)))];
+            tensor<fp16, [1, 188, 4096]> linear_62_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16_quantized, x = input_379_cast_fp16)[name = string("linear_62_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_383_cast_fp16 = silu(x = linear_62_cast_fp16)[name = string("input_383_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97888000))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99985216))))[name = string("encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100247424)))];
+            tensor<fp16, [1, 188, 1024]> linear_63_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16_quantized, x = input_383_cast_fp16)[name = string("linear_63_cast_fp16")];
+            fp16 var_1694_to_fp16 = const()[name = string("op_1694_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1695_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1694_to_fp16)[name = string("op_1695_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_389_cast_fp16 = add(x = input_377_cast_fp16, y = var_1695_cast_fp16)[name = string("input_389_cast_fp16")];
+            tensor<int32, [1]> input_391_axes_0 = const()[name = string("input_391_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100249536)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100251648)))];
+            tensor<fp16, [1, 188, 1024]> input_391_cast_fp16 = layer_norm(axes = input_391_axes_0, beta = encoder_module_layers_6_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_out_weight_to_fp16, x = input_389_cast_fp16)[name = string("input_391_cast_fp16")];
+            tensor<int32, [1]> input_393_axes_0 = const()[name = string("input_393_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100253760)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100255872)))];
+            tensor<fp16, [1, 188, 1024]> input_393_cast_fp16 = layer_norm(axes = input_393_axes_0, beta = encoder_module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_391_cast_fp16)[name = string("input_393_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100257984))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102355200))))[name = string("encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102617408)))];
+            tensor<fp16, [1, 188, 4096]> linear_64_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16_quantized, x = input_393_cast_fp16)[name = string("linear_64_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_397_cast_fp16 = silu(x = linear_64_cast_fp16)[name = string("input_397_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102625664))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104722880))))[name = string("encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104985088)))];
+            tensor<fp16, [1, 188, 1024]> linear_65_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16_quantized, x = input_397_cast_fp16)[name = string("linear_65_cast_fp16")];
+            fp16 var_1725_to_fp16 = const()[name = string("op_1725_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1726_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1725_to_fp16)[name = string("op_1726_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_403_cast_fp16 = add(x = input_391_cast_fp16, y = var_1726_cast_fp16)[name = string("input_403_cast_fp16")];
+            tensor<int32, [1]> query_15_axes_0 = const()[name = string("query_15_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104987200)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104989312)))];
+            tensor<fp16, [1, 188, 1024]> query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = encoder_module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_self_att_weight_to_fp16, x = input_403_cast_fp16)[name = string("query_15_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(104991424))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105515776))))[name = string("encoder_module_layers_7_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105581376)))];
+            tensor<fp16, [1, 188, 1024]> linear_66_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_q_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = string("linear_66_cast_fp16")];
+            tensor<int32, [4]> var_1743 = const()[name = string("op_1743"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_43_cast_fp16 = reshape(shape = var_1743, x = linear_66_cast_fp16)[name = string("q_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105583488))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106107840))))[name = string("encoder_module_layers_7_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106173440)))];
+            tensor<fp16, [1, 188, 1024]> linear_67_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_k_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = string("linear_67_cast_fp16")];
+            tensor<int32, [4]> var_1748 = const()[name = string("op_1748"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_29_cast_fp16 = reshape(shape = var_1748, x = linear_67_cast_fp16)[name = string("k_29_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106175552))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106699904))))[name = string("encoder_module_layers_7_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106765504)))];
+            tensor<fp16, [1, 188, 1024]> linear_68_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_v_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = string("linear_68_cast_fp16")];
+            tensor<int32, [4]> var_1753 = const()[name = string("op_1753"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_15_cast_fp16 = reshape(shape = var_1753, x = linear_68_cast_fp16)[name = string("v_15_cast_fp16")];
+            tensor<int32, [4]> value_19_perm_0 = const()[name = string("value_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106767616)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1765_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_module_layers_7_self_attn_pos_bias_u_to_fp16)[name = string("op_1765_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106769728)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1767_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_module_layers_7_self_attn_pos_bias_v_to_fp16)[name = string("op_1767_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_15_perm_0 = const()[name = string("q_with_bias_v_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_181_transpose_x_0 = const()[name = string("x_181_transpose_x_0"), val = bool(false)];
+            bool x_181_transpose_y_0 = const()[name = string("x_181_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1769_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106771840))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106963904))))[name = string("op_1769_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1767_cast_fp16)[name = string("transpose_263")];
+            tensor<fp16, [1, 8, 188, 375]> x_181_cast_fp16 = matmul(transpose_x = x_181_transpose_x_0, transpose_y = x_181_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = op_1769_to_fp16_quantized)[name = string("x_181_cast_fp16")];
+            tensor<int32, [8]> x_183_pad_0 = const()[name = string("x_183_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_183_mode_0 = const()[name = string("x_183_mode_0"), val = string("constant")];
+            fp16 const_158_to_fp16 = const()[name = string("const_158_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_183_cast_fp16 = pad(constant_val = const_158_to_fp16, mode = x_183_mode_0, pad = x_183_pad_0, x = x_181_cast_fp16)[name = string("x_183_cast_fp16")];
+            tensor<int32, [4]> var_1777 = const()[name = string("op_1777"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_185_cast_fp16 = reshape(shape = var_1777, x = x_183_cast_fp16)[name = string("x_185_cast_fp16")];
+            tensor<int32, [4]> var_1781_begin_0 = const()[name = string("op_1781_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1781_end_0 = const()[name = string("op_1781_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1781_end_mask_0 = const()[name = string("op_1781_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1781_cast_fp16 = slice_by_index(begin = var_1781_begin_0, end = var_1781_end_0, end_mask = var_1781_end_mask_0, x = x_185_cast_fp16)[name = string("op_1781_cast_fp16")];
+            tensor<int32, [4]> var_1782 = const()[name = string("op_1782"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_29_cast_fp16 = reshape(shape = var_1782, x = var_1781_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<int32, [4]> transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = string("transpose_261")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_110 = transpose(perm = transpose_110_perm_0, x = var_1765_cast_fp16)[name = string("transpose_262")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_31_end_0 = const()[name = string("matrix_bd_31_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1791_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = string("op_1791_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<fp16, [1, 8, 188, 188]> _inversed_scores_29_cast_fp16 = mul(x = var_1791_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = string("_inversed_scores_29_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_31_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_15)[name = string("scores_31_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1797_cast_fp16 = softmax(axis = var_152, x = scores_31_cast_fp16)[name = string("op_1797_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_405_cast_fp16 = select(a = var_164_to_fp16, b = var_1797_cast_fp16, cond = mask_15)[name = string("input_405_cast_fp16")];
+            bool x_187_transpose_x_0 = const()[name = string("x_187_transpose_x_0"), val = bool(false)];
+            bool x_187_transpose_y_0 = const()[name = string("x_187_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_15_cast_fp16)[name = string("transpose_260")];
+            tensor<fp16, [1, 8, 188, 128]> x_187_cast_fp16 = matmul(transpose_x = x_187_transpose_x_0, transpose_y = x_187_transpose_y_0, x = input_405_cast_fp16, y = value_19_cast_fp16)[name = string("x_187_cast_fp16")];
+            tensor<int32, [4]> var_1801_perm_0 = const()[name = string("op_1801_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1802 = const()[name = string("op_1802"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1801_cast_fp16 = transpose(perm = var_1801_perm_0, x = x_187_cast_fp16)[name = string("transpose_259")];
+            tensor<fp16, [1, 188, 1024]> input_407_cast_fp16 = reshape(shape = var_1802, x = var_1801_cast_fp16)[name = string("input_407_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106966976))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107491328))))[name = string("encoder_module_layers_7_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107556928)))];
+            tensor<fp16, [1, 188, 1024]> linear_70_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_out_weight_to_fp16_quantized, x = input_407_cast_fp16)[name = string("linear_70_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_411_cast_fp16 = add(x = input_403_cast_fp16, y = linear_70_cast_fp16)[name = string("input_411_cast_fp16")];
+            tensor<int32, [1]> x_191_axes_0 = const()[name = string("x_191_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107559040)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107561152)))];
+            tensor<fp16, [1, 188, 1024]> x_191_cast_fp16 = layer_norm(axes = x_191_axes_0, beta = encoder_module_layers_7_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_conv_weight_to_fp16, x = input_411_cast_fp16)[name = string("x_191_cast_fp16")];
+            tensor<int32, [3]> input_413_perm_0 = const()[name = string("input_413_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_415_pad_type_0 = const()[name = string("input_415_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_415_strides_0 = const()[name = string("input_415_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_415_pad_0 = const()[name = string("input_415_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_415_dilations_0 = const()[name = string("input_415_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_415_groups_0 = const()[name = string("input_415_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107563264))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108611904))))[name = string("encoder_module_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108743040)))];
+            tensor<fp16, [1, 1024, 188]> input_413_cast_fp16 = transpose(perm = input_413_perm_0, x = x_191_cast_fp16)[name = string("transpose_258")];
+            tensor<fp16, [1, 2048, 188]> input_415_cast_fp16 = conv(bias = encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_413_cast_fp16)[name = string("input_415_cast_fp16")];
+            int32 x_193_split_num_splits_0 = const()[name = string("x_193_split_num_splits_0"), val = int32(2)];
+            int32 x_193_split_axis_0 = const()[name = string("x_193_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_193_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_193_split_cast_fp16_1 = split(axis = x_193_split_axis_0, num_splits = x_193_split_num_splits_0, x = input_415_cast_fp16)[name = string("x_193_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_193_split_1_sigmoid_cast_fp16 = sigmoid(x = x_193_split_cast_fp16_1)[name = string("x_193_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_193_cast_fp16 = mul(x = x_193_split_cast_fp16_0, y = x_193_split_1_sigmoid_cast_fp16)[name = string("x_193_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_417_cast_fp16 = select(a = var_164_to_fp16, b = x_193_cast_fp16, cond = var_608)[name = string("input_417_cast_fp16")];
+            tensor<int32, [6]> input_419_pad_0 = const()[name = string("input_419_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("constant")];
+            fp16 const_161_to_fp16 = const()[name = string("const_161_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_419_cast_fp16 = pad(constant_val = const_161_to_fp16, mode = input_419_mode_0, pad = input_419_pad_0, x = input_417_cast_fp16)[name = string("input_419_cast_fp16")];
+            string input_421_pad_type_0 = const()[name = string("input_421_pad_type_0"), val = string("valid")];
+            int32 input_421_groups_0 = const()[name = string("input_421_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_421_strides_0 = const()[name = string("input_421_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_421_pad_0 = const()[name = string("input_421_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_421_dilations_0 = const()[name = string("input_421_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_336_to_fp16 = const()[name = string("const_336_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108747200)))];
+            tensor<fp16, [1024]> const_337_to_fp16 = const()[name = string("const_337_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108765696)))];
+            tensor<fp16, [1, 1024, 188]> input_423_cast_fp16 = conv(bias = const_337_to_fp16, dilations = input_421_dilations_0, groups = input_421_groups_0, pad = input_421_pad_0, pad_type = input_421_pad_type_0, strides = input_421_strides_0, weight = const_336_to_fp16, x = input_419_cast_fp16)[name = string("input_423_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_425_cast_fp16 = silu(x = input_423_cast_fp16)[name = string("input_425_cast_fp16")];
+            string x_195_pad_type_0 = const()[name = string("x_195_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_195_strides_0 = const()[name = string("x_195_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_195_pad_0 = const()[name = string("x_195_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_195_dilations_0 = const()[name = string("x_195_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_195_groups_0 = const()[name = string("x_195_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108767808))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109292160))))[name = string("encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109357760)))];
+            tensor<fp16, [1, 1024, 188]> x_195_cast_fp16 = conv(bias = encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16, dilations = x_195_dilations_0, groups = x_195_groups_0, pad = x_195_pad_0, pad_type = x_195_pad_type_0, strides = x_195_strides_0, weight = encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_425_cast_fp16)[name = string("x_195_cast_fp16")];
+            tensor<int32, [3]> input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_195_cast_fp16)[name = string("transpose_257")];
+            tensor<fp16, [1, 188, 1024]> input_429_cast_fp16 = add(x = input_411_cast_fp16, y = input_427_cast_fp16)[name = string("input_429_cast_fp16")];
+            tensor<int32, [1]> input_431_axes_0 = const()[name = string("input_431_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109359872)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109361984)))];
+            tensor<fp16, [1, 188, 1024]> input_431_cast_fp16 = layer_norm(axes = input_431_axes_0, beta = encoder_module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_429_cast_fp16)[name = string("input_431_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109364096))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111461312))))[name = string("encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111723520)))];
+            tensor<fp16, [1, 188, 4096]> linear_71_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16_quantized, x = input_431_cast_fp16)[name = string("linear_71_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_435_cast_fp16 = silu(x = linear_71_cast_fp16)[name = string("input_435_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111731776))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113828992))))[name = string("encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114091200)))];
+            tensor<fp16, [1, 188, 1024]> linear_72_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16_quantized, x = input_435_cast_fp16)[name = string("linear_72_cast_fp16")];
+            fp16 var_1868_to_fp16 = const()[name = string("op_1868_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1869_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1868_to_fp16)[name = string("op_1869_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_441_cast_fp16 = add(x = input_429_cast_fp16, y = var_1869_cast_fp16)[name = string("input_441_cast_fp16")];
+            tensor<int32, [1]> input_443_axes_0 = const()[name = string("input_443_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114093312)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114095424)))];
+            tensor<fp16, [1, 188, 1024]> input_443_cast_fp16 = layer_norm(axes = input_443_axes_0, beta = encoder_module_layers_7_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_out_weight_to_fp16, x = input_441_cast_fp16)[name = string("input_443_cast_fp16")];
+            tensor<int32, [1]> input_445_axes_0 = const()[name = string("input_445_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114097536)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114099648)))];
+            tensor<fp16, [1, 188, 1024]> input_445_cast_fp16 = layer_norm(axes = input_445_axes_0, beta = encoder_module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_443_cast_fp16)[name = string("input_445_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114101760))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116198976))))[name = string("encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116461184)))];
+            tensor<fp16, [1, 188, 4096]> linear_73_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16_quantized, x = input_445_cast_fp16)[name = string("linear_73_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_449_cast_fp16 = silu(x = linear_73_cast_fp16)[name = string("input_449_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116469440))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118566656))))[name = string("encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118828864)))];
+            tensor<fp16, [1, 188, 1024]> linear_74_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16_quantized, x = input_449_cast_fp16)[name = string("linear_74_cast_fp16")];
+            fp16 var_1899_to_fp16 = const()[name = string("op_1899_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1900_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1899_to_fp16)[name = string("op_1900_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_455_cast_fp16 = add(x = input_443_cast_fp16, y = var_1900_cast_fp16)[name = string("input_455_cast_fp16")];
+            tensor<int32, [1]> query_17_axes_0 = const()[name = string("query_17_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118830976)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118833088)))];
+            tensor<fp16, [1, 188, 1024]> query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = encoder_module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_self_att_weight_to_fp16, x = input_455_cast_fp16)[name = string("query_17_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118835200))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119359552))))[name = string("encoder_module_layers_8_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119425152)))];
+            tensor<fp16, [1, 188, 1024]> linear_75_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_q_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = string("linear_75_cast_fp16")];
+            tensor<int32, [4]> var_1917 = const()[name = string("op_1917"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_49_cast_fp16 = reshape(shape = var_1917, x = linear_75_cast_fp16)[name = string("q_49_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119427264))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119951616))))[name = string("encoder_module_layers_8_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120017216)))];
+            tensor<fp16, [1, 188, 1024]> linear_76_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_k_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = string("linear_76_cast_fp16")];
+            tensor<int32, [4]> var_1922 = const()[name = string("op_1922"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_33_cast_fp16 = reshape(shape = var_1922, x = linear_76_cast_fp16)[name = string("k_33_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120019328))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120543680))))[name = string("encoder_module_layers_8_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120609280)))];
+            tensor<fp16, [1, 188, 1024]> linear_77_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_v_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = string("linear_77_cast_fp16")];
+            tensor<int32, [4]> var_1927 = const()[name = string("op_1927"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_17_cast_fp16 = reshape(shape = var_1927, x = linear_77_cast_fp16)[name = string("v_17_cast_fp16")];
+            tensor<int32, [4]> value_21_perm_0 = const()[name = string("value_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120611392)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1939_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_module_layers_8_self_attn_pos_bias_u_to_fp16)[name = string("op_1939_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120613504)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1941_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_module_layers_8_self_attn_pos_bias_v_to_fp16)[name = string("op_1941_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_17_perm_0 = const()[name = string("q_with_bias_v_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_203_transpose_x_0 = const()[name = string("x_203_transpose_x_0"), val = bool(false)];
+            bool x_203_transpose_y_0 = const()[name = string("x_203_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1943_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120615616))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120807680))))[name = string("op_1943_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1941_cast_fp16)[name = string("transpose_256")];
+            tensor<fp16, [1, 8, 188, 375]> x_203_cast_fp16 = matmul(transpose_x = x_203_transpose_x_0, transpose_y = x_203_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = op_1943_to_fp16_quantized)[name = string("x_203_cast_fp16")];
+            tensor<int32, [8]> x_205_pad_0 = const()[name = string("x_205_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_205_mode_0 = const()[name = string("x_205_mode_0"), val = string("constant")];
+            fp16 const_168_to_fp16 = const()[name = string("const_168_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_205_cast_fp16 = pad(constant_val = const_168_to_fp16, mode = x_205_mode_0, pad = x_205_pad_0, x = x_203_cast_fp16)[name = string("x_205_cast_fp16")];
+            tensor<int32, [4]> var_1951 = const()[name = string("op_1951"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_207_cast_fp16 = reshape(shape = var_1951, x = x_205_cast_fp16)[name = string("x_207_cast_fp16")];
+            tensor<int32, [4]> var_1955_begin_0 = const()[name = string("op_1955_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1955_end_0 = const()[name = string("op_1955_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1955_end_mask_0 = const()[name = string("op_1955_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1955_cast_fp16 = slice_by_index(begin = var_1955_begin_0, end = var_1955_end_0, end_mask = var_1955_end_mask_0, x = x_207_cast_fp16)[name = string("op_1955_cast_fp16")];
+            tensor<int32, [4]> var_1956 = const()[name = string("op_1956"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_33_cast_fp16 = reshape(shape = var_1956, x = var_1955_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<int32, [4]> transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = string("transpose_254")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_112 = transpose(perm = transpose_112_perm_0, x = var_1939_cast_fp16)[name = string("transpose_255")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_35_end_0 = const()[name = string("matrix_bd_35_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1965_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = string("op_1965_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<fp16, [1, 8, 188, 188]> _inversed_scores_33_cast_fp16 = mul(x = var_1965_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = string("_inversed_scores_33_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_35_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_15)[name = string("scores_35_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1971_cast_fp16 = softmax(axis = var_152, x = scores_35_cast_fp16)[name = string("op_1971_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_457_cast_fp16 = select(a = var_164_to_fp16, b = var_1971_cast_fp16, cond = mask_15)[name = string("input_457_cast_fp16")];
+            bool x_209_transpose_x_0 = const()[name = string("x_209_transpose_x_0"), val = bool(false)];
+            bool x_209_transpose_y_0 = const()[name = string("x_209_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_17_cast_fp16)[name = string("transpose_253")];
+            tensor<fp16, [1, 8, 188, 128]> x_209_cast_fp16 = matmul(transpose_x = x_209_transpose_x_0, transpose_y = x_209_transpose_y_0, x = input_457_cast_fp16, y = value_21_cast_fp16)[name = string("x_209_cast_fp16")];
+            tensor<int32, [4]> var_1975_perm_0 = const()[name = string("op_1975_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1976 = const()[name = string("op_1976"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1975_cast_fp16 = transpose(perm = var_1975_perm_0, x = x_209_cast_fp16)[name = string("transpose_252")];
+            tensor<fp16, [1, 188, 1024]> input_459_cast_fp16 = reshape(shape = var_1976, x = var_1975_cast_fp16)[name = string("input_459_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120810752))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121335104))))[name = string("encoder_module_layers_8_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121400704)))];
+            tensor<fp16, [1, 188, 1024]> linear_79_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_out_weight_to_fp16_quantized, x = input_459_cast_fp16)[name = string("linear_79_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_463_cast_fp16 = add(x = input_455_cast_fp16, y = linear_79_cast_fp16)[name = string("input_463_cast_fp16")];
+            tensor<int32, [1]> x_213_axes_0 = const()[name = string("x_213_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121402816)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121404928)))];
+            tensor<fp16, [1, 188, 1024]> x_213_cast_fp16 = layer_norm(axes = x_213_axes_0, beta = encoder_module_layers_8_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_conv_weight_to_fp16, x = input_463_cast_fp16)[name = string("x_213_cast_fp16")];
+            tensor<int32, [3]> input_465_perm_0 = const()[name = string("input_465_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_467_pad_type_0 = const()[name = string("input_467_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_467_strides_0 = const()[name = string("input_467_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_467_pad_0 = const()[name = string("input_467_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_467_dilations_0 = const()[name = string("input_467_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_467_groups_0 = const()[name = string("input_467_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(121407040))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122455680))))[name = string("encoder_module_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122586816)))];
+            tensor<fp16, [1, 1024, 188]> input_465_cast_fp16 = transpose(perm = input_465_perm_0, x = x_213_cast_fp16)[name = string("transpose_251")];
+            tensor<fp16, [1, 2048, 188]> input_467_cast_fp16 = conv(bias = encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_465_cast_fp16)[name = string("input_467_cast_fp16")];
+            int32 x_215_split_num_splits_0 = const()[name = string("x_215_split_num_splits_0"), val = int32(2)];
+            int32 x_215_split_axis_0 = const()[name = string("x_215_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_215_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_215_split_cast_fp16_1 = split(axis = x_215_split_axis_0, num_splits = x_215_split_num_splits_0, x = input_467_cast_fp16)[name = string("x_215_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_215_split_1_sigmoid_cast_fp16 = sigmoid(x = x_215_split_cast_fp16_1)[name = string("x_215_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_215_cast_fp16 = mul(x = x_215_split_cast_fp16_0, y = x_215_split_1_sigmoid_cast_fp16)[name = string("x_215_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_469_cast_fp16 = select(a = var_164_to_fp16, b = x_215_cast_fp16, cond = var_608)[name = string("input_469_cast_fp16")];
+            tensor<int32, [6]> input_471_pad_0 = const()[name = string("input_471_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_471_mode_0 = const()[name = string("input_471_mode_0"), val = string("constant")];
+            fp16 const_171_to_fp16 = const()[name = string("const_171_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_471_cast_fp16 = pad(constant_val = const_171_to_fp16, mode = input_471_mode_0, pad = input_471_pad_0, x = input_469_cast_fp16)[name = string("input_471_cast_fp16")];
+            string input_473_pad_type_0 = const()[name = string("input_473_pad_type_0"), val = string("valid")];
+            int32 input_473_groups_0 = const()[name = string("input_473_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_473_strides_0 = const()[name = string("input_473_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_473_pad_0 = const()[name = string("input_473_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_473_dilations_0 = const()[name = string("input_473_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_338_to_fp16 = const()[name = string("const_338_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122590976)))];
+            tensor<fp16, [1024]> const_339_to_fp16 = const()[name = string("const_339_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122609472)))];
+            tensor<fp16, [1, 1024, 188]> input_475_cast_fp16 = conv(bias = const_339_to_fp16, dilations = input_473_dilations_0, groups = input_473_groups_0, pad = input_473_pad_0, pad_type = input_473_pad_type_0, strides = input_473_strides_0, weight = const_338_to_fp16, x = input_471_cast_fp16)[name = string("input_475_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_477_cast_fp16 = silu(x = input_475_cast_fp16)[name = string("input_477_cast_fp16")];
+            string x_217_pad_type_0 = const()[name = string("x_217_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_217_strides_0 = const()[name = string("x_217_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_217_pad_0 = const()[name = string("x_217_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_217_dilations_0 = const()[name = string("x_217_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_217_groups_0 = const()[name = string("x_217_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(122611584))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123135936))))[name = string("encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123201536)))];
+            tensor<fp16, [1, 1024, 188]> x_217_cast_fp16 = conv(bias = encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16, dilations = x_217_dilations_0, groups = x_217_groups_0, pad = x_217_pad_0, pad_type = x_217_pad_type_0, strides = x_217_strides_0, weight = encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_477_cast_fp16)[name = string("x_217_cast_fp16")];
+            tensor<int32, [3]> input_479_perm_0 = const()[name = string("input_479_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_479_cast_fp16 = transpose(perm = input_479_perm_0, x = x_217_cast_fp16)[name = string("transpose_250")];
+            tensor<fp16, [1, 188, 1024]> input_481_cast_fp16 = add(x = input_463_cast_fp16, y = input_479_cast_fp16)[name = string("input_481_cast_fp16")];
+            tensor<int32, [1]> input_483_axes_0 = const()[name = string("input_483_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123203648)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123205760)))];
+            tensor<fp16, [1, 188, 1024]> input_483_cast_fp16 = layer_norm(axes = input_483_axes_0, beta = encoder_module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_481_cast_fp16)[name = string("input_483_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123207872))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125305088))))[name = string("encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125567296)))];
+            tensor<fp16, [1, 188, 4096]> linear_80_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16_quantized, x = input_483_cast_fp16)[name = string("linear_80_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_487_cast_fp16 = silu(x = linear_80_cast_fp16)[name = string("input_487_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125575552))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127672768))))[name = string("encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127934976)))];
+            tensor<fp16, [1, 188, 1024]> linear_81_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16_quantized, x = input_487_cast_fp16)[name = string("linear_81_cast_fp16")];
+            fp16 var_2042_to_fp16 = const()[name = string("op_2042_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2043_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_2042_to_fp16)[name = string("op_2043_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_493_cast_fp16 = add(x = input_481_cast_fp16, y = var_2043_cast_fp16)[name = string("input_493_cast_fp16")];
+            tensor<int32, [1]> input_495_axes_0 = const()[name = string("input_495_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127937088)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127939200)))];
+            tensor<fp16, [1, 188, 1024]> input_495_cast_fp16 = layer_norm(axes = input_495_axes_0, beta = encoder_module_layers_8_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_out_weight_to_fp16, x = input_493_cast_fp16)[name = string("input_495_cast_fp16")];
+            tensor<int32, [1]> input_497_axes_0 = const()[name = string("input_497_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127941312)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127943424)))];
+            tensor<fp16, [1, 188, 1024]> input_497_cast_fp16 = layer_norm(axes = input_497_axes_0, beta = encoder_module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_495_cast_fp16)[name = string("input_497_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127945536))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130042752))))[name = string("encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130304960)))];
+            tensor<fp16, [1, 188, 4096]> linear_82_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16_quantized, x = input_497_cast_fp16)[name = string("linear_82_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_501_cast_fp16 = silu(x = linear_82_cast_fp16)[name = string("input_501_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(130313216))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132410432))))[name = string("encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132672640)))];
+            tensor<fp16, [1, 188, 1024]> linear_83_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16_quantized, x = input_501_cast_fp16)[name = string("linear_83_cast_fp16")];
+            fp16 var_2073_to_fp16 = const()[name = string("op_2073_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2074_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_2073_to_fp16)[name = string("op_2074_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_507_cast_fp16 = add(x = input_495_cast_fp16, y = var_2074_cast_fp16)[name = string("input_507_cast_fp16")];
+            tensor<int32, [1]> query_19_axes_0 = const()[name = string("query_19_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132674752)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132676864)))];
+            tensor<fp16, [1, 188, 1024]> query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = encoder_module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_self_att_weight_to_fp16, x = input_507_cast_fp16)[name = string("query_19_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(132678976))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133203328))))[name = string("encoder_module_layers_9_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133268928)))];
+            tensor<fp16, [1, 188, 1024]> linear_84_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_q_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = string("linear_84_cast_fp16")];
+            tensor<int32, [4]> var_2091 = const()[name = string("op_2091"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_55_cast_fp16 = reshape(shape = var_2091, x = linear_84_cast_fp16)[name = string("q_55_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133271040))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133795392))))[name = string("encoder_module_layers_9_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133860992)))];
+            tensor<fp16, [1, 188, 1024]> linear_85_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_k_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = string("linear_85_cast_fp16")];
+            tensor<int32, [4]> var_2096 = const()[name = string("op_2096"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_37_cast_fp16 = reshape(shape = var_2096, x = linear_85_cast_fp16)[name = string("k_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133863104))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134387456))))[name = string("encoder_module_layers_9_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134453056)))];
+            tensor<fp16, [1, 188, 1024]> linear_86_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_v_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = string("linear_86_cast_fp16")];
+            tensor<int32, [4]> var_2101 = const()[name = string("op_2101"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_19_cast_fp16 = reshape(shape = var_2101, x = linear_86_cast_fp16)[name = string("v_19_cast_fp16")];
+            tensor<int32, [4]> value_23_perm_0 = const()[name = string("value_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134455168)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2113_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_module_layers_9_self_attn_pos_bias_u_to_fp16)[name = string("op_2113_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134457280)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2115_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_module_layers_9_self_attn_pos_bias_v_to_fp16)[name = string("op_2115_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_19_perm_0 = const()[name = string("q_with_bias_v_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_225_transpose_x_0 = const()[name = string("x_225_transpose_x_0"), val = bool(false)];
+            bool x_225_transpose_y_0 = const()[name = string("x_225_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2117_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134459392))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134651456))))[name = string("op_2117_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_2115_cast_fp16)[name = string("transpose_249")];
+            tensor<fp16, [1, 8, 188, 375]> x_225_cast_fp16 = matmul(transpose_x = x_225_transpose_x_0, transpose_y = x_225_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = op_2117_to_fp16_quantized)[name = string("x_225_cast_fp16")];
+            tensor<int32, [8]> x_227_pad_0 = const()[name = string("x_227_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_227_mode_0 = const()[name = string("x_227_mode_0"), val = string("constant")];
+            fp16 const_178_to_fp16 = const()[name = string("const_178_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_227_cast_fp16 = pad(constant_val = const_178_to_fp16, mode = x_227_mode_0, pad = x_227_pad_0, x = x_225_cast_fp16)[name = string("x_227_cast_fp16")];
+            tensor<int32, [4]> var_2125 = const()[name = string("op_2125"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_229_cast_fp16 = reshape(shape = var_2125, x = x_227_cast_fp16)[name = string("x_229_cast_fp16")];
+            tensor<int32, [4]> var_2129_begin_0 = const()[name = string("op_2129_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2129_end_0 = const()[name = string("op_2129_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2129_end_mask_0 = const()[name = string("op_2129_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2129_cast_fp16 = slice_by_index(begin = var_2129_begin_0, end = var_2129_end_0, end_mask = var_2129_end_mask_0, x = x_229_cast_fp16)[name = string("op_2129_cast_fp16")];
+            tensor<int32, [4]> var_2130 = const()[name = string("op_2130"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_37_cast_fp16 = reshape(shape = var_2130, x = var_2129_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<int32, [4]> transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = string("transpose_247")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_114 = transpose(perm = transpose_114_perm_0, x = var_2113_cast_fp16)[name = string("transpose_248")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_39_begin_0 = const()[name = string("matrix_bd_39_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_39_end_0 = const()[name = string("matrix_bd_39_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_39_end_mask_0 = const()[name = string("matrix_bd_39_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2139_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = string("op_2139_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<fp16, [1, 8, 188, 188]> _inversed_scores_37_cast_fp16 = mul(x = var_2139_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = string("_inversed_scores_37_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_39_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_15)[name = string("scores_39_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2145_cast_fp16 = softmax(axis = var_152, x = scores_39_cast_fp16)[name = string("op_2145_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_509_cast_fp16 = select(a = var_164_to_fp16, b = var_2145_cast_fp16, cond = mask_15)[name = string("input_509_cast_fp16")];
+            bool x_231_transpose_x_0 = const()[name = string("x_231_transpose_x_0"), val = bool(false)];
+            bool x_231_transpose_y_0 = const()[name = string("x_231_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_19_cast_fp16)[name = string("transpose_246")];
+            tensor<fp16, [1, 8, 188, 128]> x_231_cast_fp16 = matmul(transpose_x = x_231_transpose_x_0, transpose_y = x_231_transpose_y_0, x = input_509_cast_fp16, y = value_23_cast_fp16)[name = string("x_231_cast_fp16")];
+            tensor<int32, [4]> var_2149_perm_0 = const()[name = string("op_2149_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2150 = const()[name = string("op_2150"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2149_cast_fp16 = transpose(perm = var_2149_perm_0, x = x_231_cast_fp16)[name = string("transpose_245")];
+            tensor<fp16, [1, 188, 1024]> input_511_cast_fp16 = reshape(shape = var_2150, x = var_2149_cast_fp16)[name = string("input_511_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(134654528))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135178880))))[name = string("encoder_module_layers_9_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135244480)))];
+            tensor<fp16, [1, 188, 1024]> linear_88_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_out_weight_to_fp16_quantized, x = input_511_cast_fp16)[name = string("linear_88_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_515_cast_fp16 = add(x = input_507_cast_fp16, y = linear_88_cast_fp16)[name = string("input_515_cast_fp16")];
+            tensor<int32, [1]> x_235_axes_0 = const()[name = string("x_235_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135246592)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135248704)))];
+            tensor<fp16, [1, 188, 1024]> x_235_cast_fp16 = layer_norm(axes = x_235_axes_0, beta = encoder_module_layers_9_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_conv_weight_to_fp16, x = input_515_cast_fp16)[name = string("x_235_cast_fp16")];
+            tensor<int32, [3]> input_517_perm_0 = const()[name = string("input_517_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_519_pad_type_0 = const()[name = string("input_519_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_519_strides_0 = const()[name = string("input_519_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_519_pad_0 = const()[name = string("input_519_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_519_dilations_0 = const()[name = string("input_519_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_519_groups_0 = const()[name = string("input_519_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135250816))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136299456))))[name = string("encoder_module_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136430592)))];
+            tensor<fp16, [1, 1024, 188]> input_517_cast_fp16 = transpose(perm = input_517_perm_0, x = x_235_cast_fp16)[name = string("transpose_244")];
+            tensor<fp16, [1, 2048, 188]> input_519_cast_fp16 = conv(bias = encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_517_cast_fp16)[name = string("input_519_cast_fp16")];
+            int32 x_237_split_num_splits_0 = const()[name = string("x_237_split_num_splits_0"), val = int32(2)];
+            int32 x_237_split_axis_0 = const()[name = string("x_237_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_237_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_237_split_cast_fp16_1 = split(axis = x_237_split_axis_0, num_splits = x_237_split_num_splits_0, x = input_519_cast_fp16)[name = string("x_237_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_237_split_1_sigmoid_cast_fp16 = sigmoid(x = x_237_split_cast_fp16_1)[name = string("x_237_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_237_cast_fp16 = mul(x = x_237_split_cast_fp16_0, y = x_237_split_1_sigmoid_cast_fp16)[name = string("x_237_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_521_cast_fp16 = select(a = var_164_to_fp16, b = x_237_cast_fp16, cond = var_608)[name = string("input_521_cast_fp16")];
+            tensor<int32, [6]> input_523_pad_0 = const()[name = string("input_523_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_523_mode_0 = const()[name = string("input_523_mode_0"), val = string("constant")];
+            fp16 const_181_to_fp16 = const()[name = string("const_181_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_523_cast_fp16 = pad(constant_val = const_181_to_fp16, mode = input_523_mode_0, pad = input_523_pad_0, x = input_521_cast_fp16)[name = string("input_523_cast_fp16")];
+            string input_525_pad_type_0 = const()[name = string("input_525_pad_type_0"), val = string("valid")];
+            int32 input_525_groups_0 = const()[name = string("input_525_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_525_strides_0 = const()[name = string("input_525_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_525_pad_0 = const()[name = string("input_525_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_525_dilations_0 = const()[name = string("input_525_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_340_to_fp16 = const()[name = string("const_340_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136434752)))];
+            tensor<fp16, [1024]> const_341_to_fp16 = const()[name = string("const_341_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136453248)))];
+            tensor<fp16, [1, 1024, 188]> input_527_cast_fp16 = conv(bias = const_341_to_fp16, dilations = input_525_dilations_0, groups = input_525_groups_0, pad = input_525_pad_0, pad_type = input_525_pad_type_0, strides = input_525_strides_0, weight = const_340_to_fp16, x = input_523_cast_fp16)[name = string("input_527_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_529_cast_fp16 = silu(x = input_527_cast_fp16)[name = string("input_529_cast_fp16")];
+            string x_239_pad_type_0 = const()[name = string("x_239_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_239_strides_0 = const()[name = string("x_239_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_239_pad_0 = const()[name = string("x_239_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_239_dilations_0 = const()[name = string("x_239_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_239_groups_0 = const()[name = string("x_239_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136455360))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136979712))))[name = string("encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137045312)))];
+            tensor<fp16, [1, 1024, 188]> x_239_cast_fp16 = conv(bias = encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16, dilations = x_239_dilations_0, groups = x_239_groups_0, pad = x_239_pad_0, pad_type = x_239_pad_type_0, strides = x_239_strides_0, weight = encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_529_cast_fp16)[name = string("x_239_cast_fp16")];
+            tensor<int32, [3]> input_531_perm_0 = const()[name = string("input_531_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_531_cast_fp16 = transpose(perm = input_531_perm_0, x = x_239_cast_fp16)[name = string("transpose_243")];
+            tensor<fp16, [1, 188, 1024]> input_533_cast_fp16 = add(x = input_515_cast_fp16, y = input_531_cast_fp16)[name = string("input_533_cast_fp16")];
+            tensor<int32, [1]> input_535_axes_0 = const()[name = string("input_535_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137047424)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137049536)))];
+            tensor<fp16, [1, 188, 1024]> input_535_cast_fp16 = layer_norm(axes = input_535_axes_0, beta = encoder_module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_533_cast_fp16)[name = string("input_535_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137051648))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139148864))))[name = string("encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139411072)))];
+            tensor<fp16, [1, 188, 4096]> linear_89_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16_quantized, x = input_535_cast_fp16)[name = string("linear_89_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_539_cast_fp16 = silu(x = linear_89_cast_fp16)[name = string("input_539_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139419328))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141516544))))[name = string("encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141778752)))];
+            tensor<fp16, [1, 188, 1024]> linear_90_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16_quantized, x = input_539_cast_fp16)[name = string("linear_90_cast_fp16")];
+            fp16 var_2216_to_fp16 = const()[name = string("op_2216_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2217_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_2216_to_fp16)[name = string("op_2217_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_545_cast_fp16 = add(x = input_533_cast_fp16, y = var_2217_cast_fp16)[name = string("input_545_cast_fp16")];
+            tensor<int32, [1]> input_547_axes_0 = const()[name = string("input_547_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141780864)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141782976)))];
+            tensor<fp16, [1, 188, 1024]> input_547_cast_fp16 = layer_norm(axes = input_547_axes_0, beta = encoder_module_layers_9_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_out_weight_to_fp16, x = input_545_cast_fp16)[name = string("input_547_cast_fp16")];
+            tensor<int32, [1]> input_549_axes_0 = const()[name = string("input_549_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141785088)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141787200)))];
+            tensor<fp16, [1, 188, 1024]> input_549_cast_fp16 = layer_norm(axes = input_549_axes_0, beta = encoder_module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_547_cast_fp16)[name = string("input_549_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141789312))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143886528))))[name = string("encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144148736)))];
+            tensor<fp16, [1, 188, 4096]> linear_91_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16_quantized, x = input_549_cast_fp16)[name = string("linear_91_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_553_cast_fp16 = silu(x = linear_91_cast_fp16)[name = string("input_553_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(144156992))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146254208))))[name = string("encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146516416)))];
+            tensor<fp16, [1, 188, 1024]> linear_92_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16_quantized, x = input_553_cast_fp16)[name = string("linear_92_cast_fp16")];
+            fp16 var_2247_to_fp16 = const()[name = string("op_2247_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2248_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_2247_to_fp16)[name = string("op_2248_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_559_cast_fp16 = add(x = input_547_cast_fp16, y = var_2248_cast_fp16)[name = string("input_559_cast_fp16")];
+            tensor<int32, [1]> query_21_axes_0 = const()[name = string("query_21_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146518528)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146520640)))];
+            tensor<fp16, [1, 188, 1024]> query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = encoder_module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_self_att_weight_to_fp16, x = input_559_cast_fp16)[name = string("query_21_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146522752))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147047104))))[name = string("encoder_module_layers_10_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147112704)))];
+            tensor<fp16, [1, 188, 1024]> linear_93_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_q_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = string("linear_93_cast_fp16")];
+            tensor<int32, [4]> var_2265 = const()[name = string("op_2265"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_61_cast_fp16 = reshape(shape = var_2265, x = linear_93_cast_fp16)[name = string("q_61_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147114816))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147639168))))[name = string("encoder_module_layers_10_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147704768)))];
+            tensor<fp16, [1, 188, 1024]> linear_94_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_k_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = string("linear_94_cast_fp16")];
+            tensor<int32, [4]> var_2270 = const()[name = string("op_2270"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_41_cast_fp16 = reshape(shape = var_2270, x = linear_94_cast_fp16)[name = string("k_41_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147706880))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148231232))))[name = string("encoder_module_layers_10_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148296832)))];
+            tensor<fp16, [1, 188, 1024]> linear_95_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_v_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = string("linear_95_cast_fp16")];
+            tensor<int32, [4]> var_2275 = const()[name = string("op_2275"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_21_cast_fp16 = reshape(shape = var_2275, x = linear_95_cast_fp16)[name = string("v_21_cast_fp16")];
+            tensor<int32, [4]> value_25_perm_0 = const()[name = string("value_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148298944)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2287_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_module_layers_10_self_attn_pos_bias_u_to_fp16)[name = string("op_2287_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148301056)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2289_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_module_layers_10_self_attn_pos_bias_v_to_fp16)[name = string("op_2289_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_21_perm_0 = const()[name = string("q_with_bias_v_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            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<fp16, [1, 8, 128, 375]> op_2291_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148303168))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148495232))))[name = string("op_2291_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2289_cast_fp16)[name = string("transpose_242")];
+            tensor<fp16, [1, 8, 188, 375]> x_247_cast_fp16 = matmul(transpose_x = x_247_transpose_x_0, transpose_y = x_247_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = op_2291_to_fp16_quantized)[name = string("x_247_cast_fp16")];
+            tensor<int32, [8]> x_249_pad_0 = const()[name = string("x_249_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_249_mode_0 = const()[name = string("x_249_mode_0"), val = string("constant")];
+            fp16 const_188_to_fp16 = const()[name = string("const_188_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_249_cast_fp16 = pad(constant_val = const_188_to_fp16, mode = x_249_mode_0, pad = x_249_pad_0, x = x_247_cast_fp16)[name = string("x_249_cast_fp16")];
+            tensor<int32, [4]> var_2299 = const()[name = string("op_2299"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_251_cast_fp16 = reshape(shape = var_2299, x = x_249_cast_fp16)[name = string("x_251_cast_fp16")];
+            tensor<int32, [4]> var_2303_begin_0 = const()[name = string("op_2303_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2303_end_0 = const()[name = string("op_2303_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2303_end_mask_0 = const()[name = string("op_2303_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2303_cast_fp16 = slice_by_index(begin = var_2303_begin_0, end = var_2303_end_0, end_mask = var_2303_end_mask_0, x = x_251_cast_fp16)[name = string("op_2303_cast_fp16")];
+            tensor<int32, [4]> var_2304 = const()[name = string("op_2304"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_41_cast_fp16 = reshape(shape = var_2304, x = var_2303_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<int32, [4]> transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = string("transpose_240")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_116 = transpose(perm = transpose_116_perm_0, x = var_2287_cast_fp16)[name = string("transpose_241")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_43_begin_0 = const()[name = string("matrix_bd_43_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_43_end_0 = const()[name = string("matrix_bd_43_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_43_end_mask_0 = const()[name = string("matrix_bd_43_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2313_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = string("op_2313_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<fp16, [1, 8, 188, 188]> _inversed_scores_41_cast_fp16 = mul(x = var_2313_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = string("_inversed_scores_41_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_43_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_15)[name = string("scores_43_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2319_cast_fp16 = softmax(axis = var_152, x = scores_43_cast_fp16)[name = string("op_2319_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_561_cast_fp16 = select(a = var_164_to_fp16, b = var_2319_cast_fp16, cond = mask_15)[name = string("input_561_cast_fp16")];
+            bool x_253_transpose_x_0 = const()[name = string("x_253_transpose_x_0"), val = bool(false)];
+            bool x_253_transpose_y_0 = const()[name = string("x_253_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_21_cast_fp16)[name = string("transpose_239")];
+            tensor<fp16, [1, 8, 188, 128]> x_253_cast_fp16 = matmul(transpose_x = x_253_transpose_x_0, transpose_y = x_253_transpose_y_0, x = input_561_cast_fp16, y = value_25_cast_fp16)[name = string("x_253_cast_fp16")];
+            tensor<int32, [4]> var_2323_perm_0 = const()[name = string("op_2323_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2324 = const()[name = string("op_2324"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2323_cast_fp16 = transpose(perm = var_2323_perm_0, x = x_253_cast_fp16)[name = string("transpose_238")];
+            tensor<fp16, [1, 188, 1024]> input_563_cast_fp16 = reshape(shape = var_2324, x = var_2323_cast_fp16)[name = string("input_563_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(148498304))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149022656))))[name = string("encoder_module_layers_10_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149088256)))];
+            tensor<fp16, [1, 188, 1024]> linear_97_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_out_weight_to_fp16_quantized, x = input_563_cast_fp16)[name = string("linear_97_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_567_cast_fp16 = add(x = input_559_cast_fp16, y = linear_97_cast_fp16)[name = string("input_567_cast_fp16")];
+            tensor<int32, [1]> x_257_axes_0 = const()[name = string("x_257_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149090368)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149092480)))];
+            tensor<fp16, [1, 188, 1024]> x_257_cast_fp16 = layer_norm(axes = x_257_axes_0, beta = encoder_module_layers_10_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_conv_weight_to_fp16, x = input_567_cast_fp16)[name = string("x_257_cast_fp16")];
+            tensor<int32, [3]> input_569_perm_0 = const()[name = string("input_569_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_571_pad_type_0 = const()[name = string("input_571_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_571_strides_0 = const()[name = string("input_571_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_571_pad_0 = const()[name = string("input_571_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_571_dilations_0 = const()[name = string("input_571_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_571_groups_0 = const()[name = string("input_571_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149094592))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150143232))))[name = string("encoder_module_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150274368)))];
+            tensor<fp16, [1, 1024, 188]> input_569_cast_fp16 = transpose(perm = input_569_perm_0, x = x_257_cast_fp16)[name = string("transpose_237")];
+            tensor<fp16, [1, 2048, 188]> input_571_cast_fp16 = conv(bias = encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_569_cast_fp16)[name = string("input_571_cast_fp16")];
+            int32 x_259_split_num_splits_0 = const()[name = string("x_259_split_num_splits_0"), val = int32(2)];
+            int32 x_259_split_axis_0 = const()[name = string("x_259_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_259_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_259_split_cast_fp16_1 = split(axis = x_259_split_axis_0, num_splits = x_259_split_num_splits_0, x = input_571_cast_fp16)[name = string("x_259_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_259_split_1_sigmoid_cast_fp16 = sigmoid(x = x_259_split_cast_fp16_1)[name = string("x_259_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_259_cast_fp16 = mul(x = x_259_split_cast_fp16_0, y = x_259_split_1_sigmoid_cast_fp16)[name = string("x_259_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_573_cast_fp16 = select(a = var_164_to_fp16, b = x_259_cast_fp16, cond = var_608)[name = string("input_573_cast_fp16")];
+            tensor<int32, [6]> input_575_pad_0 = const()[name = string("input_575_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_575_mode_0 = const()[name = string("input_575_mode_0"), val = string("constant")];
+            fp16 const_191_to_fp16 = const()[name = string("const_191_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_575_cast_fp16 = pad(constant_val = const_191_to_fp16, mode = input_575_mode_0, pad = input_575_pad_0, x = input_573_cast_fp16)[name = string("input_575_cast_fp16")];
+            string input_577_pad_type_0 = const()[name = string("input_577_pad_type_0"), val = string("valid")];
+            int32 input_577_groups_0 = const()[name = string("input_577_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_577_strides_0 = const()[name = string("input_577_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_577_pad_0 = const()[name = string("input_577_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_577_dilations_0 = const()[name = string("input_577_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_342_to_fp16 = const()[name = string("const_342_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150278528)))];
+            tensor<fp16, [1024]> const_343_to_fp16 = const()[name = string("const_343_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150297024)))];
+            tensor<fp16, [1, 1024, 188]> input_579_cast_fp16 = conv(bias = const_343_to_fp16, dilations = input_577_dilations_0, groups = input_577_groups_0, pad = input_577_pad_0, pad_type = input_577_pad_type_0, strides = input_577_strides_0, weight = const_342_to_fp16, x = input_575_cast_fp16)[name = string("input_579_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_581_cast_fp16 = silu(x = input_579_cast_fp16)[name = string("input_581_cast_fp16")];
+            string x_261_pad_type_0 = const()[name = string("x_261_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_261_strides_0 = const()[name = string("x_261_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_261_pad_0 = const()[name = string("x_261_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_261_dilations_0 = const()[name = string("x_261_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_261_groups_0 = const()[name = string("x_261_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150299136))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150823488))))[name = string("encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150889088)))];
+            tensor<fp16, [1, 1024, 188]> x_261_cast_fp16 = conv(bias = encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16, dilations = x_261_dilations_0, groups = x_261_groups_0, pad = x_261_pad_0, pad_type = x_261_pad_type_0, strides = x_261_strides_0, weight = encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_581_cast_fp16)[name = string("x_261_cast_fp16")];
+            tensor<int32, [3]> input_583_perm_0 = const()[name = string("input_583_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_583_cast_fp16 = transpose(perm = input_583_perm_0, x = x_261_cast_fp16)[name = string("transpose_236")];
+            tensor<fp16, [1, 188, 1024]> input_585_cast_fp16 = add(x = input_567_cast_fp16, y = input_583_cast_fp16)[name = string("input_585_cast_fp16")];
+            tensor<int32, [1]> input_587_axes_0 = const()[name = string("input_587_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150891200)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150893312)))];
+            tensor<fp16, [1, 188, 1024]> input_587_cast_fp16 = layer_norm(axes = input_587_axes_0, beta = encoder_module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_585_cast_fp16)[name = string("input_587_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(150895424))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152992640))))[name = string("encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153254848)))];
+            tensor<fp16, [1, 188, 4096]> linear_98_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16_quantized, x = input_587_cast_fp16)[name = string("linear_98_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_591_cast_fp16 = silu(x = linear_98_cast_fp16)[name = string("input_591_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153263104))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155360320))))[name = string("encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155622528)))];
+            tensor<fp16, [1, 188, 1024]> linear_99_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16_quantized, x = input_591_cast_fp16)[name = string("linear_99_cast_fp16")];
+            fp16 var_2390_to_fp16 = const()[name = string("op_2390_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2391_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2390_to_fp16)[name = string("op_2391_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_597_cast_fp16 = add(x = input_585_cast_fp16, y = var_2391_cast_fp16)[name = string("input_597_cast_fp16")];
+            tensor<int32, [1]> input_599_axes_0 = const()[name = string("input_599_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155624640)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155626752)))];
+            tensor<fp16, [1, 188, 1024]> input_599_cast_fp16 = layer_norm(axes = input_599_axes_0, beta = encoder_module_layers_10_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_out_weight_to_fp16, x = input_597_cast_fp16)[name = string("input_599_cast_fp16")];
+            tensor<int32, [1]> input_601_axes_0 = const()[name = string("input_601_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155628864)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155630976)))];
+            tensor<fp16, [1, 188, 1024]> input_601_cast_fp16 = layer_norm(axes = input_601_axes_0, beta = encoder_module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_599_cast_fp16)[name = string("input_601_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155633088))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157730304))))[name = string("encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(157992512)))];
+            tensor<fp16, [1, 188, 4096]> linear_100_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16_quantized, x = input_601_cast_fp16)[name = string("linear_100_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_605_cast_fp16 = silu(x = linear_100_cast_fp16)[name = string("input_605_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158000768))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160097984))))[name = string("encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160360192)))];
+            tensor<fp16, [1, 188, 1024]> linear_101_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16_quantized, x = input_605_cast_fp16)[name = string("linear_101_cast_fp16")];
+            fp16 var_2421_to_fp16 = const()[name = string("op_2421_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2422_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2421_to_fp16)[name = string("op_2422_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_611_cast_fp16 = add(x = input_599_cast_fp16, y = var_2422_cast_fp16)[name = string("input_611_cast_fp16")];
+            tensor<int32, [1]> query_23_axes_0 = const()[name = string("query_23_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160362304)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160364416)))];
+            tensor<fp16, [1, 188, 1024]> query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = encoder_module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_self_att_weight_to_fp16, x = input_611_cast_fp16)[name = string("query_23_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160366528))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160890880))))[name = string("encoder_module_layers_11_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160956480)))];
+            tensor<fp16, [1, 188, 1024]> linear_102_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_q_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = string("linear_102_cast_fp16")];
+            tensor<int32, [4]> var_2439 = const()[name = string("op_2439"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_67_cast_fp16 = reshape(shape = var_2439, x = linear_102_cast_fp16)[name = string("q_67_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160958592))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161482944))))[name = string("encoder_module_layers_11_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161548544)))];
+            tensor<fp16, [1, 188, 1024]> linear_103_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_k_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = string("linear_103_cast_fp16")];
+            tensor<int32, [4]> var_2444 = const()[name = string("op_2444"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_45_cast_fp16 = reshape(shape = var_2444, x = linear_103_cast_fp16)[name = string("k_45_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161550656))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162075008))))[name = string("encoder_module_layers_11_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162140608)))];
+            tensor<fp16, [1, 188, 1024]> linear_104_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_v_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = string("linear_104_cast_fp16")];
+            tensor<int32, [4]> var_2449 = const()[name = string("op_2449"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_23_cast_fp16 = reshape(shape = var_2449, x = linear_104_cast_fp16)[name = string("v_23_cast_fp16")];
+            tensor<int32, [4]> value_27_perm_0 = const()[name = string("value_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162142720)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2461_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_module_layers_11_self_attn_pos_bias_u_to_fp16)[name = string("op_2461_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162144832)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2463_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_module_layers_11_self_attn_pos_bias_v_to_fp16)[name = string("op_2463_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_23_perm_0 = const()[name = string("q_with_bias_v_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_269_transpose_x_0 = const()[name = string("x_269_transpose_x_0"), val = bool(false)];
+            bool x_269_transpose_y_0 = const()[name = string("x_269_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2465_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162146944))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162339008))))[name = string("op_2465_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2463_cast_fp16)[name = string("transpose_235")];
+            tensor<fp16, [1, 8, 188, 375]> x_269_cast_fp16 = matmul(transpose_x = x_269_transpose_x_0, transpose_y = x_269_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = op_2465_to_fp16_quantized)[name = string("x_269_cast_fp16")];
+            tensor<int32, [8]> x_271_pad_0 = const()[name = string("x_271_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_271_mode_0 = const()[name = string("x_271_mode_0"), val = string("constant")];
+            fp16 const_198_to_fp16 = const()[name = string("const_198_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_271_cast_fp16 = pad(constant_val = const_198_to_fp16, mode = x_271_mode_0, pad = x_271_pad_0, x = x_269_cast_fp16)[name = string("x_271_cast_fp16")];
+            tensor<int32, [4]> var_2473 = const()[name = string("op_2473"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_273_cast_fp16 = reshape(shape = var_2473, x = x_271_cast_fp16)[name = string("x_273_cast_fp16")];
+            tensor<int32, [4]> var_2477_begin_0 = const()[name = string("op_2477_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2477_end_0 = const()[name = string("op_2477_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2477_end_mask_0 = const()[name = string("op_2477_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2477_cast_fp16 = slice_by_index(begin = var_2477_begin_0, end = var_2477_end_0, end_mask = var_2477_end_mask_0, x = x_273_cast_fp16)[name = string("op_2477_cast_fp16")];
+            tensor<int32, [4]> var_2478 = const()[name = string("op_2478"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_45_cast_fp16 = reshape(shape = var_2478, x = var_2477_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<int32, [4]> transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = string("transpose_233")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2461_cast_fp16)[name = string("transpose_234")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_47_begin_0 = const()[name = string("matrix_bd_47_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_47_end_0 = const()[name = string("matrix_bd_47_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_47_end_mask_0 = const()[name = string("matrix_bd_47_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2487_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = string("op_2487_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<fp16, [1, 8, 188, 188]> _inversed_scores_45_cast_fp16 = mul(x = var_2487_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = string("_inversed_scores_45_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_47_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_15)[name = string("scores_47_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2493_cast_fp16 = softmax(axis = var_152, x = scores_47_cast_fp16)[name = string("op_2493_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_613_cast_fp16 = select(a = var_164_to_fp16, b = var_2493_cast_fp16, cond = mask_15)[name = string("input_613_cast_fp16")];
+            bool x_275_transpose_x_0 = const()[name = string("x_275_transpose_x_0"), val = bool(false)];
+            bool x_275_transpose_y_0 = const()[name = string("x_275_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_23_cast_fp16)[name = string("transpose_232")];
+            tensor<fp16, [1, 8, 188, 128]> x_275_cast_fp16 = matmul(transpose_x = x_275_transpose_x_0, transpose_y = x_275_transpose_y_0, x = input_613_cast_fp16, y = value_27_cast_fp16)[name = string("x_275_cast_fp16")];
+            tensor<int32, [4]> var_2497_perm_0 = const()[name = string("op_2497_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2498 = const()[name = string("op_2498"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2497_cast_fp16 = transpose(perm = var_2497_perm_0, x = x_275_cast_fp16)[name = string("transpose_231")];
+            tensor<fp16, [1, 188, 1024]> input_615_cast_fp16 = reshape(shape = var_2498, x = var_2497_cast_fp16)[name = string("input_615_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162342080))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162866432))))[name = string("encoder_module_layers_11_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162932032)))];
+            tensor<fp16, [1, 188, 1024]> linear_106_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_out_weight_to_fp16_quantized, x = input_615_cast_fp16)[name = string("linear_106_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_619_cast_fp16 = add(x = input_611_cast_fp16, y = linear_106_cast_fp16)[name = string("input_619_cast_fp16")];
+            tensor<int32, [1]> x_279_axes_0 = const()[name = string("x_279_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162934144)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162936256)))];
+            tensor<fp16, [1, 188, 1024]> x_279_cast_fp16 = layer_norm(axes = x_279_axes_0, beta = encoder_module_layers_11_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_conv_weight_to_fp16, x = input_619_cast_fp16)[name = string("x_279_cast_fp16")];
+            tensor<int32, [3]> input_621_perm_0 = const()[name = string("input_621_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_623_pad_type_0 = const()[name = string("input_623_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_623_strides_0 = const()[name = string("input_623_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_623_pad_0 = const()[name = string("input_623_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_623_dilations_0 = const()[name = string("input_623_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_623_groups_0 = const()[name = string("input_623_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162938368))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163987008))))[name = string("encoder_module_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164118144)))];
+            tensor<fp16, [1, 1024, 188]> input_621_cast_fp16 = transpose(perm = input_621_perm_0, x = x_279_cast_fp16)[name = string("transpose_230")];
+            tensor<fp16, [1, 2048, 188]> input_623_cast_fp16 = conv(bias = encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_621_cast_fp16)[name = string("input_623_cast_fp16")];
+            int32 x_281_split_num_splits_0 = const()[name = string("x_281_split_num_splits_0"), val = int32(2)];
+            int32 x_281_split_axis_0 = const()[name = string("x_281_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_281_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_281_split_cast_fp16_1 = split(axis = x_281_split_axis_0, num_splits = x_281_split_num_splits_0, x = input_623_cast_fp16)[name = string("x_281_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_281_split_1_sigmoid_cast_fp16 = sigmoid(x = x_281_split_cast_fp16_1)[name = string("x_281_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_281_cast_fp16 = mul(x = x_281_split_cast_fp16_0, y = x_281_split_1_sigmoid_cast_fp16)[name = string("x_281_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_625_cast_fp16 = select(a = var_164_to_fp16, b = x_281_cast_fp16, cond = var_608)[name = string("input_625_cast_fp16")];
+            tensor<int32, [6]> input_627_pad_0 = const()[name = string("input_627_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_627_mode_0 = const()[name = string("input_627_mode_0"), val = string("constant")];
+            fp16 const_201_to_fp16 = const()[name = string("const_201_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_627_cast_fp16 = pad(constant_val = const_201_to_fp16, mode = input_627_mode_0, pad = input_627_pad_0, x = input_625_cast_fp16)[name = string("input_627_cast_fp16")];
+            string input_629_pad_type_0 = const()[name = string("input_629_pad_type_0"), val = string("valid")];
+            int32 input_629_groups_0 = const()[name = string("input_629_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_629_strides_0 = const()[name = string("input_629_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_629_pad_0 = const()[name = string("input_629_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_629_dilations_0 = const()[name = string("input_629_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_344_to_fp16 = const()[name = string("const_344_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164122304)))];
+            tensor<fp16, [1024]> const_345_to_fp16 = const()[name = string("const_345_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164140800)))];
+            tensor<fp16, [1, 1024, 188]> input_631_cast_fp16 = conv(bias = const_345_to_fp16, dilations = input_629_dilations_0, groups = input_629_groups_0, pad = input_629_pad_0, pad_type = input_629_pad_type_0, strides = input_629_strides_0, weight = const_344_to_fp16, x = input_627_cast_fp16)[name = string("input_631_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_633_cast_fp16 = silu(x = input_631_cast_fp16)[name = string("input_633_cast_fp16")];
+            string x_283_pad_type_0 = const()[name = string("x_283_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_283_strides_0 = const()[name = string("x_283_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_283_pad_0 = const()[name = string("x_283_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_283_dilations_0 = const()[name = string("x_283_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_283_groups_0 = const()[name = string("x_283_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164142912))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164667264))))[name = string("encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164732864)))];
+            tensor<fp16, [1, 1024, 188]> x_283_cast_fp16 = conv(bias = encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16, dilations = x_283_dilations_0, groups = x_283_groups_0, pad = x_283_pad_0, pad_type = x_283_pad_type_0, strides = x_283_strides_0, weight = encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_633_cast_fp16)[name = string("x_283_cast_fp16")];
+            tensor<int32, [3]> input_635_perm_0 = const()[name = string("input_635_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_635_cast_fp16 = transpose(perm = input_635_perm_0, x = x_283_cast_fp16)[name = string("transpose_229")];
+            tensor<fp16, [1, 188, 1024]> input_637_cast_fp16 = add(x = input_619_cast_fp16, y = input_635_cast_fp16)[name = string("input_637_cast_fp16")];
+            tensor<int32, [1]> input_639_axes_0 = const()[name = string("input_639_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164734976)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164737088)))];
+            tensor<fp16, [1, 188, 1024]> input_639_cast_fp16 = layer_norm(axes = input_639_axes_0, beta = encoder_module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_637_cast_fp16)[name = string("input_639_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164739200))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166836416))))[name = string("encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167098624)))];
+            tensor<fp16, [1, 188, 4096]> linear_107_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16_quantized, x = input_639_cast_fp16)[name = string("linear_107_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_643_cast_fp16 = silu(x = linear_107_cast_fp16)[name = string("input_643_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167106880))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169204096))))[name = string("encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169466304)))];
+            tensor<fp16, [1, 188, 1024]> linear_108_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16_quantized, x = input_643_cast_fp16)[name = string("linear_108_cast_fp16")];
+            fp16 var_2564_to_fp16 = const()[name = string("op_2564_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2565_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2564_to_fp16)[name = string("op_2565_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_649_cast_fp16 = add(x = input_637_cast_fp16, y = var_2565_cast_fp16)[name = string("input_649_cast_fp16")];
+            tensor<int32, [1]> input_651_axes_0 = const()[name = string("input_651_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169468416)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169470528)))];
+            tensor<fp16, [1, 188, 1024]> input_651_cast_fp16 = layer_norm(axes = input_651_axes_0, beta = encoder_module_layers_11_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_out_weight_to_fp16, x = input_649_cast_fp16)[name = string("input_651_cast_fp16")];
+            tensor<int32, [1]> input_653_axes_0 = const()[name = string("input_653_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169472640)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169474752)))];
+            tensor<fp16, [1, 188, 1024]> input_653_cast_fp16 = layer_norm(axes = input_653_axes_0, beta = encoder_module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_651_cast_fp16)[name = string("input_653_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169476864))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171574080))))[name = string("encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171836288)))];
+            tensor<fp16, [1, 188, 4096]> linear_109_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16_quantized, x = input_653_cast_fp16)[name = string("linear_109_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_657_cast_fp16 = silu(x = linear_109_cast_fp16)[name = string("input_657_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171844544))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173941760))))[name = string("encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174203968)))];
+            tensor<fp16, [1, 188, 1024]> linear_110_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16_quantized, x = input_657_cast_fp16)[name = string("linear_110_cast_fp16")];
+            fp16 var_2595_to_fp16 = const()[name = string("op_2595_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2596_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2595_to_fp16)[name = string("op_2596_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_663_cast_fp16 = add(x = input_651_cast_fp16, y = var_2596_cast_fp16)[name = string("input_663_cast_fp16")];
+            tensor<int32, [1]> query_25_axes_0 = const()[name = string("query_25_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174206080)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174208192)))];
+            tensor<fp16, [1, 188, 1024]> query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = encoder_module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_self_att_weight_to_fp16, x = input_663_cast_fp16)[name = string("query_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174210304))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174734656))))[name = string("encoder_module_layers_12_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174800256)))];
+            tensor<fp16, [1, 188, 1024]> linear_111_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_q_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = string("linear_111_cast_fp16")];
+            tensor<int32, [4]> var_2613 = const()[name = string("op_2613"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_73_cast_fp16 = reshape(shape = var_2613, x = linear_111_cast_fp16)[name = string("q_73_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174802368))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175326720))))[name = string("encoder_module_layers_12_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175392320)))];
+            tensor<fp16, [1, 188, 1024]> linear_112_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_k_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = string("linear_112_cast_fp16")];
+            tensor<int32, [4]> var_2618 = const()[name = string("op_2618"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_49_cast_fp16 = reshape(shape = var_2618, x = linear_112_cast_fp16)[name = string("k_49_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175394432))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175918784))))[name = string("encoder_module_layers_12_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175984384)))];
+            tensor<fp16, [1, 188, 1024]> linear_113_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_v_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = string("linear_113_cast_fp16")];
+            tensor<int32, [4]> var_2623 = const()[name = string("op_2623"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_25_cast_fp16 = reshape(shape = var_2623, x = linear_113_cast_fp16)[name = string("v_25_cast_fp16")];
+            tensor<int32, [4]> value_29_perm_0 = const()[name = string("value_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175986496)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2635_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_module_layers_12_self_attn_pos_bias_u_to_fp16)[name = string("op_2635_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175988608)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2637_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_module_layers_12_self_attn_pos_bias_v_to_fp16)[name = string("op_2637_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_25_perm_0 = const()[name = string("q_with_bias_v_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_291_transpose_x_0 = const()[name = string("x_291_transpose_x_0"), val = bool(false)];
+            bool x_291_transpose_y_0 = const()[name = string("x_291_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2639_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175990720))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176182784))))[name = string("op_2639_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2637_cast_fp16)[name = string("transpose_228")];
+            tensor<fp16, [1, 8, 188, 375]> x_291_cast_fp16 = matmul(transpose_x = x_291_transpose_x_0, transpose_y = x_291_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = op_2639_to_fp16_quantized)[name = string("x_291_cast_fp16")];
+            tensor<int32, [8]> x_293_pad_0 = const()[name = string("x_293_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_293_mode_0 = const()[name = string("x_293_mode_0"), val = string("constant")];
+            fp16 const_208_to_fp16 = const()[name = string("const_208_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_293_cast_fp16 = pad(constant_val = const_208_to_fp16, mode = x_293_mode_0, pad = x_293_pad_0, x = x_291_cast_fp16)[name = string("x_293_cast_fp16")];
+            tensor<int32, [4]> var_2647 = const()[name = string("op_2647"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_295_cast_fp16 = reshape(shape = var_2647, x = x_293_cast_fp16)[name = string("x_295_cast_fp16")];
+            tensor<int32, [4]> var_2651_begin_0 = const()[name = string("op_2651_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2651_end_0 = const()[name = string("op_2651_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2651_end_mask_0 = const()[name = string("op_2651_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2651_cast_fp16 = slice_by_index(begin = var_2651_begin_0, end = var_2651_end_0, end_mask = var_2651_end_mask_0, x = x_295_cast_fp16)[name = string("op_2651_cast_fp16")];
+            tensor<int32, [4]> var_2652 = const()[name = string("op_2652"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_49_cast_fp16 = reshape(shape = var_2652, x = var_2651_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<int32, [4]> transpose_120_perm_0 = const()[name = string("transpose_120_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_121_perm_0 = const()[name = string("transpose_121_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = string("transpose_226")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_120 = transpose(perm = transpose_120_perm_0, x = var_2635_cast_fp16)[name = string("transpose_227")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_51_begin_0 = const()[name = string("matrix_bd_51_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_51_end_0 = const()[name = string("matrix_bd_51_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_51_end_mask_0 = const()[name = string("matrix_bd_51_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2661_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = string("op_2661_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<fp16, [1, 8, 188, 188]> _inversed_scores_49_cast_fp16 = mul(x = var_2661_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = string("_inversed_scores_49_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_51_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_15)[name = string("scores_51_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2667_cast_fp16 = softmax(axis = var_152, x = scores_51_cast_fp16)[name = string("op_2667_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_665_cast_fp16 = select(a = var_164_to_fp16, b = var_2667_cast_fp16, cond = mask_15)[name = string("input_665_cast_fp16")];
+            bool x_297_transpose_x_0 = const()[name = string("x_297_transpose_x_0"), val = bool(false)];
+            bool x_297_transpose_y_0 = const()[name = string("x_297_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_25_cast_fp16)[name = string("transpose_225")];
+            tensor<fp16, [1, 8, 188, 128]> x_297_cast_fp16 = matmul(transpose_x = x_297_transpose_x_0, transpose_y = x_297_transpose_y_0, x = input_665_cast_fp16, y = value_29_cast_fp16)[name = string("x_297_cast_fp16")];
+            tensor<int32, [4]> var_2671_perm_0 = const()[name = string("op_2671_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2672 = const()[name = string("op_2672"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2671_cast_fp16 = transpose(perm = var_2671_perm_0, x = x_297_cast_fp16)[name = string("transpose_224")];
+            tensor<fp16, [1, 188, 1024]> input_667_cast_fp16 = reshape(shape = var_2672, x = var_2671_cast_fp16)[name = string("input_667_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176185856))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176710208))))[name = string("encoder_module_layers_12_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176775808)))];
+            tensor<fp16, [1, 188, 1024]> linear_115_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_out_weight_to_fp16_quantized, x = input_667_cast_fp16)[name = string("linear_115_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_671_cast_fp16 = add(x = input_663_cast_fp16, y = linear_115_cast_fp16)[name = string("input_671_cast_fp16")];
+            tensor<int32, [1]> x_301_axes_0 = const()[name = string("x_301_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176777920)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176780032)))];
+            tensor<fp16, [1, 188, 1024]> x_301_cast_fp16 = layer_norm(axes = x_301_axes_0, beta = encoder_module_layers_12_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_conv_weight_to_fp16, x = input_671_cast_fp16)[name = string("x_301_cast_fp16")];
+            tensor<int32, [3]> input_673_perm_0 = const()[name = string("input_673_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_675_pad_type_0 = const()[name = string("input_675_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_675_strides_0 = const()[name = string("input_675_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_675_pad_0 = const()[name = string("input_675_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_675_dilations_0 = const()[name = string("input_675_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_675_groups_0 = const()[name = string("input_675_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176782144))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177830784))))[name = string("encoder_module_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177961920)))];
+            tensor<fp16, [1, 1024, 188]> input_673_cast_fp16 = transpose(perm = input_673_perm_0, x = x_301_cast_fp16)[name = string("transpose_223")];
+            tensor<fp16, [1, 2048, 188]> input_675_cast_fp16 = conv(bias = encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_673_cast_fp16)[name = string("input_675_cast_fp16")];
+            int32 x_303_split_num_splits_0 = const()[name = string("x_303_split_num_splits_0"), val = int32(2)];
+            int32 x_303_split_axis_0 = const()[name = string("x_303_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_303_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_303_split_cast_fp16_1 = split(axis = x_303_split_axis_0, num_splits = x_303_split_num_splits_0, x = input_675_cast_fp16)[name = string("x_303_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_303_split_1_sigmoid_cast_fp16 = sigmoid(x = x_303_split_cast_fp16_1)[name = string("x_303_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_303_cast_fp16 = mul(x = x_303_split_cast_fp16_0, y = x_303_split_1_sigmoid_cast_fp16)[name = string("x_303_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_677_cast_fp16 = select(a = var_164_to_fp16, b = x_303_cast_fp16, cond = var_608)[name = string("input_677_cast_fp16")];
+            tensor<int32, [6]> input_679_pad_0 = const()[name = string("input_679_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_679_mode_0 = const()[name = string("input_679_mode_0"), val = string("constant")];
+            fp16 const_211_to_fp16 = const()[name = string("const_211_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_679_cast_fp16 = pad(constant_val = const_211_to_fp16, mode = input_679_mode_0, pad = input_679_pad_0, x = input_677_cast_fp16)[name = string("input_679_cast_fp16")];
+            string input_681_pad_type_0 = const()[name = string("input_681_pad_type_0"), val = string("valid")];
+            int32 input_681_groups_0 = const()[name = string("input_681_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_681_strides_0 = const()[name = string("input_681_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_681_pad_0 = const()[name = string("input_681_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_681_dilations_0 = const()[name = string("input_681_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_346_to_fp16 = const()[name = string("const_346_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177966080)))];
+            tensor<fp16, [1024]> const_347_to_fp16 = const()[name = string("const_347_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177984576)))];
+            tensor<fp16, [1, 1024, 188]> input_683_cast_fp16 = conv(bias = const_347_to_fp16, dilations = input_681_dilations_0, groups = input_681_groups_0, pad = input_681_pad_0, pad_type = input_681_pad_type_0, strides = input_681_strides_0, weight = const_346_to_fp16, x = input_679_cast_fp16)[name = string("input_683_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_685_cast_fp16 = silu(x = input_683_cast_fp16)[name = string("input_685_cast_fp16")];
+            string x_305_pad_type_0 = const()[name = string("x_305_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_305_strides_0 = const()[name = string("x_305_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_305_pad_0 = const()[name = string("x_305_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_305_dilations_0 = const()[name = string("x_305_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_305_groups_0 = const()[name = string("x_305_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(177986688))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178511040))))[name = string("encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178576640)))];
+            tensor<fp16, [1, 1024, 188]> x_305_cast_fp16 = conv(bias = encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16, dilations = x_305_dilations_0, groups = x_305_groups_0, pad = x_305_pad_0, pad_type = x_305_pad_type_0, strides = x_305_strides_0, weight = encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_685_cast_fp16)[name = string("x_305_cast_fp16")];
+            tensor<int32, [3]> input_687_perm_0 = const()[name = string("input_687_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_687_cast_fp16 = transpose(perm = input_687_perm_0, x = x_305_cast_fp16)[name = string("transpose_222")];
+            tensor<fp16, [1, 188, 1024]> input_689_cast_fp16 = add(x = input_671_cast_fp16, y = input_687_cast_fp16)[name = string("input_689_cast_fp16")];
+            tensor<int32, [1]> input_691_axes_0 = const()[name = string("input_691_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178578752)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178580864)))];
+            tensor<fp16, [1, 188, 1024]> input_691_cast_fp16 = layer_norm(axes = input_691_axes_0, beta = encoder_module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_689_cast_fp16)[name = string("input_691_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178582976))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180680192))))[name = string("encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180942400)))];
+            tensor<fp16, [1, 188, 4096]> linear_116_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16_quantized, x = input_691_cast_fp16)[name = string("linear_116_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_695_cast_fp16 = silu(x = linear_116_cast_fp16)[name = string("input_695_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180950656))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183047872))))[name = string("encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183310080)))];
+            tensor<fp16, [1, 188, 1024]> linear_117_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16_quantized, x = input_695_cast_fp16)[name = string("linear_117_cast_fp16")];
+            fp16 var_2738_to_fp16 = const()[name = string("op_2738_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2739_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2738_to_fp16)[name = string("op_2739_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_701_cast_fp16 = add(x = input_689_cast_fp16, y = var_2739_cast_fp16)[name = string("input_701_cast_fp16")];
+            tensor<int32, [1]> input_703_axes_0 = const()[name = string("input_703_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183312192)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183314304)))];
+            tensor<fp16, [1, 188, 1024]> input_703_cast_fp16 = layer_norm(axes = input_703_axes_0, beta = encoder_module_layers_12_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_out_weight_to_fp16, x = input_701_cast_fp16)[name = string("input_703_cast_fp16")];
+            tensor<int32, [1]> input_705_axes_0 = const()[name = string("input_705_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183316416)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183318528)))];
+            tensor<fp16, [1, 188, 1024]> input_705_cast_fp16 = layer_norm(axes = input_705_axes_0, beta = encoder_module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_703_cast_fp16)[name = string("input_705_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(183320640))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185417856))))[name = string("encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185680064)))];
+            tensor<fp16, [1, 188, 4096]> linear_118_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16_quantized, x = input_705_cast_fp16)[name = string("linear_118_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_709_cast_fp16 = silu(x = linear_118_cast_fp16)[name = string("input_709_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185688320))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187785536))))[name = string("encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188047744)))];
+            tensor<fp16, [1, 188, 1024]> linear_119_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16_quantized, x = input_709_cast_fp16)[name = string("linear_119_cast_fp16")];
+            fp16 var_2769_to_fp16 = const()[name = string("op_2769_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2770_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2769_to_fp16)[name = string("op_2770_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_715_cast_fp16 = add(x = input_703_cast_fp16, y = var_2770_cast_fp16)[name = string("input_715_cast_fp16")];
+            tensor<int32, [1]> query_27_axes_0 = const()[name = string("query_27_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188049856)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188051968)))];
+            tensor<fp16, [1, 188, 1024]> query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = encoder_module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_self_att_weight_to_fp16, x = input_715_cast_fp16)[name = string("query_27_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188054080))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188578432))))[name = string("encoder_module_layers_13_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188644032)))];
+            tensor<fp16, [1, 188, 1024]> linear_120_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_q_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = string("linear_120_cast_fp16")];
+            tensor<int32, [4]> var_2787 = const()[name = string("op_2787"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_79_cast_fp16 = reshape(shape = var_2787, x = linear_120_cast_fp16)[name = string("q_79_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188646144))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189170496))))[name = string("encoder_module_layers_13_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189236096)))];
+            tensor<fp16, [1, 188, 1024]> linear_121_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_k_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = string("linear_121_cast_fp16")];
+            tensor<int32, [4]> var_2792 = const()[name = string("op_2792"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_53_cast_fp16 = reshape(shape = var_2792, x = linear_121_cast_fp16)[name = string("k_53_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189238208))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189762560))))[name = string("encoder_module_layers_13_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189828160)))];
+            tensor<fp16, [1, 188, 1024]> linear_122_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_v_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = string("linear_122_cast_fp16")];
+            tensor<int32, [4]> var_2797 = const()[name = string("op_2797"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_27_cast_fp16 = reshape(shape = var_2797, x = linear_122_cast_fp16)[name = string("v_27_cast_fp16")];
+            tensor<int32, [4]> value_31_perm_0 = const()[name = string("value_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189830272)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2809_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_module_layers_13_self_attn_pos_bias_u_to_fp16)[name = string("op_2809_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189832384)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2811_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_module_layers_13_self_attn_pos_bias_v_to_fp16)[name = string("op_2811_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_27_perm_0 = const()[name = string("q_with_bias_v_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_313_transpose_x_0 = const()[name = string("x_313_transpose_x_0"), val = bool(false)];
+            bool x_313_transpose_y_0 = const()[name = string("x_313_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2813_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189834496))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190026560))))[name = string("op_2813_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2811_cast_fp16)[name = string("transpose_221")];
+            tensor<fp16, [1, 8, 188, 375]> x_313_cast_fp16 = matmul(transpose_x = x_313_transpose_x_0, transpose_y = x_313_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = op_2813_to_fp16_quantized)[name = string("x_313_cast_fp16")];
+            tensor<int32, [8]> x_315_pad_0 = const()[name = string("x_315_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_315_mode_0 = const()[name = string("x_315_mode_0"), val = string("constant")];
+            fp16 const_218_to_fp16 = const()[name = string("const_218_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_315_cast_fp16 = pad(constant_val = const_218_to_fp16, mode = x_315_mode_0, pad = x_315_pad_0, x = x_313_cast_fp16)[name = string("x_315_cast_fp16")];
+            tensor<int32, [4]> var_2821 = const()[name = string("op_2821"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_317_cast_fp16 = reshape(shape = var_2821, x = x_315_cast_fp16)[name = string("x_317_cast_fp16")];
+            tensor<int32, [4]> var_2825_begin_0 = const()[name = string("op_2825_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2825_end_0 = const()[name = string("op_2825_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2825_end_mask_0 = const()[name = string("op_2825_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2825_cast_fp16 = slice_by_index(begin = var_2825_begin_0, end = var_2825_end_0, end_mask = var_2825_end_mask_0, x = x_317_cast_fp16)[name = string("op_2825_cast_fp16")];
+            tensor<int32, [4]> var_2826 = const()[name = string("op_2826"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_53_cast_fp16 = reshape(shape = var_2826, x = var_2825_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<int32, [4]> transpose_122_perm_0 = const()[name = string("transpose_122_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_123_perm_0 = const()[name = string("transpose_123_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = string("transpose_219")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_122 = transpose(perm = transpose_122_perm_0, x = var_2809_cast_fp16)[name = string("transpose_220")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_55_begin_0 = const()[name = string("matrix_bd_55_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_55_end_0 = const()[name = string("matrix_bd_55_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_55_end_mask_0 = const()[name = string("matrix_bd_55_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2835_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = string("op_2835_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<fp16, [1, 8, 188, 188]> _inversed_scores_53_cast_fp16 = mul(x = var_2835_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = string("_inversed_scores_53_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_55_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_15)[name = string("scores_55_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2841_cast_fp16 = softmax(axis = var_152, x = scores_55_cast_fp16)[name = string("op_2841_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_717_cast_fp16 = select(a = var_164_to_fp16, b = var_2841_cast_fp16, cond = mask_15)[name = string("input_717_cast_fp16")];
+            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<fp16, [1, 8, 188, 128]> value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_27_cast_fp16)[name = string("transpose_218")];
+            tensor<fp16, [1, 8, 188, 128]> x_319_cast_fp16 = matmul(transpose_x = x_319_transpose_x_0, transpose_y = x_319_transpose_y_0, x = input_717_cast_fp16, y = value_31_cast_fp16)[name = string("x_319_cast_fp16")];
+            tensor<int32, [4]> var_2845_perm_0 = const()[name = string("op_2845_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2846 = const()[name = string("op_2846"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2845_cast_fp16 = transpose(perm = var_2845_perm_0, x = x_319_cast_fp16)[name = string("transpose_217")];
+            tensor<fp16, [1, 188, 1024]> input_719_cast_fp16 = reshape(shape = var_2846, x = var_2845_cast_fp16)[name = string("input_719_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190029632))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190553984))))[name = string("encoder_module_layers_13_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190619584)))];
+            tensor<fp16, [1, 188, 1024]> linear_124_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_out_weight_to_fp16_quantized, x = input_719_cast_fp16)[name = string("linear_124_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_723_cast_fp16 = add(x = input_715_cast_fp16, y = linear_124_cast_fp16)[name = string("input_723_cast_fp16")];
+            tensor<int32, [1]> x_323_axes_0 = const()[name = string("x_323_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190621696)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190623808)))];
+            tensor<fp16, [1, 188, 1024]> x_323_cast_fp16 = layer_norm(axes = x_323_axes_0, beta = encoder_module_layers_13_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_conv_weight_to_fp16, x = input_723_cast_fp16)[name = string("x_323_cast_fp16")];
+            tensor<int32, [3]> input_725_perm_0 = const()[name = string("input_725_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_727_pad_type_0 = const()[name = string("input_727_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_727_strides_0 = const()[name = string("input_727_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_727_pad_0 = const()[name = string("input_727_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_727_dilations_0 = const()[name = string("input_727_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_727_groups_0 = const()[name = string("input_727_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(190625920))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191674560))))[name = string("encoder_module_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191805696)))];
+            tensor<fp16, [1, 1024, 188]> input_725_cast_fp16 = transpose(perm = input_725_perm_0, x = x_323_cast_fp16)[name = string("transpose_216")];
+            tensor<fp16, [1, 2048, 188]> input_727_cast_fp16 = conv(bias = encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_725_cast_fp16)[name = string("input_727_cast_fp16")];
+            int32 x_325_split_num_splits_0 = const()[name = string("x_325_split_num_splits_0"), val = int32(2)];
+            int32 x_325_split_axis_0 = const()[name = string("x_325_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_325_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_325_split_cast_fp16_1 = split(axis = x_325_split_axis_0, num_splits = x_325_split_num_splits_0, x = input_727_cast_fp16)[name = string("x_325_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_325_split_1_sigmoid_cast_fp16 = sigmoid(x = x_325_split_cast_fp16_1)[name = string("x_325_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_325_cast_fp16 = mul(x = x_325_split_cast_fp16_0, y = x_325_split_1_sigmoid_cast_fp16)[name = string("x_325_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_729_cast_fp16 = select(a = var_164_to_fp16, b = x_325_cast_fp16, cond = var_608)[name = string("input_729_cast_fp16")];
+            tensor<int32, [6]> input_731_pad_0 = const()[name = string("input_731_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_731_mode_0 = const()[name = string("input_731_mode_0"), val = string("constant")];
+            fp16 const_221_to_fp16 = const()[name = string("const_221_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_731_cast_fp16 = pad(constant_val = const_221_to_fp16, mode = input_731_mode_0, pad = input_731_pad_0, x = input_729_cast_fp16)[name = string("input_731_cast_fp16")];
+            string input_733_pad_type_0 = const()[name = string("input_733_pad_type_0"), val = string("valid")];
+            int32 input_733_groups_0 = const()[name = string("input_733_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_733_strides_0 = const()[name = string("input_733_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_733_pad_0 = const()[name = string("input_733_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_733_dilations_0 = const()[name = string("input_733_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_348_to_fp16 = const()[name = string("const_348_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191809856)))];
+            tensor<fp16, [1024]> const_349_to_fp16 = const()[name = string("const_349_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191828352)))];
+            tensor<fp16, [1, 1024, 188]> input_735_cast_fp16 = conv(bias = const_349_to_fp16, dilations = input_733_dilations_0, groups = input_733_groups_0, pad = input_733_pad_0, pad_type = input_733_pad_type_0, strides = input_733_strides_0, weight = const_348_to_fp16, x = input_731_cast_fp16)[name = string("input_735_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_737_cast_fp16 = silu(x = input_735_cast_fp16)[name = string("input_737_cast_fp16")];
+            string x_327_pad_type_0 = const()[name = string("x_327_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_327_strides_0 = const()[name = string("x_327_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_327_pad_0 = const()[name = string("x_327_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_327_dilations_0 = const()[name = string("x_327_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_327_groups_0 = const()[name = string("x_327_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191830464))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192354816))))[name = string("encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192420416)))];
+            tensor<fp16, [1, 1024, 188]> x_327_cast_fp16 = conv(bias = encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16, dilations = x_327_dilations_0, groups = x_327_groups_0, pad = x_327_pad_0, pad_type = x_327_pad_type_0, strides = x_327_strides_0, weight = encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_737_cast_fp16)[name = string("x_327_cast_fp16")];
+            tensor<int32, [3]> input_739_perm_0 = const()[name = string("input_739_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_739_cast_fp16 = transpose(perm = input_739_perm_0, x = x_327_cast_fp16)[name = string("transpose_215")];
+            tensor<fp16, [1, 188, 1024]> input_741_cast_fp16 = add(x = input_723_cast_fp16, y = input_739_cast_fp16)[name = string("input_741_cast_fp16")];
+            tensor<int32, [1]> input_743_axes_0 = const()[name = string("input_743_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192422528)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192424640)))];
+            tensor<fp16, [1, 188, 1024]> input_743_cast_fp16 = layer_norm(axes = input_743_axes_0, beta = encoder_module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_741_cast_fp16)[name = string("input_743_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192426752))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194523968))))[name = string("encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194786176)))];
+            tensor<fp16, [1, 188, 4096]> linear_125_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16_quantized, x = input_743_cast_fp16)[name = string("linear_125_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_747_cast_fp16 = silu(x = linear_125_cast_fp16)[name = string("input_747_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194794432))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196891648))))[name = string("encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197153856)))];
+            tensor<fp16, [1, 188, 1024]> linear_126_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16_quantized, x = input_747_cast_fp16)[name = string("linear_126_cast_fp16")];
+            fp16 var_2912_to_fp16 = const()[name = string("op_2912_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2913_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2912_to_fp16)[name = string("op_2913_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_753_cast_fp16 = add(x = input_741_cast_fp16, y = var_2913_cast_fp16)[name = string("input_753_cast_fp16")];
+            tensor<int32, [1]> input_755_axes_0 = const()[name = string("input_755_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197155968)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197158080)))];
+            tensor<fp16, [1, 188, 1024]> input_755_cast_fp16 = layer_norm(axes = input_755_axes_0, beta = encoder_module_layers_13_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_out_weight_to_fp16, x = input_753_cast_fp16)[name = string("input_755_cast_fp16")];
+            tensor<int32, [1]> input_757_axes_0 = const()[name = string("input_757_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197160192)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197162304)))];
+            tensor<fp16, [1, 188, 1024]> input_757_cast_fp16 = layer_norm(axes = input_757_axes_0, beta = encoder_module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_755_cast_fp16)[name = string("input_757_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197164416))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199261632))))[name = string("encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199523840)))];
+            tensor<fp16, [1, 188, 4096]> linear_127_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16_quantized, x = input_757_cast_fp16)[name = string("linear_127_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_761_cast_fp16 = silu(x = linear_127_cast_fp16)[name = string("input_761_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(199532096))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201629312))))[name = string("encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201891520)))];
+            tensor<fp16, [1, 188, 1024]> linear_128_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16_quantized, x = input_761_cast_fp16)[name = string("linear_128_cast_fp16")];
+            fp16 var_2943_to_fp16 = const()[name = string("op_2943_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2944_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2943_to_fp16)[name = string("op_2944_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_767_cast_fp16 = add(x = input_755_cast_fp16, y = var_2944_cast_fp16)[name = string("input_767_cast_fp16")];
+            tensor<int32, [1]> query_29_axes_0 = const()[name = string("query_29_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201893632)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201895744)))];
+            tensor<fp16, [1, 188, 1024]> query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = encoder_module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_self_att_weight_to_fp16, x = input_767_cast_fp16)[name = string("query_29_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201897856))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202422208))))[name = string("encoder_module_layers_14_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202487808)))];
+            tensor<fp16, [1, 188, 1024]> linear_129_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_q_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = string("linear_129_cast_fp16")];
+            tensor<int32, [4]> var_2961 = const()[name = string("op_2961"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_85_cast_fp16 = reshape(shape = var_2961, x = linear_129_cast_fp16)[name = string("q_85_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(202489920))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203014272))))[name = string("encoder_module_layers_14_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203079872)))];
+            tensor<fp16, [1, 188, 1024]> linear_130_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_k_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = string("linear_130_cast_fp16")];
+            tensor<int32, [4]> var_2966 = const()[name = string("op_2966"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_57_cast_fp16 = reshape(shape = var_2966, x = linear_130_cast_fp16)[name = string("k_57_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203081984))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203606336))))[name = string("encoder_module_layers_14_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203671936)))];
+            tensor<fp16, [1, 188, 1024]> linear_131_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_v_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = string("linear_131_cast_fp16")];
+            tensor<int32, [4]> var_2971 = const()[name = string("op_2971"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_29_cast_fp16 = reshape(shape = var_2971, x = linear_131_cast_fp16)[name = string("v_29_cast_fp16")];
+            tensor<int32, [4]> value_33_perm_0 = const()[name = string("value_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203674048)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2983_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_module_layers_14_self_attn_pos_bias_u_to_fp16)[name = string("op_2983_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203676160)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2985_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_module_layers_14_self_attn_pos_bias_v_to_fp16)[name = string("op_2985_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_29_perm_0 = const()[name = string("q_with_bias_v_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_335_transpose_x_0 = const()[name = string("x_335_transpose_x_0"), val = bool(false)];
+            bool x_335_transpose_y_0 = const()[name = string("x_335_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2987_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203678272))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203870336))))[name = string("op_2987_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2985_cast_fp16)[name = string("transpose_214")];
+            tensor<fp16, [1, 8, 188, 375]> x_335_cast_fp16 = matmul(transpose_x = x_335_transpose_x_0, transpose_y = x_335_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = op_2987_to_fp16_quantized)[name = string("x_335_cast_fp16")];
+            tensor<int32, [8]> x_337_pad_0 = const()[name = string("x_337_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_337_mode_0 = const()[name = string("x_337_mode_0"), val = string("constant")];
+            fp16 const_228_to_fp16 = const()[name = string("const_228_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_337_cast_fp16 = pad(constant_val = const_228_to_fp16, mode = x_337_mode_0, pad = x_337_pad_0, x = x_335_cast_fp16)[name = string("x_337_cast_fp16")];
+            tensor<int32, [4]> var_2995 = const()[name = string("op_2995"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_339_cast_fp16 = reshape(shape = var_2995, x = x_337_cast_fp16)[name = string("x_339_cast_fp16")];
+            tensor<int32, [4]> var_2999_begin_0 = const()[name = string("op_2999_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2999_end_0 = const()[name = string("op_2999_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2999_end_mask_0 = const()[name = string("op_2999_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2999_cast_fp16 = slice_by_index(begin = var_2999_begin_0, end = var_2999_end_0, end_mask = var_2999_end_mask_0, x = x_339_cast_fp16)[name = string("op_2999_cast_fp16")];
+            tensor<int32, [4]> var_3000 = const()[name = string("op_3000"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_57_cast_fp16 = reshape(shape = var_3000, x = var_2999_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<int32, [4]> transpose_124_perm_0 = const()[name = string("transpose_124_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_125_perm_0 = const()[name = string("transpose_125_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = string("transpose_212")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_124 = transpose(perm = transpose_124_perm_0, x = var_2983_cast_fp16)[name = string("transpose_213")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_59_begin_0 = const()[name = string("matrix_bd_59_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_59_end_0 = const()[name = string("matrix_bd_59_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_59_end_mask_0 = const()[name = string("matrix_bd_59_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3009_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = string("op_3009_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<fp16, [1, 8, 188, 188]> _inversed_scores_57_cast_fp16 = mul(x = var_3009_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = string("_inversed_scores_57_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_59_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_15)[name = string("scores_59_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3015_cast_fp16 = softmax(axis = var_152, x = scores_59_cast_fp16)[name = string("op_3015_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_769_cast_fp16 = select(a = var_164_to_fp16, b = var_3015_cast_fp16, cond = mask_15)[name = string("input_769_cast_fp16")];
+            bool x_341_transpose_x_0 = const()[name = string("x_341_transpose_x_0"), val = bool(false)];
+            bool x_341_transpose_y_0 = const()[name = string("x_341_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_29_cast_fp16)[name = string("transpose_211")];
+            tensor<fp16, [1, 8, 188, 128]> x_341_cast_fp16 = matmul(transpose_x = x_341_transpose_x_0, transpose_y = x_341_transpose_y_0, x = input_769_cast_fp16, y = value_33_cast_fp16)[name = string("x_341_cast_fp16")];
+            tensor<int32, [4]> var_3019_perm_0 = const()[name = string("op_3019_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3020 = const()[name = string("op_3020"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3019_cast_fp16 = transpose(perm = var_3019_perm_0, x = x_341_cast_fp16)[name = string("transpose_210")];
+            tensor<fp16, [1, 188, 1024]> input_771_cast_fp16 = reshape(shape = var_3020, x = var_3019_cast_fp16)[name = string("input_771_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203873408))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204397760))))[name = string("encoder_module_layers_14_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204463360)))];
+            tensor<fp16, [1, 188, 1024]> linear_133_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_out_weight_to_fp16_quantized, x = input_771_cast_fp16)[name = string("linear_133_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_775_cast_fp16 = add(x = input_767_cast_fp16, y = linear_133_cast_fp16)[name = string("input_775_cast_fp16")];
+            tensor<int32, [1]> x_345_axes_0 = const()[name = string("x_345_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204465472)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204467584)))];
+            tensor<fp16, [1, 188, 1024]> x_345_cast_fp16 = layer_norm(axes = x_345_axes_0, beta = encoder_module_layers_14_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_conv_weight_to_fp16, x = input_775_cast_fp16)[name = string("x_345_cast_fp16")];
+            tensor<int32, [3]> input_777_perm_0 = const()[name = string("input_777_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_779_pad_type_0 = const()[name = string("input_779_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_779_strides_0 = const()[name = string("input_779_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_779_pad_0 = const()[name = string("input_779_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_779_dilations_0 = const()[name = string("input_779_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_779_groups_0 = const()[name = string("input_779_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(204469696))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205518336))))[name = string("encoder_module_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205649472)))];
+            tensor<fp16, [1, 1024, 188]> input_777_cast_fp16 = transpose(perm = input_777_perm_0, x = x_345_cast_fp16)[name = string("transpose_209")];
+            tensor<fp16, [1, 2048, 188]> input_779_cast_fp16 = conv(bias = encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_777_cast_fp16)[name = string("input_779_cast_fp16")];
+            int32 x_347_split_num_splits_0 = const()[name = string("x_347_split_num_splits_0"), val = int32(2)];
+            int32 x_347_split_axis_0 = const()[name = string("x_347_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_347_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_347_split_cast_fp16_1 = split(axis = x_347_split_axis_0, num_splits = x_347_split_num_splits_0, x = input_779_cast_fp16)[name = string("x_347_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_347_split_1_sigmoid_cast_fp16 = sigmoid(x = x_347_split_cast_fp16_1)[name = string("x_347_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_347_cast_fp16 = mul(x = x_347_split_cast_fp16_0, y = x_347_split_1_sigmoid_cast_fp16)[name = string("x_347_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_781_cast_fp16 = select(a = var_164_to_fp16, b = x_347_cast_fp16, cond = var_608)[name = string("input_781_cast_fp16")];
+            tensor<int32, [6]> input_783_pad_0 = const()[name = string("input_783_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_783_mode_0 = const()[name = string("input_783_mode_0"), val = string("constant")];
+            fp16 const_231_to_fp16 = const()[name = string("const_231_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_783_cast_fp16 = pad(constant_val = const_231_to_fp16, mode = input_783_mode_0, pad = input_783_pad_0, x = input_781_cast_fp16)[name = string("input_783_cast_fp16")];
+            string input_785_pad_type_0 = const()[name = string("input_785_pad_type_0"), val = string("valid")];
+            int32 input_785_groups_0 = const()[name = string("input_785_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_785_strides_0 = const()[name = string("input_785_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_785_pad_0 = const()[name = string("input_785_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_785_dilations_0 = const()[name = string("input_785_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_350_to_fp16 = const()[name = string("const_350_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205653632)))];
+            tensor<fp16, [1024]> const_351_to_fp16 = const()[name = string("const_351_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205672128)))];
+            tensor<fp16, [1, 1024, 188]> input_787_cast_fp16 = conv(bias = const_351_to_fp16, dilations = input_785_dilations_0, groups = input_785_groups_0, pad = input_785_pad_0, pad_type = input_785_pad_type_0, strides = input_785_strides_0, weight = const_350_to_fp16, x = input_783_cast_fp16)[name = string("input_787_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_789_cast_fp16 = silu(x = input_787_cast_fp16)[name = string("input_789_cast_fp16")];
+            string x_349_pad_type_0 = const()[name = string("x_349_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_349_strides_0 = const()[name = string("x_349_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_349_pad_0 = const()[name = string("x_349_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_349_dilations_0 = const()[name = string("x_349_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_349_groups_0 = const()[name = string("x_349_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205674240))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206198592))))[name = string("encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206264192)))];
+            tensor<fp16, [1, 1024, 188]> x_349_cast_fp16 = conv(bias = encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16, dilations = x_349_dilations_0, groups = x_349_groups_0, pad = x_349_pad_0, pad_type = x_349_pad_type_0, strides = x_349_strides_0, weight = encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_789_cast_fp16)[name = string("x_349_cast_fp16")];
+            tensor<int32, [3]> input_791_perm_0 = const()[name = string("input_791_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_791_cast_fp16 = transpose(perm = input_791_perm_0, x = x_349_cast_fp16)[name = string("transpose_208")];
+            tensor<fp16, [1, 188, 1024]> input_793_cast_fp16 = add(x = input_775_cast_fp16, y = input_791_cast_fp16)[name = string("input_793_cast_fp16")];
+            tensor<int32, [1]> input_795_axes_0 = const()[name = string("input_795_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206266304)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206268416)))];
+            tensor<fp16, [1, 188, 1024]> input_795_cast_fp16 = layer_norm(axes = input_795_axes_0, beta = encoder_module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_793_cast_fp16)[name = string("input_795_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(206270528))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208367744))))[name = string("encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208629952)))];
+            tensor<fp16, [1, 188, 4096]> linear_134_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16_quantized, x = input_795_cast_fp16)[name = string("linear_134_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_799_cast_fp16 = silu(x = linear_134_cast_fp16)[name = string("input_799_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(208638208))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210735424))))[name = string("encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210997632)))];
+            tensor<fp16, [1, 188, 1024]> linear_135_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16_quantized, x = input_799_cast_fp16)[name = string("linear_135_cast_fp16")];
+            fp16 var_3086_to_fp16 = const()[name = string("op_3086_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3087_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_3086_to_fp16)[name = string("op_3087_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_805_cast_fp16 = add(x = input_793_cast_fp16, y = var_3087_cast_fp16)[name = string("input_805_cast_fp16")];
+            tensor<int32, [1]> input_807_axes_0 = const()[name = string("input_807_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210999744)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211001856)))];
+            tensor<fp16, [1, 188, 1024]> input_807_cast_fp16 = layer_norm(axes = input_807_axes_0, beta = encoder_module_layers_14_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_out_weight_to_fp16, x = input_805_cast_fp16)[name = string("input_807_cast_fp16")];
+            tensor<int32, [1]> input_809_axes_0 = const()[name = string("input_809_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211003968)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211006080)))];
+            tensor<fp16, [1, 188, 1024]> input_809_cast_fp16 = layer_norm(axes = input_809_axes_0, beta = encoder_module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_807_cast_fp16)[name = string("input_809_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211008192))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213105408))))[name = string("encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213367616)))];
+            tensor<fp16, [1, 188, 4096]> linear_136_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16_quantized, x = input_809_cast_fp16)[name = string("linear_136_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_813_cast_fp16 = silu(x = linear_136_cast_fp16)[name = string("input_813_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213375872))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215473088))))[name = string("encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215735296)))];
+            tensor<fp16, [1, 188, 1024]> linear_137_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16_quantized, x = input_813_cast_fp16)[name = string("linear_137_cast_fp16")];
+            fp16 var_3117_to_fp16 = const()[name = string("op_3117_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3118_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_3117_to_fp16)[name = string("op_3118_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_819_cast_fp16 = add(x = input_807_cast_fp16, y = var_3118_cast_fp16)[name = string("input_819_cast_fp16")];
+            tensor<int32, [1]> query_31_axes_0 = const()[name = string("query_31_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215737408)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215739520)))];
+            tensor<fp16, [1, 188, 1024]> query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = encoder_module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_self_att_weight_to_fp16, x = input_819_cast_fp16)[name = string("query_31_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(215741632))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216265984))))[name = string("encoder_module_layers_15_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216331584)))];
+            tensor<fp16, [1, 188, 1024]> linear_138_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_q_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = string("linear_138_cast_fp16")];
+            tensor<int32, [4]> var_3135 = const()[name = string("op_3135"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_91_cast_fp16 = reshape(shape = var_3135, x = linear_138_cast_fp16)[name = string("q_91_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216333696))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216858048))))[name = string("encoder_module_layers_15_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216923648)))];
+            tensor<fp16, [1, 188, 1024]> linear_139_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_k_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = string("linear_139_cast_fp16")];
+            tensor<int32, [4]> var_3140 = const()[name = string("op_3140"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_61_cast_fp16 = reshape(shape = var_3140, x = linear_139_cast_fp16)[name = string("k_61_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216925760))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217450112))))[name = string("encoder_module_layers_15_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217515712)))];
+            tensor<fp16, [1, 188, 1024]> linear_140_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_v_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = string("linear_140_cast_fp16")];
+            tensor<int32, [4]> var_3145 = const()[name = string("op_3145"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_31_cast_fp16 = reshape(shape = var_3145, x = linear_140_cast_fp16)[name = string("v_31_cast_fp16")];
+            tensor<int32, [4]> value_35_perm_0 = const()[name = string("value_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217517824)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3157_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_module_layers_15_self_attn_pos_bias_u_to_fp16)[name = string("op_3157_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217519936)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3159_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_module_layers_15_self_attn_pos_bias_v_to_fp16)[name = string("op_3159_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_31_perm_0 = const()[name = string("q_with_bias_v_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_357_transpose_x_0 = const()[name = string("x_357_transpose_x_0"), val = bool(false)];
+            bool x_357_transpose_y_0 = const()[name = string("x_357_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3161_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217522048))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217714112))))[name = string("op_3161_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_3159_cast_fp16)[name = string("transpose_207")];
+            tensor<fp16, [1, 8, 188, 375]> x_357_cast_fp16 = matmul(transpose_x = x_357_transpose_x_0, transpose_y = x_357_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = op_3161_to_fp16_quantized)[name = string("x_357_cast_fp16")];
+            tensor<int32, [8]> x_359_pad_0 = const()[name = string("x_359_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_359_mode_0 = const()[name = string("x_359_mode_0"), val = string("constant")];
+            fp16 const_238_to_fp16 = const()[name = string("const_238_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_359_cast_fp16 = pad(constant_val = const_238_to_fp16, mode = x_359_mode_0, pad = x_359_pad_0, x = x_357_cast_fp16)[name = string("x_359_cast_fp16")];
+            tensor<int32, [4]> var_3169 = const()[name = string("op_3169"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_361_cast_fp16 = reshape(shape = var_3169, x = x_359_cast_fp16)[name = string("x_361_cast_fp16")];
+            tensor<int32, [4]> var_3173_begin_0 = const()[name = string("op_3173_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3173_end_0 = const()[name = string("op_3173_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3173_end_mask_0 = const()[name = string("op_3173_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3173_cast_fp16 = slice_by_index(begin = var_3173_begin_0, end = var_3173_end_0, end_mask = var_3173_end_mask_0, x = x_361_cast_fp16)[name = string("op_3173_cast_fp16")];
+            tensor<int32, [4]> var_3174 = const()[name = string("op_3174"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_61_cast_fp16 = reshape(shape = var_3174, x = var_3173_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<int32, [4]> transpose_126_perm_0 = const()[name = string("transpose_126_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_127_perm_0 = const()[name = string("transpose_127_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = string("transpose_205")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_126 = transpose(perm = transpose_126_perm_0, x = var_3157_cast_fp16)[name = string("transpose_206")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_63_begin_0 = const()[name = string("matrix_bd_63_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_63_end_0 = const()[name = string("matrix_bd_63_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_63_end_mask_0 = const()[name = string("matrix_bd_63_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3183_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = string("op_3183_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<fp16, [1, 8, 188, 188]> _inversed_scores_61_cast_fp16 = mul(x = var_3183_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = string("_inversed_scores_61_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_63_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_15)[name = string("scores_63_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3189_cast_fp16 = softmax(axis = var_152, x = scores_63_cast_fp16)[name = string("op_3189_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_821_cast_fp16 = select(a = var_164_to_fp16, b = var_3189_cast_fp16, cond = mask_15)[name = string("input_821_cast_fp16")];
+            bool x_363_transpose_x_0 = const()[name = string("x_363_transpose_x_0"), val = bool(false)];
+            bool x_363_transpose_y_0 = const()[name = string("x_363_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_31_cast_fp16)[name = string("transpose_204")];
+            tensor<fp16, [1, 8, 188, 128]> x_363_cast_fp16 = matmul(transpose_x = x_363_transpose_x_0, transpose_y = x_363_transpose_y_0, x = input_821_cast_fp16, y = value_35_cast_fp16)[name = string("x_363_cast_fp16")];
+            tensor<int32, [4]> var_3193_perm_0 = const()[name = string("op_3193_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3194 = const()[name = string("op_3194"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3193_cast_fp16 = transpose(perm = var_3193_perm_0, x = x_363_cast_fp16)[name = string("transpose_203")];
+            tensor<fp16, [1, 188, 1024]> input_823_cast_fp16 = reshape(shape = var_3194, x = var_3193_cast_fp16)[name = string("input_823_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217717184))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218241536))))[name = string("encoder_module_layers_15_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218307136)))];
+            tensor<fp16, [1, 188, 1024]> linear_142_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_out_weight_to_fp16_quantized, x = input_823_cast_fp16)[name = string("linear_142_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_827_cast_fp16 = add(x = input_819_cast_fp16, y = linear_142_cast_fp16)[name = string("input_827_cast_fp16")];
+            tensor<int32, [1]> x_367_axes_0 = const()[name = string("x_367_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218309248)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218311360)))];
+            tensor<fp16, [1, 188, 1024]> x_367_cast_fp16 = layer_norm(axes = x_367_axes_0, beta = encoder_module_layers_15_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_conv_weight_to_fp16, x = input_827_cast_fp16)[name = string("x_367_cast_fp16")];
+            tensor<int32, [3]> input_829_perm_0 = const()[name = string("input_829_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_831_pad_type_0 = const()[name = string("input_831_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_831_strides_0 = const()[name = string("input_831_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_831_pad_0 = const()[name = string("input_831_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_831_dilations_0 = const()[name = string("input_831_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_831_groups_0 = const()[name = string("input_831_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218313472))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219362112))))[name = string("encoder_module_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219493248)))];
+            tensor<fp16, [1, 1024, 188]> input_829_cast_fp16 = transpose(perm = input_829_perm_0, x = x_367_cast_fp16)[name = string("transpose_202")];
+            tensor<fp16, [1, 2048, 188]> input_831_cast_fp16 = conv(bias = encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_829_cast_fp16)[name = string("input_831_cast_fp16")];
+            int32 x_369_split_num_splits_0 = const()[name = string("x_369_split_num_splits_0"), val = int32(2)];
+            int32 x_369_split_axis_0 = const()[name = string("x_369_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_369_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_369_split_cast_fp16_1 = split(axis = x_369_split_axis_0, num_splits = x_369_split_num_splits_0, x = input_831_cast_fp16)[name = string("x_369_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_369_split_1_sigmoid_cast_fp16 = sigmoid(x = x_369_split_cast_fp16_1)[name = string("x_369_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_369_cast_fp16 = mul(x = x_369_split_cast_fp16_0, y = x_369_split_1_sigmoid_cast_fp16)[name = string("x_369_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_833_cast_fp16 = select(a = var_164_to_fp16, b = x_369_cast_fp16, cond = var_608)[name = string("input_833_cast_fp16")];
+            tensor<int32, [6]> input_835_pad_0 = const()[name = string("input_835_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_835_mode_0 = const()[name = string("input_835_mode_0"), val = string("constant")];
+            fp16 const_241_to_fp16 = const()[name = string("const_241_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_835_cast_fp16 = pad(constant_val = const_241_to_fp16, mode = input_835_mode_0, pad = input_835_pad_0, x = input_833_cast_fp16)[name = string("input_835_cast_fp16")];
+            string input_837_pad_type_0 = const()[name = string("input_837_pad_type_0"), val = string("valid")];
+            int32 input_837_groups_0 = const()[name = string("input_837_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_837_strides_0 = const()[name = string("input_837_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_837_pad_0 = const()[name = string("input_837_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_837_dilations_0 = const()[name = string("input_837_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_352_to_fp16 = const()[name = string("const_352_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219497408)))];
+            tensor<fp16, [1024]> const_353_to_fp16 = const()[name = string("const_353_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219515904)))];
+            tensor<fp16, [1, 1024, 188]> input_839_cast_fp16 = conv(bias = const_353_to_fp16, dilations = input_837_dilations_0, groups = input_837_groups_0, pad = input_837_pad_0, pad_type = input_837_pad_type_0, strides = input_837_strides_0, weight = const_352_to_fp16, x = input_835_cast_fp16)[name = string("input_839_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_841_cast_fp16 = silu(x = input_839_cast_fp16)[name = string("input_841_cast_fp16")];
+            string x_371_pad_type_0 = const()[name = string("x_371_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_371_strides_0 = const()[name = string("x_371_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_371_pad_0 = const()[name = string("x_371_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_371_dilations_0 = const()[name = string("x_371_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_371_groups_0 = const()[name = string("x_371_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219518016))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220042368))))[name = string("encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220107968)))];
+            tensor<fp16, [1, 1024, 188]> x_371_cast_fp16 = conv(bias = encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16, dilations = x_371_dilations_0, groups = x_371_groups_0, pad = x_371_pad_0, pad_type = x_371_pad_type_0, strides = x_371_strides_0, weight = encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_841_cast_fp16)[name = string("x_371_cast_fp16")];
+            tensor<int32, [3]> input_843_perm_0 = const()[name = string("input_843_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_843_cast_fp16 = transpose(perm = input_843_perm_0, x = x_371_cast_fp16)[name = string("transpose_201")];
+            tensor<fp16, [1, 188, 1024]> input_845_cast_fp16 = add(x = input_827_cast_fp16, y = input_843_cast_fp16)[name = string("input_845_cast_fp16")];
+            tensor<int32, [1]> input_847_axes_0 = const()[name = string("input_847_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220110080)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220112192)))];
+            tensor<fp16, [1, 188, 1024]> input_847_cast_fp16 = layer_norm(axes = input_847_axes_0, beta = encoder_module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_845_cast_fp16)[name = string("input_847_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220114304))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222211520))))[name = string("encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222473728)))];
+            tensor<fp16, [1, 188, 4096]> linear_143_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16_quantized, x = input_847_cast_fp16)[name = string("linear_143_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_851_cast_fp16 = silu(x = linear_143_cast_fp16)[name = string("input_851_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(222481984))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224579200))))[name = string("encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224841408)))];
+            tensor<fp16, [1, 188, 1024]> linear_144_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16_quantized, x = input_851_cast_fp16)[name = string("linear_144_cast_fp16")];
+            fp16 var_3260_to_fp16 = const()[name = string("op_3260_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3261_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_3260_to_fp16)[name = string("op_3261_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_857_cast_fp16 = add(x = input_845_cast_fp16, y = var_3261_cast_fp16)[name = string("input_857_cast_fp16")];
+            tensor<int32, [1]> input_859_axes_0 = const()[name = string("input_859_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224843520)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224845632)))];
+            tensor<fp16, [1, 188, 1024]> input_859_cast_fp16 = layer_norm(axes = input_859_axes_0, beta = encoder_module_layers_15_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_out_weight_to_fp16, x = input_857_cast_fp16)[name = string("input_859_cast_fp16")];
+            tensor<int32, [1]> input_861_axes_0 = const()[name = string("input_861_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224847744)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224849856)))];
+            tensor<fp16, [1, 188, 1024]> input_861_cast_fp16 = layer_norm(axes = input_861_axes_0, beta = encoder_module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_859_cast_fp16)[name = string("input_861_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224851968))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226949184))))[name = string("encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227211392)))];
+            tensor<fp16, [1, 188, 4096]> linear_145_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16_quantized, x = input_861_cast_fp16)[name = string("linear_145_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_865_cast_fp16 = silu(x = linear_145_cast_fp16)[name = string("input_865_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(227219648))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229316864))))[name = string("encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229579072)))];
+            tensor<fp16, [1, 188, 1024]> linear_146_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16_quantized, x = input_865_cast_fp16)[name = string("linear_146_cast_fp16")];
+            fp16 var_3291_to_fp16 = const()[name = string("op_3291_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3292_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3291_to_fp16)[name = string("op_3292_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_871_cast_fp16 = add(x = input_859_cast_fp16, y = var_3292_cast_fp16)[name = string("input_871_cast_fp16")];
+            tensor<int32, [1]> query_33_axes_0 = const()[name = string("query_33_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229581184)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229583296)))];
+            tensor<fp16, [1, 188, 1024]> query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = encoder_module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_self_att_weight_to_fp16, x = input_871_cast_fp16)[name = string("query_33_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(229585408))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230109760))))[name = string("encoder_module_layers_16_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230175360)))];
+            tensor<fp16, [1, 188, 1024]> linear_147_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_q_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = string("linear_147_cast_fp16")];
+            tensor<int32, [4]> var_3309 = const()[name = string("op_3309"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_97_cast_fp16 = reshape(shape = var_3309, x = linear_147_cast_fp16)[name = string("q_97_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230177472))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230701824))))[name = string("encoder_module_layers_16_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230767424)))];
+            tensor<fp16, [1, 188, 1024]> linear_148_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_k_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = string("linear_148_cast_fp16")];
+            tensor<int32, [4]> var_3314 = const()[name = string("op_3314"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_65_cast_fp16 = reshape(shape = var_3314, x = linear_148_cast_fp16)[name = string("k_65_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230769536))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231293888))))[name = string("encoder_module_layers_16_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231359488)))];
+            tensor<fp16, [1, 188, 1024]> linear_149_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_v_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = string("linear_149_cast_fp16")];
+            tensor<int32, [4]> var_3319 = const()[name = string("op_3319"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_33_cast_fp16 = reshape(shape = var_3319, x = linear_149_cast_fp16)[name = string("v_33_cast_fp16")];
+            tensor<int32, [4]> value_37_perm_0 = const()[name = string("value_37_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231361600)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3331_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_module_layers_16_self_attn_pos_bias_u_to_fp16)[name = string("op_3331_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231363712)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3333_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_module_layers_16_self_attn_pos_bias_v_to_fp16)[name = string("op_3333_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_33_perm_0 = const()[name = string("q_with_bias_v_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_379_transpose_x_0 = const()[name = string("x_379_transpose_x_0"), val = bool(false)];
+            bool x_379_transpose_y_0 = const()[name = string("x_379_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3335_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231365824))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231557888))))[name = string("op_3335_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_3333_cast_fp16)[name = string("transpose_200")];
+            tensor<fp16, [1, 8, 188, 375]> x_379_cast_fp16 = matmul(transpose_x = x_379_transpose_x_0, transpose_y = x_379_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = op_3335_to_fp16_quantized)[name = string("x_379_cast_fp16")];
+            tensor<int32, [8]> x_381_pad_0 = const()[name = string("x_381_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_381_mode_0 = const()[name = string("x_381_mode_0"), val = string("constant")];
+            fp16 const_248_to_fp16 = const()[name = string("const_248_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_381_cast_fp16 = pad(constant_val = const_248_to_fp16, mode = x_381_mode_0, pad = x_381_pad_0, x = x_379_cast_fp16)[name = string("x_381_cast_fp16")];
+            tensor<int32, [4]> var_3343 = const()[name = string("op_3343"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_383_cast_fp16 = reshape(shape = var_3343, x = x_381_cast_fp16)[name = string("x_383_cast_fp16")];
+            tensor<int32, [4]> var_3347_begin_0 = const()[name = string("op_3347_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3347_end_0 = const()[name = string("op_3347_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3347_end_mask_0 = const()[name = string("op_3347_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3347_cast_fp16 = slice_by_index(begin = var_3347_begin_0, end = var_3347_end_0, end_mask = var_3347_end_mask_0, x = x_383_cast_fp16)[name = string("op_3347_cast_fp16")];
+            tensor<int32, [4]> var_3348 = const()[name = string("op_3348"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_65_cast_fp16 = reshape(shape = var_3348, x = var_3347_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<int32, [4]> transpose_128_perm_0 = const()[name = string("transpose_128_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_129_perm_0 = const()[name = string("transpose_129_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = string("transpose_198")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_128 = transpose(perm = transpose_128_perm_0, x = var_3331_cast_fp16)[name = string("transpose_199")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_67_begin_0 = const()[name = string("matrix_bd_67_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_67_end_0 = const()[name = string("matrix_bd_67_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_67_end_mask_0 = const()[name = string("matrix_bd_67_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3357_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = string("op_3357_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<fp16, [1, 8, 188, 188]> _inversed_scores_65_cast_fp16 = mul(x = var_3357_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = string("_inversed_scores_65_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_67_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_15)[name = string("scores_67_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3363_cast_fp16 = softmax(axis = var_152, x = scores_67_cast_fp16)[name = string("op_3363_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_873_cast_fp16 = select(a = var_164_to_fp16, b = var_3363_cast_fp16, cond = mask_15)[name = string("input_873_cast_fp16")];
+            bool x_385_transpose_x_0 = const()[name = string("x_385_transpose_x_0"), val = bool(false)];
+            bool x_385_transpose_y_0 = const()[name = string("x_385_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_33_cast_fp16)[name = string("transpose_197")];
+            tensor<fp16, [1, 8, 188, 128]> x_385_cast_fp16 = matmul(transpose_x = x_385_transpose_x_0, transpose_y = x_385_transpose_y_0, x = input_873_cast_fp16, y = value_37_cast_fp16)[name = string("x_385_cast_fp16")];
+            tensor<int32, [4]> var_3367_perm_0 = const()[name = string("op_3367_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3368 = const()[name = string("op_3368"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3367_cast_fp16 = transpose(perm = var_3367_perm_0, x = x_385_cast_fp16)[name = string("transpose_196")];
+            tensor<fp16, [1, 188, 1024]> input_875_cast_fp16 = reshape(shape = var_3368, x = var_3367_cast_fp16)[name = string("input_875_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231560960))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232085312))))[name = string("encoder_module_layers_16_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232150912)))];
+            tensor<fp16, [1, 188, 1024]> linear_151_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_out_weight_to_fp16_quantized, x = input_875_cast_fp16)[name = string("linear_151_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_879_cast_fp16 = add(x = input_871_cast_fp16, y = linear_151_cast_fp16)[name = string("input_879_cast_fp16")];
+            tensor<int32, [1]> x_389_axes_0 = const()[name = string("x_389_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232153024)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232155136)))];
+            tensor<fp16, [1, 188, 1024]> x_389_cast_fp16 = layer_norm(axes = x_389_axes_0, beta = encoder_module_layers_16_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_conv_weight_to_fp16, x = input_879_cast_fp16)[name = string("x_389_cast_fp16")];
+            tensor<int32, [3]> input_881_perm_0 = const()[name = string("input_881_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_883_pad_type_0 = const()[name = string("input_883_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_883_strides_0 = const()[name = string("input_883_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_883_pad_0 = const()[name = string("input_883_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_883_dilations_0 = const()[name = string("input_883_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_883_groups_0 = const()[name = string("input_883_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(232157248))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233205888))))[name = string("encoder_module_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233337024)))];
+            tensor<fp16, [1, 1024, 188]> input_881_cast_fp16 = transpose(perm = input_881_perm_0, x = x_389_cast_fp16)[name = string("transpose_195")];
+            tensor<fp16, [1, 2048, 188]> input_883_cast_fp16 = conv(bias = encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_881_cast_fp16)[name = string("input_883_cast_fp16")];
+            int32 x_391_split_num_splits_0 = const()[name = string("x_391_split_num_splits_0"), val = int32(2)];
+            int32 x_391_split_axis_0 = const()[name = string("x_391_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_391_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_391_split_cast_fp16_1 = split(axis = x_391_split_axis_0, num_splits = x_391_split_num_splits_0, x = input_883_cast_fp16)[name = string("x_391_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_391_split_1_sigmoid_cast_fp16 = sigmoid(x = x_391_split_cast_fp16_1)[name = string("x_391_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_391_cast_fp16 = mul(x = x_391_split_cast_fp16_0, y = x_391_split_1_sigmoid_cast_fp16)[name = string("x_391_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_885_cast_fp16 = select(a = var_164_to_fp16, b = x_391_cast_fp16, cond = var_608)[name = string("input_885_cast_fp16")];
+            tensor<int32, [6]> input_887_pad_0 = const()[name = string("input_887_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_887_mode_0 = const()[name = string("input_887_mode_0"), val = string("constant")];
+            fp16 const_251_to_fp16 = const()[name = string("const_251_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_887_cast_fp16 = pad(constant_val = const_251_to_fp16, mode = input_887_mode_0, pad = input_887_pad_0, x = input_885_cast_fp16)[name = string("input_887_cast_fp16")];
+            string input_889_pad_type_0 = const()[name = string("input_889_pad_type_0"), val = string("valid")];
+            int32 input_889_groups_0 = const()[name = string("input_889_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_889_strides_0 = const()[name = string("input_889_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_889_pad_0 = const()[name = string("input_889_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_889_dilations_0 = const()[name = string("input_889_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_354_to_fp16 = const()[name = string("const_354_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233341184)))];
+            tensor<fp16, [1024]> const_355_to_fp16 = const()[name = string("const_355_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233359680)))];
+            tensor<fp16, [1, 1024, 188]> input_891_cast_fp16 = conv(bias = const_355_to_fp16, dilations = input_889_dilations_0, groups = input_889_groups_0, pad = input_889_pad_0, pad_type = input_889_pad_type_0, strides = input_889_strides_0, weight = const_354_to_fp16, x = input_887_cast_fp16)[name = string("input_891_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_893_cast_fp16 = silu(x = input_891_cast_fp16)[name = string("input_893_cast_fp16")];
+            string x_393_pad_type_0 = const()[name = string("x_393_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_393_strides_0 = const()[name = string("x_393_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_393_pad_0 = const()[name = string("x_393_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_393_dilations_0 = const()[name = string("x_393_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_393_groups_0 = const()[name = string("x_393_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233361792))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233886144))))[name = string("encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233951744)))];
+            tensor<fp16, [1, 1024, 188]> x_393_cast_fp16 = conv(bias = encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16, dilations = x_393_dilations_0, groups = x_393_groups_0, pad = x_393_pad_0, pad_type = x_393_pad_type_0, strides = x_393_strides_0, weight = encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_893_cast_fp16)[name = string("x_393_cast_fp16")];
+            tensor<int32, [3]> input_895_perm_0 = const()[name = string("input_895_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_895_cast_fp16 = transpose(perm = input_895_perm_0, x = x_393_cast_fp16)[name = string("transpose_194")];
+            tensor<fp16, [1, 188, 1024]> input_897_cast_fp16 = add(x = input_879_cast_fp16, y = input_895_cast_fp16)[name = string("input_897_cast_fp16")];
+            tensor<int32, [1]> input_899_axes_0 = const()[name = string("input_899_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233953856)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233955968)))];
+            tensor<fp16, [1, 188, 1024]> input_899_cast_fp16 = layer_norm(axes = input_899_axes_0, beta = encoder_module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_897_cast_fp16)[name = string("input_899_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(233958080))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236055296))))[name = string("encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236317504)))];
+            tensor<fp16, [1, 188, 4096]> linear_152_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16_quantized, x = input_899_cast_fp16)[name = string("linear_152_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_903_cast_fp16 = silu(x = linear_152_cast_fp16)[name = string("input_903_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236325760))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238422976))))[name = string("encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238685184)))];
+            tensor<fp16, [1, 188, 1024]> linear_153_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16_quantized, x = input_903_cast_fp16)[name = string("linear_153_cast_fp16")];
+            fp16 var_3434_to_fp16 = const()[name = string("op_3434_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3435_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_3434_to_fp16)[name = string("op_3435_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_909_cast_fp16 = add(x = input_897_cast_fp16, y = var_3435_cast_fp16)[name = string("input_909_cast_fp16")];
+            tensor<int32, [1]> input_911_axes_0 = const()[name = string("input_911_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238687296)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238689408)))];
+            tensor<fp16, [1, 188, 1024]> input_911_cast_fp16 = layer_norm(axes = input_911_axes_0, beta = encoder_module_layers_16_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_out_weight_to_fp16, x = input_909_cast_fp16)[name = string("input_911_cast_fp16")];
+            tensor<int32, [1]> input_913_axes_0 = const()[name = string("input_913_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238691520)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238693632)))];
+            tensor<fp16, [1, 188, 1024]> input_913_cast_fp16 = layer_norm(axes = input_913_axes_0, beta = encoder_module_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_feed_forward1_weight_to_fp16, x = input_911_cast_fp16)[name = string("input_913_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238695744))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240792960))))[name = string("encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241055168)))];
+            tensor<fp16, [1, 188, 4096]> linear_154_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16_quantized, x = input_913_cast_fp16)[name = string("linear_154_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_917_cast_fp16 = silu(x = linear_154_cast_fp16)[name = string("input_917_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241063424))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243160640))))[name = string("encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243422848)))];
+            tensor<fp16, [1, 188, 1024]> linear_155_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16_quantized, x = input_917_cast_fp16)[name = string("linear_155_cast_fp16")];
+            fp16 var_3465_to_fp16 = const()[name = string("op_3465_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3466_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_3465_to_fp16)[name = string("op_3466_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_923_cast_fp16 = add(x = input_911_cast_fp16, y = var_3466_cast_fp16)[name = string("input_923_cast_fp16")];
+            tensor<int32, [1]> query_35_axes_0 = const()[name = string("query_35_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243424960)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243427072)))];
+            tensor<fp16, [1, 188, 1024]> query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = encoder_module_layers_17_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_self_att_weight_to_fp16, x = input_923_cast_fp16)[name = string("query_35_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243429184))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(243953536))))[name = string("encoder_module_layers_17_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244019136)))];
+            tensor<fp16, [1, 188, 1024]> linear_156_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_q_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = string("linear_156_cast_fp16")];
+            tensor<int32, [4]> var_3483 = const()[name = string("op_3483"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_103_cast_fp16 = reshape(shape = var_3483, x = linear_156_cast_fp16)[name = string("q_103_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244021248))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244545600))))[name = string("encoder_module_layers_17_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244611200)))];
+            tensor<fp16, [1, 188, 1024]> linear_157_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_k_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = string("linear_157_cast_fp16")];
+            tensor<int32, [4]> var_3488 = const()[name = string("op_3488"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_69_cast_fp16 = reshape(shape = var_3488, x = linear_157_cast_fp16)[name = string("k_69_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(244613312))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245137664))))[name = string("encoder_module_layers_17_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245203264)))];
+            tensor<fp16, [1, 188, 1024]> linear_158_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_v_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = string("linear_158_cast_fp16")];
+            tensor<int32, [4]> var_3493 = const()[name = string("op_3493"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_35_cast_fp16 = reshape(shape = var_3493, x = linear_158_cast_fp16)[name = string("v_35_cast_fp16")];
+            tensor<int32, [4]> value_39_perm_0 = const()[name = string("value_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245205376)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3505_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_module_layers_17_self_attn_pos_bias_u_to_fp16)[name = string("op_3505_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245207488)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3507_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_module_layers_17_self_attn_pos_bias_v_to_fp16)[name = string("op_3507_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_35_perm_0 = const()[name = string("q_with_bias_v_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_401_transpose_x_0 = const()[name = string("x_401_transpose_x_0"), val = bool(false)];
+            bool x_401_transpose_y_0 = const()[name = string("x_401_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3509_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245209600))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245401664))))[name = string("op_3509_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3507_cast_fp16)[name = string("transpose_193")];
+            tensor<fp16, [1, 8, 188, 375]> x_401_cast_fp16 = matmul(transpose_x = x_401_transpose_x_0, transpose_y = x_401_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = op_3509_to_fp16_quantized)[name = string("x_401_cast_fp16")];
+            tensor<int32, [8]> x_403_pad_0 = const()[name = string("x_403_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_403_mode_0 = const()[name = string("x_403_mode_0"), val = string("constant")];
+            fp16 const_258_to_fp16 = const()[name = string("const_258_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_403_cast_fp16 = pad(constant_val = const_258_to_fp16, mode = x_403_mode_0, pad = x_403_pad_0, x = x_401_cast_fp16)[name = string("x_403_cast_fp16")];
+            tensor<int32, [4]> var_3517 = const()[name = string("op_3517"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_405_cast_fp16 = reshape(shape = var_3517, x = x_403_cast_fp16)[name = string("x_405_cast_fp16")];
+            tensor<int32, [4]> var_3521_begin_0 = const()[name = string("op_3521_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3521_end_0 = const()[name = string("op_3521_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3521_end_mask_0 = const()[name = string("op_3521_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3521_cast_fp16 = slice_by_index(begin = var_3521_begin_0, end = var_3521_end_0, end_mask = var_3521_end_mask_0, x = x_405_cast_fp16)[name = string("op_3521_cast_fp16")];
+            tensor<int32, [4]> var_3522 = const()[name = string("op_3522"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_69_cast_fp16 = reshape(shape = var_3522, x = var_3521_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<int32, [4]> transpose_130_perm_0 = const()[name = string("transpose_130_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_131_perm_0 = const()[name = string("transpose_131_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = string("transpose_191")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_130 = transpose(perm = transpose_130_perm_0, x = var_3505_cast_fp16)[name = string("transpose_192")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_71_begin_0 = const()[name = string("matrix_bd_71_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_71_end_0 = const()[name = string("matrix_bd_71_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_71_end_mask_0 = const()[name = string("matrix_bd_71_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3531_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = string("op_3531_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<fp16, [1, 8, 188, 188]> _inversed_scores_69_cast_fp16 = mul(x = var_3531_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = string("_inversed_scores_69_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_71_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_15)[name = string("scores_71_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3537_cast_fp16 = softmax(axis = var_152, x = scores_71_cast_fp16)[name = string("op_3537_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_925_cast_fp16 = select(a = var_164_to_fp16, b = var_3537_cast_fp16, cond = mask_15)[name = string("input_925_cast_fp16")];
+            bool x_407_transpose_x_0 = const()[name = string("x_407_transpose_x_0"), val = bool(false)];
+            bool x_407_transpose_y_0 = const()[name = string("x_407_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_35_cast_fp16)[name = string("transpose_190")];
+            tensor<fp16, [1, 8, 188, 128]> x_407_cast_fp16 = matmul(transpose_x = x_407_transpose_x_0, transpose_y = x_407_transpose_y_0, x = input_925_cast_fp16, y = value_39_cast_fp16)[name = string("x_407_cast_fp16")];
+            tensor<int32, [4]> var_3541_perm_0 = const()[name = string("op_3541_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3542 = const()[name = string("op_3542"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3541_cast_fp16 = transpose(perm = var_3541_perm_0, x = x_407_cast_fp16)[name = string("transpose_189")];
+            tensor<fp16, [1, 188, 1024]> input_927_cast_fp16 = reshape(shape = var_3542, x = var_3541_cast_fp16)[name = string("input_927_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245404736))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245929088))))[name = string("encoder_module_layers_17_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245994688)))];
+            tensor<fp16, [1, 188, 1024]> linear_160_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_out_weight_to_fp16_quantized, x = input_927_cast_fp16)[name = string("linear_160_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_931_cast_fp16 = add(x = input_923_cast_fp16, y = linear_160_cast_fp16)[name = string("input_931_cast_fp16")];
+            tensor<int32, [1]> x_411_axes_0 = const()[name = string("x_411_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245996800)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(245998912)))];
+            tensor<fp16, [1, 188, 1024]> x_411_cast_fp16 = layer_norm(axes = x_411_axes_0, beta = encoder_module_layers_17_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_conv_weight_to_fp16, x = input_931_cast_fp16)[name = string("x_411_cast_fp16")];
+            tensor<int32, [3]> input_933_perm_0 = const()[name = string("input_933_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_935_pad_type_0 = const()[name = string("input_935_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_935_strides_0 = const()[name = string("input_935_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_935_pad_0 = const()[name = string("input_935_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_935_dilations_0 = const()[name = string("input_935_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_935_groups_0 = const()[name = string("input_935_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246001024))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247049664))))[name = string("encoder_module_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247180800)))];
+            tensor<fp16, [1, 1024, 188]> input_933_cast_fp16 = transpose(perm = input_933_perm_0, x = x_411_cast_fp16)[name = string("transpose_188")];
+            tensor<fp16, [1, 2048, 188]> input_935_cast_fp16 = conv(bias = encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_933_cast_fp16)[name = string("input_935_cast_fp16")];
+            int32 x_413_split_num_splits_0 = const()[name = string("x_413_split_num_splits_0"), val = int32(2)];
+            int32 x_413_split_axis_0 = const()[name = string("x_413_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_413_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_413_split_cast_fp16_1 = split(axis = x_413_split_axis_0, num_splits = x_413_split_num_splits_0, x = input_935_cast_fp16)[name = string("x_413_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_413_split_1_sigmoid_cast_fp16 = sigmoid(x = x_413_split_cast_fp16_1)[name = string("x_413_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_413_cast_fp16 = mul(x = x_413_split_cast_fp16_0, y = x_413_split_1_sigmoid_cast_fp16)[name = string("x_413_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_937_cast_fp16 = select(a = var_164_to_fp16, b = x_413_cast_fp16, cond = var_608)[name = string("input_937_cast_fp16")];
+            tensor<int32, [6]> input_939_pad_0 = const()[name = string("input_939_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_939_mode_0 = const()[name = string("input_939_mode_0"), val = string("constant")];
+            fp16 const_261_to_fp16 = const()[name = string("const_261_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_939_cast_fp16 = pad(constant_val = const_261_to_fp16, mode = input_939_mode_0, pad = input_939_pad_0, x = input_937_cast_fp16)[name = string("input_939_cast_fp16")];
+            string input_941_pad_type_0 = const()[name = string("input_941_pad_type_0"), val = string("valid")];
+            int32 input_941_groups_0 = const()[name = string("input_941_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_941_strides_0 = const()[name = string("input_941_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_941_pad_0 = const()[name = string("input_941_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_941_dilations_0 = const()[name = string("input_941_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_356_to_fp16 = const()[name = string("const_356_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247184960)))];
+            tensor<fp16, [1024]> const_357_to_fp16 = const()[name = string("const_357_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247203456)))];
+            tensor<fp16, [1, 1024, 188]> input_943_cast_fp16 = conv(bias = const_357_to_fp16, dilations = input_941_dilations_0, groups = input_941_groups_0, pad = input_941_pad_0, pad_type = input_941_pad_type_0, strides = input_941_strides_0, weight = const_356_to_fp16, x = input_939_cast_fp16)[name = string("input_943_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_945_cast_fp16 = silu(x = input_943_cast_fp16)[name = string("input_945_cast_fp16")];
+            string x_415_pad_type_0 = const()[name = string("x_415_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_415_strides_0 = const()[name = string("x_415_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_415_pad_0 = const()[name = string("x_415_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_415_dilations_0 = const()[name = string("x_415_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_415_groups_0 = const()[name = string("x_415_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247205568))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247729920))))[name = string("encoder_module_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247795520)))];
+            tensor<fp16, [1, 1024, 188]> x_415_cast_fp16 = conv(bias = encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_945_cast_fp16)[name = string("x_415_cast_fp16")];
+            tensor<int32, [3]> input_947_perm_0 = const()[name = string("input_947_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_947_cast_fp16 = transpose(perm = input_947_perm_0, x = x_415_cast_fp16)[name = string("transpose_187")];
+            tensor<fp16, [1, 188, 1024]> input_949_cast_fp16 = add(x = input_931_cast_fp16, y = input_947_cast_fp16)[name = string("input_949_cast_fp16")];
+            tensor<int32, [1]> input_951_axes_0 = const()[name = string("input_951_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247797632)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247799744)))];
+            tensor<fp16, [1, 188, 1024]> input_951_cast_fp16 = layer_norm(axes = input_951_axes_0, beta = encoder_module_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_feed_forward2_weight_to_fp16, x = input_949_cast_fp16)[name = string("input_951_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(247801856))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(249899072))))[name = string("encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250161280)))];
+            tensor<fp16, [1, 188, 4096]> linear_161_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16_quantized, x = input_951_cast_fp16)[name = string("linear_161_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_955_cast_fp16 = silu(x = linear_161_cast_fp16)[name = string("input_955_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250169536))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252266752))))[name = string("encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252528960)))];
+            tensor<fp16, [1, 188, 1024]> linear_162_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16_quantized, x = input_955_cast_fp16)[name = string("linear_162_cast_fp16")];
+            fp16 var_3608_to_fp16 = const()[name = string("op_3608_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3609_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_3608_to_fp16)[name = string("op_3609_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_961_cast_fp16 = add(x = input_949_cast_fp16, y = var_3609_cast_fp16)[name = string("input_961_cast_fp16")];
+            tensor<int32, [1]> input_963_axes_0 = const()[name = string("input_963_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252531072)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252533184)))];
+            tensor<fp16, [1, 188, 1024]> input_963_cast_fp16 = layer_norm(axes = input_963_axes_0, beta = encoder_module_layers_17_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_out_weight_to_fp16, x = input_961_cast_fp16)[name = string("input_963_cast_fp16")];
+            tensor<int32, [1]> input_965_axes_0 = const()[name = string("input_965_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252535296)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252537408)))];
+            tensor<fp16, [1, 188, 1024]> input_965_cast_fp16 = layer_norm(axes = input_965_axes_0, beta = encoder_module_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_feed_forward1_weight_to_fp16, x = input_963_cast_fp16)[name = string("input_965_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(252539520))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254636736))))[name = string("encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254898944)))];
+            tensor<fp16, [1, 188, 4096]> linear_163_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16_quantized, x = input_965_cast_fp16)[name = string("linear_163_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_969_cast_fp16 = silu(x = linear_163_cast_fp16)[name = string("input_969_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254907200))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257004416))))[name = string("encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257266624)))];
+            tensor<fp16, [1, 188, 1024]> linear_164_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16_quantized, x = input_969_cast_fp16)[name = string("linear_164_cast_fp16")];
+            fp16 var_3639_to_fp16 = const()[name = string("op_3639_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3640_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3639_to_fp16)[name = string("op_3640_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_975_cast_fp16 = add(x = input_963_cast_fp16, y = var_3640_cast_fp16)[name = string("input_975_cast_fp16")];
+            tensor<int32, [1]> query_37_axes_0 = const()[name = string("query_37_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257268736)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257270848)))];
+            tensor<fp16, [1, 188, 1024]> query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = encoder_module_layers_18_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_self_att_weight_to_fp16, x = input_975_cast_fp16)[name = string("query_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257272960))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257797312))))[name = string("encoder_module_layers_18_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257862912)))];
+            tensor<fp16, [1, 188, 1024]> linear_165_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_q_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = string("linear_165_cast_fp16")];
+            tensor<int32, [4]> var_3657 = const()[name = string("op_3657"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_109_cast_fp16 = reshape(shape = var_3657, x = linear_165_cast_fp16)[name = string("q_109_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257865024))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258389376))))[name = string("encoder_module_layers_18_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258454976)))];
+            tensor<fp16, [1, 188, 1024]> linear_166_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_k_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = string("linear_166_cast_fp16")];
+            tensor<int32, [4]> var_3662 = const()[name = string("op_3662"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_73_cast_fp16 = reshape(shape = var_3662, x = linear_166_cast_fp16)[name = string("k_73_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258457088))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258981440))))[name = string("encoder_module_layers_18_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259047040)))];
+            tensor<fp16, [1, 188, 1024]> linear_167_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_v_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = string("linear_167_cast_fp16")];
+            tensor<int32, [4]> var_3667 = const()[name = string("op_3667"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_37_cast_fp16 = reshape(shape = var_3667, x = linear_167_cast_fp16)[name = string("v_37_cast_fp16")];
+            tensor<int32, [4]> value_41_perm_0 = const()[name = string("value_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259049152)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3679_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_module_layers_18_self_attn_pos_bias_u_to_fp16)[name = string("op_3679_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259051264)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3681_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_module_layers_18_self_attn_pos_bias_v_to_fp16)[name = string("op_3681_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_37_perm_0 = const()[name = string("q_with_bias_v_37_perm_0"), val = tensor<int32, [4]>([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<fp16, [1, 8, 128, 375]> op_3683_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259053376))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259245440))))[name = string("op_3683_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3681_cast_fp16)[name = string("transpose_186")];
+            tensor<fp16, [1, 8, 188, 375]> x_423_cast_fp16 = matmul(transpose_x = x_423_transpose_x_0, transpose_y = x_423_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = op_3683_to_fp16_quantized)[name = string("x_423_cast_fp16")];
+            tensor<int32, [8]> x_425_pad_0 = const()[name = string("x_425_pad_0"), val = tensor<int32, [8]>([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_268_to_fp16 = const()[name = string("const_268_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_425_cast_fp16 = pad(constant_val = const_268_to_fp16, mode = x_425_mode_0, pad = x_425_pad_0, x = x_423_cast_fp16)[name = string("x_425_cast_fp16")];
+            tensor<int32, [4]> var_3691 = const()[name = string("op_3691"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_427_cast_fp16 = reshape(shape = var_3691, x = x_425_cast_fp16)[name = string("x_427_cast_fp16")];
+            tensor<int32, [4]> var_3695_begin_0 = const()[name = string("op_3695_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3695_end_0 = const()[name = string("op_3695_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3695_end_mask_0 = const()[name = string("op_3695_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3695_cast_fp16 = slice_by_index(begin = var_3695_begin_0, end = var_3695_end_0, end_mask = var_3695_end_mask_0, x = x_427_cast_fp16)[name = string("op_3695_cast_fp16")];
+            tensor<int32, [4]> var_3696 = const()[name = string("op_3696"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_73_cast_fp16 = reshape(shape = var_3696, x = var_3695_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<int32, [4]> transpose_132_perm_0 = const()[name = string("transpose_132_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_133_perm_0 = const()[name = string("transpose_133_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = string("transpose_184")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_132 = transpose(perm = transpose_132_perm_0, x = var_3679_cast_fp16)[name = string("transpose_185")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_75_begin_0 = const()[name = string("matrix_bd_75_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_75_end_0 = const()[name = string("matrix_bd_75_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_75_end_mask_0 = const()[name = string("matrix_bd_75_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3705_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = string("op_3705_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<fp16, [1, 8, 188, 188]> _inversed_scores_73_cast_fp16 = mul(x = var_3705_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = string("_inversed_scores_73_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_75_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_15)[name = string("scores_75_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3711_cast_fp16 = softmax(axis = var_152, x = scores_75_cast_fp16)[name = string("op_3711_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_977_cast_fp16 = select(a = var_164_to_fp16, b = var_3711_cast_fp16, cond = mask_15)[name = string("input_977_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<fp16, [1, 8, 188, 128]> value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_37_cast_fp16)[name = string("transpose_183")];
+            tensor<fp16, [1, 8, 188, 128]> x_429_cast_fp16 = matmul(transpose_x = x_429_transpose_x_0, transpose_y = x_429_transpose_y_0, x = input_977_cast_fp16, y = value_41_cast_fp16)[name = string("x_429_cast_fp16")];
+            tensor<int32, [4]> var_3715_perm_0 = const()[name = string("op_3715_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3716 = const()[name = string("op_3716"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3715_cast_fp16 = transpose(perm = var_3715_perm_0, x = x_429_cast_fp16)[name = string("transpose_182")];
+            tensor<fp16, [1, 188, 1024]> input_979_cast_fp16 = reshape(shape = var_3716, x = var_3715_cast_fp16)[name = string("input_979_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259248512))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259772864))))[name = string("encoder_module_layers_18_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259838464)))];
+            tensor<fp16, [1, 188, 1024]> linear_169_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_out_weight_to_fp16_quantized, x = input_979_cast_fp16)[name = string("linear_169_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_983_cast_fp16 = add(x = input_975_cast_fp16, y = linear_169_cast_fp16)[name = string("input_983_cast_fp16")];
+            tensor<int32, [1]> x_433_axes_0 = const()[name = string("x_433_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259840576)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259842688)))];
+            tensor<fp16, [1, 188, 1024]> x_433_cast_fp16 = layer_norm(axes = x_433_axes_0, beta = encoder_module_layers_18_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_conv_weight_to_fp16, x = input_983_cast_fp16)[name = string("x_433_cast_fp16")];
+            tensor<int32, [3]> input_985_perm_0 = const()[name = string("input_985_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_987_pad_type_0 = const()[name = string("input_987_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_987_strides_0 = const()[name = string("input_987_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_987_pad_0 = const()[name = string("input_987_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_987_dilations_0 = const()[name = string("input_987_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_987_groups_0 = const()[name = string("input_987_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259844800))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260893440))))[name = string("encoder_module_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261024576)))];
+            tensor<fp16, [1, 1024, 188]> input_985_cast_fp16 = transpose(perm = input_985_perm_0, x = x_433_cast_fp16)[name = string("transpose_181")];
+            tensor<fp16, [1, 2048, 188]> input_987_cast_fp16 = conv(bias = encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_985_cast_fp16)[name = string("input_987_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<fp16, [1, 1024, 188]> x_435_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_435_split_cast_fp16_1 = split(axis = x_435_split_axis_0, num_splits = x_435_split_num_splits_0, x = input_987_cast_fp16)[name = string("x_435_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> input_989_cast_fp16 = select(a = var_164_to_fp16, b = x_435_cast_fp16, cond = var_608)[name = string("input_989_cast_fp16")];
+            tensor<int32, [6]> input_991_pad_0 = const()[name = string("input_991_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_991_mode_0 = const()[name = string("input_991_mode_0"), val = string("constant")];
+            fp16 const_271_to_fp16 = const()[name = string("const_271_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_991_cast_fp16 = pad(constant_val = const_271_to_fp16, mode = input_991_mode_0, pad = input_991_pad_0, x = input_989_cast_fp16)[name = string("input_991_cast_fp16")];
+            string input_993_pad_type_0 = const()[name = string("input_993_pad_type_0"), val = string("valid")];
+            int32 input_993_groups_0 = const()[name = string("input_993_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_993_strides_0 = const()[name = string("input_993_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_993_pad_0 = const()[name = string("input_993_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_993_dilations_0 = const()[name = string("input_993_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_358_to_fp16 = const()[name = string("const_358_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261028736)))];
+            tensor<fp16, [1024]> const_359_to_fp16 = const()[name = string("const_359_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261047232)))];
+            tensor<fp16, [1, 1024, 188]> input_995_cast_fp16 = conv(bias = const_359_to_fp16, dilations = input_993_dilations_0, groups = input_993_groups_0, pad = input_993_pad_0, pad_type = input_993_pad_type_0, strides = input_993_strides_0, weight = const_358_to_fp16, x = input_991_cast_fp16)[name = string("input_995_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_997_cast_fp16 = silu(x = input_995_cast_fp16)[name = string("input_997_cast_fp16")];
+            string x_437_pad_type_0 = const()[name = string("x_437_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_437_strides_0 = const()[name = string("x_437_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_437_pad_0 = const()[name = string("x_437_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_437_dilations_0 = const()[name = string("x_437_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_437_groups_0 = const()[name = string("x_437_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261049344))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261573696))))[name = string("encoder_module_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261639296)))];
+            tensor<fp16, [1, 1024, 188]> x_437_cast_fp16 = conv(bias = encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_997_cast_fp16)[name = string("x_437_cast_fp16")];
+            tensor<int32, [3]> input_999_perm_0 = const()[name = string("input_999_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_999_cast_fp16 = transpose(perm = input_999_perm_0, x = x_437_cast_fp16)[name = string("transpose_180")];
+            tensor<fp16, [1, 188, 1024]> input_1001_cast_fp16 = add(x = input_983_cast_fp16, y = input_999_cast_fp16)[name = string("input_1001_cast_fp16")];
+            tensor<int32, [1]> input_1003_axes_0 = const()[name = string("input_1003_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261641408)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261643520)))];
+            tensor<fp16, [1, 188, 1024]> input_1003_cast_fp16 = layer_norm(axes = input_1003_axes_0, beta = encoder_module_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_feed_forward2_weight_to_fp16, x = input_1001_cast_fp16)[name = string("input_1003_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261645632))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263742848))))[name = string("encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(264005056)))];
+            tensor<fp16, [1, 188, 4096]> linear_170_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1003_cast_fp16)[name = string("linear_170_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1007_cast_fp16 = silu(x = linear_170_cast_fp16)[name = string("input_1007_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(264013312))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266110528))))[name = string("encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266372736)))];
+            tensor<fp16, [1, 188, 1024]> linear_171_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1007_cast_fp16)[name = string("linear_171_cast_fp16")];
+            fp16 var_3782_to_fp16 = const()[name = string("op_3782_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3783_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_3782_to_fp16)[name = string("op_3783_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1013_cast_fp16 = add(x = input_1001_cast_fp16, y = var_3783_cast_fp16)[name = string("input_1013_cast_fp16")];
+            tensor<int32, [1]> input_1015_axes_0 = const()[name = string("input_1015_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266374848)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266376960)))];
+            tensor<fp16, [1, 188, 1024]> input_1015_cast_fp16 = layer_norm(axes = input_1015_axes_0, beta = encoder_module_layers_18_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_out_weight_to_fp16, x = input_1013_cast_fp16)[name = string("input_1015_cast_fp16")];
+            tensor<int32, [1]> input_1017_axes_0 = const()[name = string("input_1017_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266379072)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266381184)))];
+            tensor<fp16, [1, 188, 1024]> input_1017_cast_fp16 = layer_norm(axes = input_1017_axes_0, beta = encoder_module_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1015_cast_fp16)[name = string("input_1017_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266383296))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268480512))))[name = string("encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268742720)))];
+            tensor<fp16, [1, 188, 4096]> linear_172_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1017_cast_fp16)[name = string("linear_172_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1021_cast_fp16 = silu(x = linear_172_cast_fp16)[name = string("input_1021_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(268750976))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270848192))))[name = string("encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271110400)))];
+            tensor<fp16, [1, 188, 1024]> linear_173_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1021_cast_fp16)[name = string("linear_173_cast_fp16")];
+            fp16 var_3813_to_fp16 = const()[name = string("op_3813_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3814_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3813_to_fp16)[name = string("op_3814_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1027_cast_fp16 = add(x = input_1015_cast_fp16, y = var_3814_cast_fp16)[name = string("input_1027_cast_fp16")];
+            tensor<int32, [1]> query_39_axes_0 = const()[name = string("query_39_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271112512)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271114624)))];
+            tensor<fp16, [1, 188, 1024]> query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = encoder_module_layers_19_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_self_att_weight_to_fp16, x = input_1027_cast_fp16)[name = string("query_39_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271116736))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271641088))))[name = string("encoder_module_layers_19_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271706688)))];
+            tensor<fp16, [1, 188, 1024]> linear_174_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_q_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = string("linear_174_cast_fp16")];
+            tensor<int32, [4]> var_3831 = const()[name = string("op_3831"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_115_cast_fp16 = reshape(shape = var_3831, x = linear_174_cast_fp16)[name = string("q_115_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(271708800))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272233152))))[name = string("encoder_module_layers_19_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272298752)))];
+            tensor<fp16, [1, 188, 1024]> linear_175_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_k_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = string("linear_175_cast_fp16")];
+            tensor<int32, [4]> var_3836 = const()[name = string("op_3836"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_77_cast_fp16 = reshape(shape = var_3836, x = linear_175_cast_fp16)[name = string("k_77_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272300864))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272825216))))[name = string("encoder_module_layers_19_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272890816)))];
+            tensor<fp16, [1, 188, 1024]> linear_176_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_v_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = string("linear_176_cast_fp16")];
+            tensor<int32, [4]> var_3841 = const()[name = string("op_3841"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_39_cast_fp16 = reshape(shape = var_3841, x = linear_176_cast_fp16)[name = string("v_39_cast_fp16")];
+            tensor<int32, [4]> value_43_perm_0 = const()[name = string("value_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272892928)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3853_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_module_layers_19_self_attn_pos_bias_u_to_fp16)[name = string("op_3853_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272895040)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3855_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_module_layers_19_self_attn_pos_bias_v_to_fp16)[name = string("op_3855_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_39_perm_0 = const()[name = string("q_with_bias_v_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_445_transpose_x_0 = const()[name = string("x_445_transpose_x_0"), val = bool(false)];
+            bool x_445_transpose_y_0 = const()[name = string("x_445_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3857_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272897152))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273089216))))[name = string("op_3857_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3855_cast_fp16)[name = string("transpose_179")];
+            tensor<fp16, [1, 8, 188, 375]> x_445_cast_fp16 = matmul(transpose_x = x_445_transpose_x_0, transpose_y = x_445_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = op_3857_to_fp16_quantized)[name = string("x_445_cast_fp16")];
+            tensor<int32, [8]> x_447_pad_0 = const()[name = string("x_447_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_447_mode_0 = const()[name = string("x_447_mode_0"), val = string("constant")];
+            fp16 const_278_to_fp16 = const()[name = string("const_278_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_447_cast_fp16 = pad(constant_val = const_278_to_fp16, mode = x_447_mode_0, pad = x_447_pad_0, x = x_445_cast_fp16)[name = string("x_447_cast_fp16")];
+            tensor<int32, [4]> var_3865 = const()[name = string("op_3865"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_449_cast_fp16 = reshape(shape = var_3865, x = x_447_cast_fp16)[name = string("x_449_cast_fp16")];
+            tensor<int32, [4]> var_3869_begin_0 = const()[name = string("op_3869_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3869_end_0 = const()[name = string("op_3869_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3869_end_mask_0 = const()[name = string("op_3869_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3869_cast_fp16 = slice_by_index(begin = var_3869_begin_0, end = var_3869_end_0, end_mask = var_3869_end_mask_0, x = x_449_cast_fp16)[name = string("op_3869_cast_fp16")];
+            tensor<int32, [4]> var_3870 = const()[name = string("op_3870"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_77_cast_fp16 = reshape(shape = var_3870, x = var_3869_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<int32, [4]> transpose_134_perm_0 = const()[name = string("transpose_134_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_135_perm_0 = const()[name = string("transpose_135_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = string("transpose_177")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_134 = transpose(perm = transpose_134_perm_0, x = var_3853_cast_fp16)[name = string("transpose_178")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_79_begin_0 = const()[name = string("matrix_bd_79_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_79_end_0 = const()[name = string("matrix_bd_79_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_79_end_mask_0 = const()[name = string("matrix_bd_79_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3879_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = string("op_3879_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<fp16, [1, 8, 188, 188]> _inversed_scores_77_cast_fp16 = mul(x = var_3879_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = string("_inversed_scores_77_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_79_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_15)[name = string("scores_79_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3885_cast_fp16 = softmax(axis = var_152, x = scores_79_cast_fp16)[name = string("op_3885_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1029_cast_fp16 = select(a = var_164_to_fp16, b = var_3885_cast_fp16, cond = mask_15)[name = string("input_1029_cast_fp16")];
+            bool x_451_transpose_x_0 = const()[name = string("x_451_transpose_x_0"), val = bool(false)];
+            bool x_451_transpose_y_0 = const()[name = string("x_451_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_39_cast_fp16)[name = string("transpose_176")];
+            tensor<fp16, [1, 8, 188, 128]> x_451_cast_fp16 = matmul(transpose_x = x_451_transpose_x_0, transpose_y = x_451_transpose_y_0, x = input_1029_cast_fp16, y = value_43_cast_fp16)[name = string("x_451_cast_fp16")];
+            tensor<int32, [4]> var_3889_perm_0 = const()[name = string("op_3889_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3890 = const()[name = string("op_3890"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3889_cast_fp16 = transpose(perm = var_3889_perm_0, x = x_451_cast_fp16)[name = string("transpose_175")];
+            tensor<fp16, [1, 188, 1024]> input_1031_cast_fp16 = reshape(shape = var_3890, x = var_3889_cast_fp16)[name = string("input_1031_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273092288))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273616640))))[name = string("encoder_module_layers_19_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273682240)))];
+            tensor<fp16, [1, 188, 1024]> linear_178_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_out_weight_to_fp16_quantized, x = input_1031_cast_fp16)[name = string("linear_178_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1035_cast_fp16 = add(x = input_1027_cast_fp16, y = linear_178_cast_fp16)[name = string("input_1035_cast_fp16")];
+            tensor<int32, [1]> x_455_axes_0 = const()[name = string("x_455_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273684352)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273686464)))];
+            tensor<fp16, [1, 188, 1024]> x_455_cast_fp16 = layer_norm(axes = x_455_axes_0, beta = encoder_module_layers_19_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_conv_weight_to_fp16, x = input_1035_cast_fp16)[name = string("x_455_cast_fp16")];
+            tensor<int32, [3]> input_1037_perm_0 = const()[name = string("input_1037_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1039_pad_type_0 = const()[name = string("input_1039_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1039_strides_0 = const()[name = string("input_1039_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1039_pad_0 = const()[name = string("input_1039_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1039_dilations_0 = const()[name = string("input_1039_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1039_groups_0 = const()[name = string("input_1039_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273688576))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274737216))))[name = string("encoder_module_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274868352)))];
+            tensor<fp16, [1, 1024, 188]> input_1037_cast_fp16 = transpose(perm = input_1037_perm_0, x = x_455_cast_fp16)[name = string("transpose_174")];
+            tensor<fp16, [1, 2048, 188]> input_1039_cast_fp16 = conv(bias = encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1037_cast_fp16)[name = string("input_1039_cast_fp16")];
+            int32 x_457_split_num_splits_0 = const()[name = string("x_457_split_num_splits_0"), val = int32(2)];
+            int32 x_457_split_axis_0 = const()[name = string("x_457_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_457_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_457_split_cast_fp16_1 = split(axis = x_457_split_axis_0, num_splits = x_457_split_num_splits_0, x = input_1039_cast_fp16)[name = string("x_457_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_457_split_1_sigmoid_cast_fp16 = sigmoid(x = x_457_split_cast_fp16_1)[name = string("x_457_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_457_cast_fp16 = mul(x = x_457_split_cast_fp16_0, y = x_457_split_1_sigmoid_cast_fp16)[name = string("x_457_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1041_cast_fp16 = select(a = var_164_to_fp16, b = x_457_cast_fp16, cond = var_608)[name = string("input_1041_cast_fp16")];
+            tensor<int32, [6]> input_1043_pad_0 = const()[name = string("input_1043_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1043_mode_0 = const()[name = string("input_1043_mode_0"), val = string("constant")];
+            fp16 const_281_to_fp16 = const()[name = string("const_281_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1043_cast_fp16 = pad(constant_val = const_281_to_fp16, mode = input_1043_mode_0, pad = input_1043_pad_0, x = input_1041_cast_fp16)[name = string("input_1043_cast_fp16")];
+            string input_1045_pad_type_0 = const()[name = string("input_1045_pad_type_0"), val = string("valid")];
+            int32 input_1045_groups_0 = const()[name = string("input_1045_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1045_strides_0 = const()[name = string("input_1045_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1045_pad_0 = const()[name = string("input_1045_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1045_dilations_0 = const()[name = string("input_1045_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_360_to_fp16 = const()[name = string("const_360_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274872512)))];
+            tensor<fp16, [1024]> const_361_to_fp16 = const()[name = string("const_361_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274891008)))];
+            tensor<fp16, [1, 1024, 188]> input_1047_cast_fp16 = conv(bias = const_361_to_fp16, dilations = input_1045_dilations_0, groups = input_1045_groups_0, pad = input_1045_pad_0, pad_type = input_1045_pad_type_0, strides = input_1045_strides_0, weight = const_360_to_fp16, x = input_1043_cast_fp16)[name = string("input_1047_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1049_cast_fp16 = silu(x = input_1047_cast_fp16)[name = string("input_1049_cast_fp16")];
+            string x_459_pad_type_0 = const()[name = string("x_459_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_459_strides_0 = const()[name = string("x_459_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_459_pad_0 = const()[name = string("x_459_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_459_dilations_0 = const()[name = string("x_459_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_459_groups_0 = const()[name = string("x_459_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(274893120))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275417472))))[name = string("encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275483072)))];
+            tensor<fp16, [1, 1024, 188]> x_459_cast_fp16 = conv(bias = encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16, dilations = x_459_dilations_0, groups = x_459_groups_0, pad = x_459_pad_0, pad_type = x_459_pad_type_0, strides = x_459_strides_0, weight = encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1049_cast_fp16)[name = string("x_459_cast_fp16")];
+            tensor<int32, [3]> input_1051_perm_0 = const()[name = string("input_1051_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1051_cast_fp16 = transpose(perm = input_1051_perm_0, x = x_459_cast_fp16)[name = string("transpose_173")];
+            tensor<fp16, [1, 188, 1024]> input_1053_cast_fp16 = add(x = input_1035_cast_fp16, y = input_1051_cast_fp16)[name = string("input_1053_cast_fp16")];
+            tensor<int32, [1]> input_1055_axes_0 = const()[name = string("input_1055_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275485184)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275487296)))];
+            tensor<fp16, [1, 188, 1024]> input_1055_cast_fp16 = layer_norm(axes = input_1055_axes_0, beta = encoder_module_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1053_cast_fp16)[name = string("input_1055_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275489408))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277586624))))[name = string("encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277848832)))];
+            tensor<fp16, [1, 188, 4096]> linear_179_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1055_cast_fp16)[name = string("linear_179_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1059_cast_fp16 = silu(x = linear_179_cast_fp16)[name = string("input_1059_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(277857088))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279954304))))[name = string("encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280216512)))];
+            tensor<fp16, [1, 188, 1024]> linear_180_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1059_cast_fp16)[name = string("linear_180_cast_fp16")];
+            fp16 var_3956_to_fp16 = const()[name = string("op_3956_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3957_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_3956_to_fp16)[name = string("op_3957_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1065_cast_fp16 = add(x = input_1053_cast_fp16, y = var_3957_cast_fp16)[name = string("input_1065_cast_fp16")];
+            tensor<int32, [1]> input_1067_axes_0 = const()[name = string("input_1067_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280218624)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280220736)))];
+            tensor<fp16, [1, 188, 1024]> input_1067_cast_fp16 = layer_norm(axes = input_1067_axes_0, beta = encoder_module_layers_19_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_out_weight_to_fp16, x = input_1065_cast_fp16)[name = string("input_1067_cast_fp16")];
+            tensor<int32, [1]> input_1069_axes_0 = const()[name = string("input_1069_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280222848)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280224960)))];
+            tensor<fp16, [1, 188, 1024]> input_1069_cast_fp16 = layer_norm(axes = input_1069_axes_0, beta = encoder_module_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1067_cast_fp16)[name = string("input_1069_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(280227072))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282324288))))[name = string("encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282586496)))];
+            tensor<fp16, [1, 188, 4096]> linear_181_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1069_cast_fp16)[name = string("linear_181_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1073_cast_fp16 = silu(x = linear_181_cast_fp16)[name = string("input_1073_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(282594752))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284691968))))[name = string("encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284954176)))];
+            tensor<fp16, [1, 188, 1024]> linear_182_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1073_cast_fp16)[name = string("linear_182_cast_fp16")];
+            fp16 var_3987_to_fp16 = const()[name = string("op_3987_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3988_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3987_to_fp16)[name = string("op_3988_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1079_cast_fp16 = add(x = input_1067_cast_fp16, y = var_3988_cast_fp16)[name = string("input_1079_cast_fp16")];
+            tensor<int32, [1]> query_41_axes_0 = const()[name = string("query_41_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284956288)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284958400)))];
+            tensor<fp16, [1, 188, 1024]> query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = encoder_module_layers_20_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_self_att_weight_to_fp16, x = input_1079_cast_fp16)[name = string("query_41_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284960512))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285484864))))[name = string("encoder_module_layers_20_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285550464)))];
+            tensor<fp16, [1, 188, 1024]> linear_183_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_q_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = string("linear_183_cast_fp16")];
+            tensor<int32, [4]> var_4005 = const()[name = string("op_4005"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_121_cast_fp16 = reshape(shape = var_4005, x = linear_183_cast_fp16)[name = string("q_121_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285552576))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286076928))))[name = string("encoder_module_layers_20_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286142528)))];
+            tensor<fp16, [1, 188, 1024]> linear_184_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_k_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = string("linear_184_cast_fp16")];
+            tensor<int32, [4]> var_4010 = const()[name = string("op_4010"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_81_cast_fp16 = reshape(shape = var_4010, x = linear_184_cast_fp16)[name = string("k_81_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286144640))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286668992))))[name = string("encoder_module_layers_20_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286734592)))];
+            tensor<fp16, [1, 188, 1024]> linear_185_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_v_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = string("linear_185_cast_fp16")];
+            tensor<int32, [4]> var_4015 = const()[name = string("op_4015"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_41_cast_fp16 = reshape(shape = var_4015, x = linear_185_cast_fp16)[name = string("v_41_cast_fp16")];
+            tensor<int32, [4]> value_45_perm_0 = const()[name = string("value_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286736704)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4027_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_module_layers_20_self_attn_pos_bias_u_to_fp16)[name = string("op_4027_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286738816)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4029_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_module_layers_20_self_attn_pos_bias_v_to_fp16)[name = string("op_4029_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_41_perm_0 = const()[name = string("q_with_bias_v_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_467_transpose_x_0 = const()[name = string("x_467_transpose_x_0"), val = bool(false)];
+            bool x_467_transpose_y_0 = const()[name = string("x_467_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_4031_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286740928))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286932992))))[name = string("op_4031_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_4029_cast_fp16)[name = string("transpose_172")];
+            tensor<fp16, [1, 8, 188, 375]> x_467_cast_fp16 = matmul(transpose_x = x_467_transpose_x_0, transpose_y = x_467_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = op_4031_to_fp16_quantized)[name = string("x_467_cast_fp16")];
+            tensor<int32, [8]> x_469_pad_0 = const()[name = string("x_469_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_469_mode_0 = const()[name = string("x_469_mode_0"), val = string("constant")];
+            fp16 const_288_to_fp16 = const()[name = string("const_288_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_469_cast_fp16 = pad(constant_val = const_288_to_fp16, mode = x_469_mode_0, pad = x_469_pad_0, x = x_467_cast_fp16)[name = string("x_469_cast_fp16")];
+            tensor<int32, [4]> var_4039 = const()[name = string("op_4039"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_471_cast_fp16 = reshape(shape = var_4039, x = x_469_cast_fp16)[name = string("x_471_cast_fp16")];
+            tensor<int32, [4]> var_4043_begin_0 = const()[name = string("op_4043_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4043_end_0 = const()[name = string("op_4043_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4043_end_mask_0 = const()[name = string("op_4043_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4043_cast_fp16 = slice_by_index(begin = var_4043_begin_0, end = var_4043_end_0, end_mask = var_4043_end_mask_0, x = x_471_cast_fp16)[name = string("op_4043_cast_fp16")];
+            tensor<int32, [4]> var_4044 = const()[name = string("op_4044"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_81_cast_fp16 = reshape(shape = var_4044, x = var_4043_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<int32, [4]> transpose_136_perm_0 = const()[name = string("transpose_136_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_137_perm_0 = const()[name = string("transpose_137_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = string("transpose_170")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_136 = transpose(perm = transpose_136_perm_0, x = var_4027_cast_fp16)[name = string("transpose_171")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_83_begin_0 = const()[name = string("matrix_bd_83_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_83_end_0 = const()[name = string("matrix_bd_83_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_83_end_mask_0 = const()[name = string("matrix_bd_83_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4053_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = string("op_4053_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<fp16, [1, 8, 188, 188]> _inversed_scores_81_cast_fp16 = mul(x = var_4053_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = string("_inversed_scores_81_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_83_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_15)[name = string("scores_83_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4059_cast_fp16 = softmax(axis = var_152, x = scores_83_cast_fp16)[name = string("op_4059_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1081_cast_fp16 = select(a = var_164_to_fp16, b = var_4059_cast_fp16, cond = mask_15)[name = string("input_1081_cast_fp16")];
+            bool x_473_transpose_x_0 = const()[name = string("x_473_transpose_x_0"), val = bool(false)];
+            bool x_473_transpose_y_0 = const()[name = string("x_473_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_41_cast_fp16)[name = string("transpose_169")];
+            tensor<fp16, [1, 8, 188, 128]> x_473_cast_fp16 = matmul(transpose_x = x_473_transpose_x_0, transpose_y = x_473_transpose_y_0, x = input_1081_cast_fp16, y = value_45_cast_fp16)[name = string("x_473_cast_fp16")];
+            tensor<int32, [4]> var_4063_perm_0 = const()[name = string("op_4063_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4064 = const()[name = string("op_4064"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4063_cast_fp16 = transpose(perm = var_4063_perm_0, x = x_473_cast_fp16)[name = string("transpose_168")];
+            tensor<fp16, [1, 188, 1024]> input_1083_cast_fp16 = reshape(shape = var_4064, x = var_4063_cast_fp16)[name = string("input_1083_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286936064))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287460416))))[name = string("encoder_module_layers_20_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287526016)))];
+            tensor<fp16, [1, 188, 1024]> linear_187_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_out_weight_to_fp16_quantized, x = input_1083_cast_fp16)[name = string("linear_187_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1087_cast_fp16 = add(x = input_1079_cast_fp16, y = linear_187_cast_fp16)[name = string("input_1087_cast_fp16")];
+            tensor<int32, [1]> x_477_axes_0 = const()[name = string("x_477_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287528128)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287530240)))];
+            tensor<fp16, [1, 188, 1024]> x_477_cast_fp16 = layer_norm(axes = x_477_axes_0, beta = encoder_module_layers_20_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_conv_weight_to_fp16, x = input_1087_cast_fp16)[name = string("x_477_cast_fp16")];
+            tensor<int32, [3]> input_1089_perm_0 = const()[name = string("input_1089_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1091_pad_type_0 = const()[name = string("input_1091_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1091_strides_0 = const()[name = string("input_1091_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1091_pad_0 = const()[name = string("input_1091_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1091_dilations_0 = const()[name = string("input_1091_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1091_groups_0 = const()[name = string("input_1091_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287532352))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288580992))))[name = string("encoder_module_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288712128)))];
+            tensor<fp16, [1, 1024, 188]> input_1089_cast_fp16 = transpose(perm = input_1089_perm_0, x = x_477_cast_fp16)[name = string("transpose_167")];
+            tensor<fp16, [1, 2048, 188]> input_1091_cast_fp16 = conv(bias = encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1089_cast_fp16)[name = string("input_1091_cast_fp16")];
+            int32 x_479_split_num_splits_0 = const()[name = string("x_479_split_num_splits_0"), val = int32(2)];
+            int32 x_479_split_axis_0 = const()[name = string("x_479_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_479_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_479_split_cast_fp16_1 = split(axis = x_479_split_axis_0, num_splits = x_479_split_num_splits_0, x = input_1091_cast_fp16)[name = string("x_479_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_479_split_1_sigmoid_cast_fp16 = sigmoid(x = x_479_split_cast_fp16_1)[name = string("x_479_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_479_cast_fp16 = mul(x = x_479_split_cast_fp16_0, y = x_479_split_1_sigmoid_cast_fp16)[name = string("x_479_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1093_cast_fp16 = select(a = var_164_to_fp16, b = x_479_cast_fp16, cond = var_608)[name = string("input_1093_cast_fp16")];
+            tensor<int32, [6]> input_1095_pad_0 = const()[name = string("input_1095_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1095_mode_0 = const()[name = string("input_1095_mode_0"), val = string("constant")];
+            fp16 const_291_to_fp16 = const()[name = string("const_291_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1095_cast_fp16 = pad(constant_val = const_291_to_fp16, mode = input_1095_mode_0, pad = input_1095_pad_0, x = input_1093_cast_fp16)[name = string("input_1095_cast_fp16")];
+            string input_1097_pad_type_0 = const()[name = string("input_1097_pad_type_0"), val = string("valid")];
+            int32 input_1097_groups_0 = const()[name = string("input_1097_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1097_strides_0 = const()[name = string("input_1097_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1097_pad_0 = const()[name = string("input_1097_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1097_dilations_0 = const()[name = string("input_1097_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_362_to_fp16 = const()[name = string("const_362_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288716288)))];
+            tensor<fp16, [1024]> const_363_to_fp16 = const()[name = string("const_363_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288734784)))];
+            tensor<fp16, [1, 1024, 188]> input_1099_cast_fp16 = conv(bias = const_363_to_fp16, dilations = input_1097_dilations_0, groups = input_1097_groups_0, pad = input_1097_pad_0, pad_type = input_1097_pad_type_0, strides = input_1097_strides_0, weight = const_362_to_fp16, x = input_1095_cast_fp16)[name = string("input_1099_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1101_cast_fp16 = silu(x = input_1099_cast_fp16)[name = string("input_1101_cast_fp16")];
+            string x_481_pad_type_0 = const()[name = string("x_481_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_481_strides_0 = const()[name = string("x_481_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_481_pad_0 = const()[name = string("x_481_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_481_dilations_0 = const()[name = string("x_481_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_481_groups_0 = const()[name = string("x_481_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288736896))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289261248))))[name = string("encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289326848)))];
+            tensor<fp16, [1, 1024, 188]> x_481_cast_fp16 = conv(bias = encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16, dilations = x_481_dilations_0, groups = x_481_groups_0, pad = x_481_pad_0, pad_type = x_481_pad_type_0, strides = x_481_strides_0, weight = encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1101_cast_fp16)[name = string("x_481_cast_fp16")];
+            tensor<int32, [3]> input_1103_perm_0 = const()[name = string("input_1103_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1103_cast_fp16 = transpose(perm = input_1103_perm_0, x = x_481_cast_fp16)[name = string("transpose_166")];
+            tensor<fp16, [1, 188, 1024]> input_1105_cast_fp16 = add(x = input_1087_cast_fp16, y = input_1103_cast_fp16)[name = string("input_1105_cast_fp16")];
+            tensor<int32, [1]> input_1107_axes_0 = const()[name = string("input_1107_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289328960)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289331072)))];
+            tensor<fp16, [1, 188, 1024]> input_1107_cast_fp16 = layer_norm(axes = input_1107_axes_0, beta = encoder_module_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1105_cast_fp16)[name = string("input_1107_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(289333184))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291430400))))[name = string("encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291692608)))];
+            tensor<fp16, [1, 188, 4096]> linear_188_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1107_cast_fp16)[name = string("linear_188_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1111_cast_fp16 = silu(x = linear_188_cast_fp16)[name = string("input_1111_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291700864))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(293798080))))[name = string("encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294060288)))];
+            tensor<fp16, [1, 188, 1024]> linear_189_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1111_cast_fp16)[name = string("linear_189_cast_fp16")];
+            fp16 var_4130_to_fp16 = const()[name = string("op_4130_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4131_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_4130_to_fp16)[name = string("op_4131_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1117_cast_fp16 = add(x = input_1105_cast_fp16, y = var_4131_cast_fp16)[name = string("input_1117_cast_fp16")];
+            tensor<int32, [1]> input_1119_axes_0 = const()[name = string("input_1119_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294062400)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294064512)))];
+            tensor<fp16, [1, 188, 1024]> input_1119_cast_fp16 = layer_norm(axes = input_1119_axes_0, beta = encoder_module_layers_20_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_out_weight_to_fp16, x = input_1117_cast_fp16)[name = string("input_1119_cast_fp16")];
+            tensor<int32, [1]> input_1121_axes_0 = const()[name = string("input_1121_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294066624)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294068736)))];
+            tensor<fp16, [1, 188, 1024]> input_1121_cast_fp16 = layer_norm(axes = input_1121_axes_0, beta = encoder_module_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1119_cast_fp16)[name = string("input_1121_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(294070848))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296168064))))[name = string("encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296430272)))];
+            tensor<fp16, [1, 188, 4096]> linear_190_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1121_cast_fp16)[name = string("linear_190_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1125_cast_fp16 = silu(x = linear_190_cast_fp16)[name = string("input_1125_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(296438528))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298535744))))[name = string("encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298797952)))];
+            tensor<fp16, [1, 188, 1024]> linear_191_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1125_cast_fp16)[name = string("linear_191_cast_fp16")];
+            fp16 var_4161_to_fp16 = const()[name = string("op_4161_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4162_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_4161_to_fp16)[name = string("op_4162_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1131_cast_fp16 = add(x = input_1119_cast_fp16, y = var_4162_cast_fp16)[name = string("input_1131_cast_fp16")];
+            tensor<int32, [1]> query_43_axes_0 = const()[name = string("query_43_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298800064)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298802176)))];
+            tensor<fp16, [1, 188, 1024]> query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = encoder_module_layers_21_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_self_att_weight_to_fp16, x = input_1131_cast_fp16)[name = string("query_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298804288))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299328640))))[name = string("encoder_module_layers_21_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299394240)))];
+            tensor<fp16, [1, 188, 1024]> linear_192_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_q_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = string("linear_192_cast_fp16")];
+            tensor<int32, [4]> var_4179 = const()[name = string("op_4179"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_127_cast_fp16 = reshape(shape = var_4179, x = linear_192_cast_fp16)[name = string("q_127_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299396352))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299920704))))[name = string("encoder_module_layers_21_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299986304)))];
+            tensor<fp16, [1, 188, 1024]> linear_193_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_k_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = string("linear_193_cast_fp16")];
+            tensor<int32, [4]> var_4184 = const()[name = string("op_4184"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_85_cast_fp16 = reshape(shape = var_4184, x = linear_193_cast_fp16)[name = string("k_85_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299988416))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300512768))))[name = string("encoder_module_layers_21_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300578368)))];
+            tensor<fp16, [1, 188, 1024]> linear_194_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_v_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = string("linear_194_cast_fp16")];
+            tensor<int32, [4]> var_4189 = const()[name = string("op_4189"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_43_cast_fp16 = reshape(shape = var_4189, x = linear_194_cast_fp16)[name = string("v_43_cast_fp16")];
+            tensor<int32, [4]> value_47_perm_0 = const()[name = string("value_47_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300580480)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4201_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_module_layers_21_self_attn_pos_bias_u_to_fp16)[name = string("op_4201_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300582592)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4203_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_module_layers_21_self_attn_pos_bias_v_to_fp16)[name = string("op_4203_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_43_perm_0 = const()[name = string("q_with_bias_v_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_489_transpose_x_0 = const()[name = string("x_489_transpose_x_0"), val = bool(false)];
+            bool x_489_transpose_y_0 = const()[name = string("x_489_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_4205_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300584704))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300776768))))[name = string("op_4205_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_4203_cast_fp16)[name = string("transpose_165")];
+            tensor<fp16, [1, 8, 188, 375]> x_489_cast_fp16 = matmul(transpose_x = x_489_transpose_x_0, transpose_y = x_489_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = op_4205_to_fp16_quantized)[name = string("x_489_cast_fp16")];
+            tensor<int32, [8]> x_491_pad_0 = const()[name = string("x_491_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_491_mode_0 = const()[name = string("x_491_mode_0"), val = string("constant")];
+            fp16 const_298_to_fp16 = const()[name = string("const_298_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_491_cast_fp16 = pad(constant_val = const_298_to_fp16, mode = x_491_mode_0, pad = x_491_pad_0, x = x_489_cast_fp16)[name = string("x_491_cast_fp16")];
+            tensor<int32, [4]> var_4213 = const()[name = string("op_4213"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_493_cast_fp16 = reshape(shape = var_4213, x = x_491_cast_fp16)[name = string("x_493_cast_fp16")];
+            tensor<int32, [4]> var_4217_begin_0 = const()[name = string("op_4217_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4217_end_0 = const()[name = string("op_4217_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4217_end_mask_0 = const()[name = string("op_4217_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4217_cast_fp16 = slice_by_index(begin = var_4217_begin_0, end = var_4217_end_0, end_mask = var_4217_end_mask_0, x = x_493_cast_fp16)[name = string("op_4217_cast_fp16")];
+            tensor<int32, [4]> var_4218 = const()[name = string("op_4218"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_85_cast_fp16 = reshape(shape = var_4218, x = var_4217_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<int32, [4]> transpose_138_perm_0 = const()[name = string("transpose_138_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_139_perm_0 = const()[name = string("transpose_139_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = string("transpose_163")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_138 = transpose(perm = transpose_138_perm_0, x = var_4201_cast_fp16)[name = string("transpose_164")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_87_begin_0 = const()[name = string("matrix_bd_87_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_87_end_0 = const()[name = string("matrix_bd_87_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_87_end_mask_0 = const()[name = string("matrix_bd_87_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4227_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = string("op_4227_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<fp16, [1, 8, 188, 188]> _inversed_scores_85_cast_fp16 = mul(x = var_4227_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = string("_inversed_scores_85_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_87_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_15)[name = string("scores_87_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4233_cast_fp16 = softmax(axis = var_152, x = scores_87_cast_fp16)[name = string("op_4233_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1133_cast_fp16 = select(a = var_164_to_fp16, b = var_4233_cast_fp16, cond = mask_15)[name = string("input_1133_cast_fp16")];
+            bool x_495_transpose_x_0 = const()[name = string("x_495_transpose_x_0"), val = bool(false)];
+            bool x_495_transpose_y_0 = const()[name = string("x_495_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_43_cast_fp16)[name = string("transpose_162")];
+            tensor<fp16, [1, 8, 188, 128]> x_495_cast_fp16 = matmul(transpose_x = x_495_transpose_x_0, transpose_y = x_495_transpose_y_0, x = input_1133_cast_fp16, y = value_47_cast_fp16)[name = string("x_495_cast_fp16")];
+            tensor<int32, [4]> var_4237_perm_0 = const()[name = string("op_4237_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4238 = const()[name = string("op_4238"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4237_cast_fp16 = transpose(perm = var_4237_perm_0, x = x_495_cast_fp16)[name = string("transpose_161")];
+            tensor<fp16, [1, 188, 1024]> input_1135_cast_fp16 = reshape(shape = var_4238, x = var_4237_cast_fp16)[name = string("input_1135_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(300779840))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301304192))))[name = string("encoder_module_layers_21_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301369792)))];
+            tensor<fp16, [1, 188, 1024]> linear_196_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_out_weight_to_fp16_quantized, x = input_1135_cast_fp16)[name = string("linear_196_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1139_cast_fp16 = add(x = input_1131_cast_fp16, y = linear_196_cast_fp16)[name = string("input_1139_cast_fp16")];
+            tensor<int32, [1]> x_499_axes_0 = const()[name = string("x_499_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301371904)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301374016)))];
+            tensor<fp16, [1, 188, 1024]> x_499_cast_fp16 = layer_norm(axes = x_499_axes_0, beta = encoder_module_layers_21_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_conv_weight_to_fp16, x = input_1139_cast_fp16)[name = string("x_499_cast_fp16")];
+            tensor<int32, [3]> input_1141_perm_0 = const()[name = string("input_1141_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1143_pad_type_0 = const()[name = string("input_1143_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1143_strides_0 = const()[name = string("input_1143_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1143_pad_0 = const()[name = string("input_1143_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1143_dilations_0 = const()[name = string("input_1143_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1143_groups_0 = const()[name = string("input_1143_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301376128))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302424768))))[name = string("encoder_module_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302555904)))];
+            tensor<fp16, [1, 1024, 188]> input_1141_cast_fp16 = transpose(perm = input_1141_perm_0, x = x_499_cast_fp16)[name = string("transpose_160")];
+            tensor<fp16, [1, 2048, 188]> input_1143_cast_fp16 = conv(bias = encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1141_cast_fp16)[name = string("input_1143_cast_fp16")];
+            int32 x_501_split_num_splits_0 = const()[name = string("x_501_split_num_splits_0"), val = int32(2)];
+            int32 x_501_split_axis_0 = const()[name = string("x_501_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_501_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_501_split_cast_fp16_1 = split(axis = x_501_split_axis_0, num_splits = x_501_split_num_splits_0, x = input_1143_cast_fp16)[name = string("x_501_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_501_split_1_sigmoid_cast_fp16 = sigmoid(x = x_501_split_cast_fp16_1)[name = string("x_501_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_501_cast_fp16 = mul(x = x_501_split_cast_fp16_0, y = x_501_split_1_sigmoid_cast_fp16)[name = string("x_501_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1145_cast_fp16 = select(a = var_164_to_fp16, b = x_501_cast_fp16, cond = var_608)[name = string("input_1145_cast_fp16")];
+            tensor<int32, [6]> input_1147_pad_0 = const()[name = string("input_1147_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1147_mode_0 = const()[name = string("input_1147_mode_0"), val = string("constant")];
+            fp16 const_301_to_fp16 = const()[name = string("const_301_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1147_cast_fp16 = pad(constant_val = const_301_to_fp16, mode = input_1147_mode_0, pad = input_1147_pad_0, x = input_1145_cast_fp16)[name = string("input_1147_cast_fp16")];
+            string input_1149_pad_type_0 = const()[name = string("input_1149_pad_type_0"), val = string("valid")];
+            int32 input_1149_groups_0 = const()[name = string("input_1149_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1149_strides_0 = const()[name = string("input_1149_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1149_pad_0 = const()[name = string("input_1149_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1149_dilations_0 = const()[name = string("input_1149_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_364_to_fp16 = const()[name = string("const_364_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302560064)))];
+            tensor<fp16, [1024]> const_365_to_fp16 = const()[name = string("const_365_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302578560)))];
+            tensor<fp16, [1, 1024, 188]> input_1151_cast_fp16 = conv(bias = const_365_to_fp16, dilations = input_1149_dilations_0, groups = input_1149_groups_0, pad = input_1149_pad_0, pad_type = input_1149_pad_type_0, strides = input_1149_strides_0, weight = const_364_to_fp16, x = input_1147_cast_fp16)[name = string("input_1151_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1153_cast_fp16 = silu(x = input_1151_cast_fp16)[name = string("input_1153_cast_fp16")];
+            string x_503_pad_type_0 = const()[name = string("x_503_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_503_strides_0 = const()[name = string("x_503_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_503_pad_0 = const()[name = string("x_503_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_503_dilations_0 = const()[name = string("x_503_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_503_groups_0 = const()[name = string("x_503_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302580672))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303105024))))[name = string("encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303170624)))];
+            tensor<fp16, [1, 1024, 188]> x_503_cast_fp16 = conv(bias = encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16, dilations = x_503_dilations_0, groups = x_503_groups_0, pad = x_503_pad_0, pad_type = x_503_pad_type_0, strides = x_503_strides_0, weight = encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1153_cast_fp16)[name = string("x_503_cast_fp16")];
+            tensor<int32, [3]> input_1155_perm_0 = const()[name = string("input_1155_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1155_cast_fp16 = transpose(perm = input_1155_perm_0, x = x_503_cast_fp16)[name = string("transpose_159")];
+            tensor<fp16, [1, 188, 1024]> input_1157_cast_fp16 = add(x = input_1139_cast_fp16, y = input_1155_cast_fp16)[name = string("input_1157_cast_fp16")];
+            tensor<int32, [1]> input_1159_axes_0 = const()[name = string("input_1159_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303172736)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303174848)))];
+            tensor<fp16, [1, 188, 1024]> input_1159_cast_fp16 = layer_norm(axes = input_1159_axes_0, beta = encoder_module_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1157_cast_fp16)[name = string("input_1159_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303176960))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305274176))))[name = string("encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305536384)))];
+            tensor<fp16, [1, 188, 4096]> linear_197_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1159_cast_fp16)[name = string("linear_197_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1163_cast_fp16 = silu(x = linear_197_cast_fp16)[name = string("input_1163_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305544640))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307641856))))[name = string("encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307904064)))];
+            tensor<fp16, [1, 188, 1024]> linear_198_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1163_cast_fp16)[name = string("linear_198_cast_fp16")];
+            fp16 var_4304_to_fp16 = const()[name = string("op_4304_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4305_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_4304_to_fp16)[name = string("op_4305_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1169_cast_fp16 = add(x = input_1157_cast_fp16, y = var_4305_cast_fp16)[name = string("input_1169_cast_fp16")];
+            tensor<int32, [1]> input_1171_axes_0 = const()[name = string("input_1171_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307906176)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307908288)))];
+            tensor<fp16, [1, 188, 1024]> input_1171_cast_fp16 = layer_norm(axes = input_1171_axes_0, beta = encoder_module_layers_21_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_out_weight_to_fp16, x = input_1169_cast_fp16)[name = string("input_1171_cast_fp16")];
+            tensor<int32, [1]> input_1173_axes_0 = const()[name = string("input_1173_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307910400)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307912512)))];
+            tensor<fp16, [1, 188, 1024]> input_1173_cast_fp16 = layer_norm(axes = input_1173_axes_0, beta = encoder_module_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1171_cast_fp16)[name = string("input_1173_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307914624))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310011840))))[name = string("encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310274048)))];
+            tensor<fp16, [1, 188, 4096]> linear_199_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1173_cast_fp16)[name = string("linear_199_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1177_cast_fp16 = silu(x = linear_199_cast_fp16)[name = string("input_1177_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310282304))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312379520))))[name = string("encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312641728)))];
+            tensor<fp16, [1, 188, 1024]> linear_200_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1177_cast_fp16)[name = string("linear_200_cast_fp16")];
+            fp16 var_4335_to_fp16 = const()[name = string("op_4335_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4336_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_4335_to_fp16)[name = string("op_4336_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1183_cast_fp16 = add(x = input_1171_cast_fp16, y = var_4336_cast_fp16)[name = string("input_1183_cast_fp16")];
+            tensor<int32, [1]> query_45_axes_0 = const()[name = string("query_45_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312643840)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312645952)))];
+            tensor<fp16, [1, 188, 1024]> query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = encoder_module_layers_22_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_self_att_weight_to_fp16, x = input_1183_cast_fp16)[name = string("query_45_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312648064))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313172416))))[name = string("encoder_module_layers_22_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313238016)))];
+            tensor<fp16, [1, 188, 1024]> linear_201_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_q_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = string("linear_201_cast_fp16")];
+            tensor<int32, [4]> var_4353 = const()[name = string("op_4353"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_133_cast_fp16 = reshape(shape = var_4353, x = linear_201_cast_fp16)[name = string("q_133_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313240128))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313764480))))[name = string("encoder_module_layers_22_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313830080)))];
+            tensor<fp16, [1, 188, 1024]> linear_202_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_k_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = string("linear_202_cast_fp16")];
+            tensor<int32, [4]> var_4358 = const()[name = string("op_4358"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_89_cast_fp16 = reshape(shape = var_4358, x = linear_202_cast_fp16)[name = string("k_89_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(313832192))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314356544))))[name = string("encoder_module_layers_22_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314422144)))];
+            tensor<fp16, [1, 188, 1024]> linear_203_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_v_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = string("linear_203_cast_fp16")];
+            tensor<int32, [4]> var_4363 = const()[name = string("op_4363"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_45_cast_fp16 = reshape(shape = var_4363, x = linear_203_cast_fp16)[name = string("v_45_cast_fp16")];
+            tensor<int32, [4]> value_49_perm_0 = const()[name = string("value_49_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314424256)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4375_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_module_layers_22_self_attn_pos_bias_u_to_fp16)[name = string("op_4375_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314426368)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4377_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_module_layers_22_self_attn_pos_bias_v_to_fp16)[name = string("op_4377_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_45_perm_0 = const()[name = string("q_with_bias_v_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_511_transpose_x_0 = const()[name = string("x_511_transpose_x_0"), val = bool(false)];
+            bool x_511_transpose_y_0 = const()[name = string("x_511_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_4379_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314428480))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314620544))))[name = string("op_4379_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_4377_cast_fp16)[name = string("transpose_158")];
+            tensor<fp16, [1, 8, 188, 375]> x_511_cast_fp16 = matmul(transpose_x = x_511_transpose_x_0, transpose_y = x_511_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = op_4379_to_fp16_quantized)[name = string("x_511_cast_fp16")];
+            tensor<int32, [8]> x_513_pad_0 = const()[name = string("x_513_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_513_mode_0 = const()[name = string("x_513_mode_0"), val = string("constant")];
+            fp16 const_308_to_fp16 = const()[name = string("const_308_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_513_cast_fp16 = pad(constant_val = const_308_to_fp16, mode = x_513_mode_0, pad = x_513_pad_0, x = x_511_cast_fp16)[name = string("x_513_cast_fp16")];
+            tensor<int32, [4]> var_4387 = const()[name = string("op_4387"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_515_cast_fp16 = reshape(shape = var_4387, x = x_513_cast_fp16)[name = string("x_515_cast_fp16")];
+            tensor<int32, [4]> var_4391_begin_0 = const()[name = string("op_4391_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4391_end_0 = const()[name = string("op_4391_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4391_end_mask_0 = const()[name = string("op_4391_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4391_cast_fp16 = slice_by_index(begin = var_4391_begin_0, end = var_4391_end_0, end_mask = var_4391_end_mask_0, x = x_515_cast_fp16)[name = string("op_4391_cast_fp16")];
+            tensor<int32, [4]> var_4392 = const()[name = string("op_4392"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_89_cast_fp16 = reshape(shape = var_4392, x = var_4391_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<int32, [4]> transpose_140_perm_0 = const()[name = string("transpose_140_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_141_perm_0 = const()[name = string("transpose_141_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = string("transpose_156")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_140 = transpose(perm = transpose_140_perm_0, x = var_4375_cast_fp16)[name = string("transpose_157")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_91_begin_0 = const()[name = string("matrix_bd_91_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_91_end_0 = const()[name = string("matrix_bd_91_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_91_end_mask_0 = const()[name = string("matrix_bd_91_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4401_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = string("op_4401_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<fp16, [1, 8, 188, 188]> _inversed_scores_89_cast_fp16 = mul(x = var_4401_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = string("_inversed_scores_89_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_91_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_15)[name = string("scores_91_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4407_cast_fp16 = softmax(axis = var_152, x = scores_91_cast_fp16)[name = string("op_4407_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1185_cast_fp16 = select(a = var_164_to_fp16, b = var_4407_cast_fp16, cond = mask_15)[name = string("input_1185_cast_fp16")];
+            bool x_517_transpose_x_0 = const()[name = string("x_517_transpose_x_0"), val = bool(false)];
+            bool x_517_transpose_y_0 = const()[name = string("x_517_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_49_cast_fp16 = transpose(perm = value_49_perm_0, x = v_45_cast_fp16)[name = string("transpose_155")];
+            tensor<fp16, [1, 8, 188, 128]> x_517_cast_fp16 = matmul(transpose_x = x_517_transpose_x_0, transpose_y = x_517_transpose_y_0, x = input_1185_cast_fp16, y = value_49_cast_fp16)[name = string("x_517_cast_fp16")];
+            tensor<int32, [4]> var_4411_perm_0 = const()[name = string("op_4411_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4412 = const()[name = string("op_4412"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4411_cast_fp16 = transpose(perm = var_4411_perm_0, x = x_517_cast_fp16)[name = string("transpose_154")];
+            tensor<fp16, [1, 188, 1024]> input_1187_cast_fp16 = reshape(shape = var_4412, x = var_4411_cast_fp16)[name = string("input_1187_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314623616))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315147968))))[name = string("encoder_module_layers_22_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315213568)))];
+            tensor<fp16, [1, 188, 1024]> linear_205_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_out_weight_to_fp16_quantized, x = input_1187_cast_fp16)[name = string("linear_205_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1191_cast_fp16 = add(x = input_1183_cast_fp16, y = linear_205_cast_fp16)[name = string("input_1191_cast_fp16")];
+            tensor<int32, [1]> x_521_axes_0 = const()[name = string("x_521_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315215680)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315217792)))];
+            tensor<fp16, [1, 188, 1024]> x_521_cast_fp16 = layer_norm(axes = x_521_axes_0, beta = encoder_module_layers_22_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_conv_weight_to_fp16, x = input_1191_cast_fp16)[name = string("x_521_cast_fp16")];
+            tensor<int32, [3]> input_1193_perm_0 = const()[name = string("input_1193_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1195_pad_type_0 = const()[name = string("input_1195_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1195_strides_0 = const()[name = string("input_1195_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1195_pad_0 = const()[name = string("input_1195_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1195_dilations_0 = const()[name = string("input_1195_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1195_groups_0 = const()[name = string("input_1195_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315219904))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316268544))))[name = string("encoder_module_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316399680)))];
+            tensor<fp16, [1, 1024, 188]> input_1193_cast_fp16 = transpose(perm = input_1193_perm_0, x = x_521_cast_fp16)[name = string("transpose_153")];
+            tensor<fp16, [1, 2048, 188]> input_1195_cast_fp16 = conv(bias = encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1193_cast_fp16)[name = string("input_1195_cast_fp16")];
+            int32 x_523_split_num_splits_0 = const()[name = string("x_523_split_num_splits_0"), val = int32(2)];
+            int32 x_523_split_axis_0 = const()[name = string("x_523_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_523_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_523_split_cast_fp16_1 = split(axis = x_523_split_axis_0, num_splits = x_523_split_num_splits_0, x = input_1195_cast_fp16)[name = string("x_523_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_523_split_1_sigmoid_cast_fp16 = sigmoid(x = x_523_split_cast_fp16_1)[name = string("x_523_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_523_cast_fp16 = mul(x = x_523_split_cast_fp16_0, y = x_523_split_1_sigmoid_cast_fp16)[name = string("x_523_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1197_cast_fp16 = select(a = var_164_to_fp16, b = x_523_cast_fp16, cond = var_608)[name = string("input_1197_cast_fp16")];
+            tensor<int32, [6]> input_1199_pad_0 = const()[name = string("input_1199_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1199_mode_0 = const()[name = string("input_1199_mode_0"), val = string("constant")];
+            fp16 const_311_to_fp16 = const()[name = string("const_311_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1199_cast_fp16 = pad(constant_val = const_311_to_fp16, mode = input_1199_mode_0, pad = input_1199_pad_0, x = input_1197_cast_fp16)[name = string("input_1199_cast_fp16")];
+            string input_1201_pad_type_0 = const()[name = string("input_1201_pad_type_0"), val = string("valid")];
+            int32 input_1201_groups_0 = const()[name = string("input_1201_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1201_strides_0 = const()[name = string("input_1201_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1201_pad_0 = const()[name = string("input_1201_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1201_dilations_0 = const()[name = string("input_1201_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_366_to_fp16 = const()[name = string("const_366_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316403840)))];
+            tensor<fp16, [1024]> const_367_to_fp16 = const()[name = string("const_367_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316422336)))];
+            tensor<fp16, [1, 1024, 188]> input_1203_cast_fp16 = conv(bias = const_367_to_fp16, dilations = input_1201_dilations_0, groups = input_1201_groups_0, pad = input_1201_pad_0, pad_type = input_1201_pad_type_0, strides = input_1201_strides_0, weight = const_366_to_fp16, x = input_1199_cast_fp16)[name = string("input_1203_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1205_cast_fp16 = silu(x = input_1203_cast_fp16)[name = string("input_1205_cast_fp16")];
+            string x_525_pad_type_0 = const()[name = string("x_525_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_525_strides_0 = const()[name = string("x_525_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_525_pad_0 = const()[name = string("x_525_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_525_dilations_0 = const()[name = string("x_525_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_525_groups_0 = const()[name = string("x_525_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316424448))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(316948800))))[name = string("encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317014400)))];
+            tensor<fp16, [1, 1024, 188]> x_525_cast_fp16 = conv(bias = encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16, dilations = x_525_dilations_0, groups = x_525_groups_0, pad = x_525_pad_0, pad_type = x_525_pad_type_0, strides = x_525_strides_0, weight = encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1205_cast_fp16)[name = string("x_525_cast_fp16")];
+            tensor<int32, [3]> input_1207_perm_0 = const()[name = string("input_1207_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1207_cast_fp16 = transpose(perm = input_1207_perm_0, x = x_525_cast_fp16)[name = string("transpose_152")];
+            tensor<fp16, [1, 188, 1024]> input_1209_cast_fp16 = add(x = input_1191_cast_fp16, y = input_1207_cast_fp16)[name = string("input_1209_cast_fp16")];
+            tensor<int32, [1]> input_1211_axes_0 = const()[name = string("input_1211_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317016512)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317018624)))];
+            tensor<fp16, [1, 188, 1024]> input_1211_cast_fp16 = layer_norm(axes = input_1211_axes_0, beta = encoder_module_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1209_cast_fp16)[name = string("input_1211_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(317020736))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319117952))))[name = string("encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319380160)))];
+            tensor<fp16, [1, 188, 4096]> linear_206_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1211_cast_fp16)[name = string("linear_206_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1215_cast_fp16 = silu(x = linear_206_cast_fp16)[name = string("input_1215_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319388416))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321485632))))[name = string("encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321747840)))];
+            tensor<fp16, [1, 188, 1024]> linear_207_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1215_cast_fp16)[name = string("linear_207_cast_fp16")];
+            fp16 var_4478_to_fp16 = const()[name = string("op_4478_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4479_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_4478_to_fp16)[name = string("op_4479_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1221_cast_fp16 = add(x = input_1209_cast_fp16, y = var_4479_cast_fp16)[name = string("input_1221_cast_fp16")];
+            tensor<int32, [1]> input_1223_axes_0 = const()[name = string("input_1223_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321749952)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321752064)))];
+            tensor<fp16, [1, 188, 1024]> input_1223_cast_fp16 = layer_norm(axes = input_1223_axes_0, beta = encoder_module_layers_22_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_out_weight_to_fp16, x = input_1221_cast_fp16)[name = string("input_1223_cast_fp16")];
+            tensor<int32, [1]> input_1225_axes_0 = const()[name = string("input_1225_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321754176)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321756288)))];
+            tensor<fp16, [1, 188, 1024]> input_1225_cast_fp16 = layer_norm(axes = input_1225_axes_0, beta = encoder_module_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1223_cast_fp16)[name = string("input_1225_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(321758400))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(323855616))))[name = string("encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324117824)))];
+            tensor<fp16, [1, 188, 4096]> linear_208_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1225_cast_fp16)[name = string("linear_208_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1229_cast_fp16 = silu(x = linear_208_cast_fp16)[name = string("input_1229_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324126080))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326223296))))[name = string("encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326485504)))];
+            tensor<fp16, [1, 188, 1024]> linear_209_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1229_cast_fp16)[name = string("linear_209_cast_fp16")];
+            fp16 var_4509_to_fp16 = const()[name = string("op_4509_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4510_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_4509_to_fp16)[name = string("op_4510_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1235_cast_fp16 = add(x = input_1223_cast_fp16, y = var_4510_cast_fp16)[name = string("input_1235_cast_fp16")];
+            tensor<int32, [1]> query_axes_0 = const()[name = string("query_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326487616)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326489728)))];
+            tensor<fp16, [1, 188, 1024]> query_cast_fp16 = layer_norm(axes = query_axes_0, beta = encoder_module_layers_23_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_self_att_weight_to_fp16, x = input_1235_cast_fp16)[name = string("query_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(326491840))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327016192))))[name = string("encoder_module_layers_23_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327081792)))];
+            tensor<fp16, [1, 188, 1024]> linear_210_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_q_weight_to_fp16_quantized, x = query_cast_fp16)[name = string("linear_210_cast_fp16")];
+            tensor<int32, [4]> var_4527 = const()[name = string("op_4527"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_139_cast_fp16 = reshape(shape = var_4527, x = linear_210_cast_fp16)[name = string("q_139_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327083904))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327608256))))[name = string("encoder_module_layers_23_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327673856)))];
+            tensor<fp16, [1, 188, 1024]> linear_211_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_k_weight_to_fp16_quantized, x = query_cast_fp16)[name = string("linear_211_cast_fp16")];
+            tensor<int32, [4]> var_4532 = const()[name = string("op_4532"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_93_cast_fp16 = reshape(shape = var_4532, x = linear_211_cast_fp16)[name = string("k_93_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(327675968))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328200320))))[name = string("encoder_module_layers_23_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328265920)))];
+            tensor<fp16, [1, 188, 1024]> linear_212_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_v_weight_to_fp16_quantized, x = query_cast_fp16)[name = string("linear_212_cast_fp16")];
+            tensor<int32, [4]> var_4537 = const()[name = string("op_4537"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_cast_fp16 = reshape(shape = var_4537, x = linear_212_cast_fp16)[name = string("v_cast_fp16")];
+            tensor<int32, [4]> value_perm_0 = const()[name = string("value_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328268032)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4549_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_module_layers_23_self_attn_pos_bias_u_to_fp16)[name = string("op_4549_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328270144)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4551_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_module_layers_23_self_attn_pos_bias_v_to_fp16)[name = string("op_4551_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_perm_0 = const()[name = string("q_with_bias_v_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            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<fp16, [1, 8, 128, 375]> op_4553_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328272256))), scale = tensor<fp16, [1, 1, 4, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328464320))))[name = string("op_4553_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4551_cast_fp16)[name = string("transpose_151")];
+            tensor<fp16, [1, 8, 188, 375]> x_533_cast_fp16 = matmul(transpose_x = x_533_transpose_x_0, transpose_y = x_533_transpose_y_0, x = q_with_bias_v_cast_fp16, y = op_4553_to_fp16_quantized)[name = string("x_533_cast_fp16")];
+            tensor<int32, [8]> x_535_pad_0 = const()[name = string("x_535_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_535_mode_0 = const()[name = string("x_535_mode_0"), val = string("constant")];
+            fp16 const_318_to_fp16 = const()[name = string("const_318_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_535_cast_fp16 = pad(constant_val = const_318_to_fp16, mode = x_535_mode_0, pad = x_535_pad_0, x = x_533_cast_fp16)[name = string("x_535_cast_fp16")];
+            tensor<int32, [4]> var_4561 = const()[name = string("op_4561"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_537_cast_fp16 = reshape(shape = var_4561, x = x_535_cast_fp16)[name = string("x_537_cast_fp16")];
+            tensor<int32, [4]> var_4565_begin_0 = const()[name = string("op_4565_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4565_end_0 = const()[name = string("op_4565_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4565_end_mask_0 = const()[name = string("op_4565_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4565_cast_fp16 = slice_by_index(begin = var_4565_begin_0, end = var_4565_end_0, end_mask = var_4565_end_mask_0, x = x_537_cast_fp16)[name = string("op_4565_cast_fp16")];
+            tensor<int32, [4]> var_4566 = const()[name = string("op_4566"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_93_cast_fp16 = reshape(shape = var_4566, x = var_4565_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<int32, [4]> transpose_142_perm_0 = const()[name = string("transpose_142_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_143_perm_0 = const()[name = string("transpose_143_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = string("transpose_149")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_142 = transpose(perm = transpose_142_perm_0, x = var_4549_cast_fp16)[name = string("transpose_150")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_end_0 = const()[name = string("matrix_bd_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4575_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = string("op_4575_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<fp16, [1, 8, 188, 188]> _inversed_scores_93_cast_fp16 = mul(x = var_4575_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = string("_inversed_scores_93_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_15)[name = string("scores_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4581_cast_fp16 = softmax(axis = var_152, x = scores_cast_fp16)[name = string("op_4581_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1237_cast_fp16 = select(a = var_164_to_fp16, b = var_4581_cast_fp16, cond = mask_15)[name = string("input_1237_cast_fp16")];
+            bool x_539_transpose_x_0 = const()[name = string("x_539_transpose_x_0"), val = bool(false)];
+            bool x_539_transpose_y_0 = const()[name = string("x_539_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = string("transpose_148")];
+            tensor<fp16, [1, 8, 188, 128]> x_539_cast_fp16 = matmul(transpose_x = x_539_transpose_x_0, transpose_y = x_539_transpose_y_0, x = input_1237_cast_fp16, y = value_cast_fp16)[name = string("x_539_cast_fp16")];
+            tensor<int32, [4]> var_4585_perm_0 = const()[name = string("op_4585_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4586 = const()[name = string("op_4586"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4585_cast_fp16 = transpose(perm = var_4585_perm_0, x = x_539_cast_fp16)[name = string("transpose_147")];
+            tensor<fp16, [1, 188, 1024]> input_1239_cast_fp16 = reshape(shape = var_4586, x = var_4585_cast_fp16)[name = string("input_1239_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328467392))), scale = tensor<fp16, [1024, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328991744))))[name = string("encoder_module_layers_23_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329057344)))];
+            tensor<fp16, [1, 188, 1024]> linear_214_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_out_weight_to_fp16_quantized, x = input_1239_cast_fp16)[name = string("linear_214_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1243_cast_fp16 = add(x = input_1235_cast_fp16, y = linear_214_cast_fp16)[name = string("input_1243_cast_fp16")];
+            tensor<int32, [1]> x_543_axes_0 = const()[name = string("x_543_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329059456)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329061568)))];
+            tensor<fp16, [1, 188, 1024]> x_543_cast_fp16 = layer_norm(axes = x_543_axes_0, beta = encoder_module_layers_23_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_conv_weight_to_fp16, x = input_1243_cast_fp16)[name = string("x_543_cast_fp16")];
+            tensor<int32, [3]> input_1245_perm_0 = const()[name = string("input_1245_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1247_pad_type_0 = const()[name = string("input_1247_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1247_strides_0 = const()[name = string("input_1247_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1247_pad_0 = const()[name = string("input_1247_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1247_dilations_0 = const()[name = string("input_1247_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1247_groups_0 = const()[name = string("input_1247_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329063680))), scale = tensor<fp16, [2048, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330112320))))[name = string("encoder_module_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330243456)))];
+            tensor<fp16, [1, 1024, 188]> input_1245_cast_fp16 = transpose(perm = input_1245_perm_0, x = x_543_cast_fp16)[name = string("transpose_146")];
+            tensor<fp16, [1, 2048, 188]> input_1247_cast_fp16 = conv(bias = encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1245_cast_fp16)[name = string("input_1247_cast_fp16")];
+            int32 x_545_split_num_splits_0 = const()[name = string("x_545_split_num_splits_0"), val = int32(2)];
+            int32 x_545_split_axis_0 = const()[name = string("x_545_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_545_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_545_split_cast_fp16_1 = split(axis = x_545_split_axis_0, num_splits = x_545_split_num_splits_0, x = input_1247_cast_fp16)[name = string("x_545_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_545_split_1_sigmoid_cast_fp16 = sigmoid(x = x_545_split_cast_fp16_1)[name = string("x_545_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_545_cast_fp16 = mul(x = x_545_split_cast_fp16_0, y = x_545_split_1_sigmoid_cast_fp16)[name = string("x_545_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1249_cast_fp16 = select(a = var_164_to_fp16, b = x_545_cast_fp16, cond = var_608)[name = string("input_1249_cast_fp16")];
+            tensor<int32, [6]> input_1251_pad_0 = const()[name = string("input_1251_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1251_mode_0 = const()[name = string("input_1251_mode_0"), val = string("constant")];
+            fp16 const_321_to_fp16 = const()[name = string("const_321_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1251_cast_fp16 = pad(constant_val = const_321_to_fp16, mode = input_1251_mode_0, pad = input_1251_pad_0, x = input_1249_cast_fp16)[name = string("input_1251_cast_fp16")];
+            string input_1253_pad_type_0 = const()[name = string("input_1253_pad_type_0"), val = string("valid")];
+            int32 input_1253_groups_0 = const()[name = string("input_1253_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1253_strides_0 = const()[name = string("input_1253_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1253_pad_0 = const()[name = string("input_1253_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1253_dilations_0 = const()[name = string("input_1253_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_368_to_fp16 = const()[name = string("const_368_to_fp16"), val = tensor<fp16, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330247616)))];
+            tensor<fp16, [1024]> const_369_to_fp16 = const()[name = string("const_369_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330266112)))];
+            tensor<fp16, [1, 1024, 188]> input_1255_cast_fp16 = conv(bias = const_369_to_fp16, dilations = input_1253_dilations_0, groups = input_1253_groups_0, pad = input_1253_pad_0, pad_type = input_1253_pad_type_0, strides = input_1253_strides_0, weight = const_368_to_fp16, x = input_1251_cast_fp16)[name = string("input_1255_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1257_cast_fp16 = silu(x = input_1255_cast_fp16)[name = string("input_1257_cast_fp16")];
+            string x_547_pad_type_0 = const()[name = string("x_547_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_547_strides_0 = const()[name = string("x_547_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_547_pad_0 = const()[name = string("x_547_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_547_dilations_0 = const()[name = string("x_547_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_547_groups_0 = const()[name = string("x_547_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330268224))), scale = tensor<fp16, [1024, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330792576))))[name = string("encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330858176)))];
+            tensor<fp16, [1, 1024, 188]> x_547_cast_fp16 = conv(bias = encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16, dilations = x_547_dilations_0, groups = x_547_groups_0, pad = x_547_pad_0, pad_type = x_547_pad_type_0, strides = x_547_strides_0, weight = encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1257_cast_fp16)[name = string("x_547_cast_fp16")];
+            tensor<int32, [3]> input_1259_perm_0 = const()[name = string("input_1259_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1259_cast_fp16 = transpose(perm = input_1259_perm_0, x = x_547_cast_fp16)[name = string("transpose_145")];
+            tensor<fp16, [1, 188, 1024]> input_1261_cast_fp16 = add(x = input_1243_cast_fp16, y = input_1259_cast_fp16)[name = string("input_1261_cast_fp16")];
+            tensor<int32, [1]> input_1263_axes_0 = const()[name = string("input_1263_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330860288)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330862400)))];
+            tensor<fp16, [1, 188, 1024]> input_1263_cast_fp16 = layer_norm(axes = input_1263_axes_0, beta = encoder_module_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1261_cast_fp16)[name = string("input_1263_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(330864512))), scale = tensor<fp16, [4096, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332961728))))[name = string("encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333223936)))];
+            tensor<fp16, [1, 188, 4096]> linear_215_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1263_cast_fp16)[name = string("linear_215_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1267_cast_fp16 = silu(x = linear_215_cast_fp16)[name = string("input_1267_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int4, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333232192))), scale = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335329408))))[name = string("encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335591616)))];
+            tensor<fp16, [1, 188, 1024]> linear_216_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1267_cast_fp16)[name = string("linear_216_cast_fp16")];
+            fp16 var_4652_to_fp16 = const()[name = string("op_4652_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4653_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_4652_to_fp16)[name = string("op_4653_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_cast_fp16 = add(x = input_1261_cast_fp16, y = var_4653_cast_fp16)[name = string("input_cast_fp16")];
+            tensor<int32, [1]> audio_signal_axes_0 = const()[name = string("audio_signal_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335593728)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335595840)))];
+            tensor<fp16, [1, 188, 1024]> audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = encoder_module_layers_23_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = string("audio_signal_cast_fp16")];
+            tensor<int32, [3]> obj_3_perm_0 = const()[name = string("obj_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp16, [1, 1024, 188]> obj_3_cast_fp16 = transpose(perm = obj_3_perm_0, x = audio_signal_cast_fp16)[name = string("transpose_144")];
+            tensor<fp32, [1, 1024, 188]> encoder = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_0")];
+        } -> (encoder, encoder_length);
+}
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/weights/weight.bin b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..32c2114a5c996d39ff9a9f08c300049bd32c4fe8
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int4_linear/parakeet_ctc_mel_encoder.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5c0cdf923b883bc26edbea473afdfddca4c827d5633ff185221cde0ee0ef8f76
+size 335597952
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new file mode 100644
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+++ b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_decoder.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
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diff --git a/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_decoder.mlmodelc/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_decoder.mlmodelc/coremldata.bin
new file mode 100644
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+++ b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_decoder.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 463
diff --git a/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_decoder.mlmodelc/metadata.json b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_decoder.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..73a02a988e6e6a09e253f9bcdb880c5084f1f920
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_decoder.mlmodelc/metadata.json
@@ -0,0 +1,70 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "shortDescription" : "Parakeet CTC decoder head (encoder -> log_probs)",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "log_probs",
+        "type" : "MultiArray"
+      }
+    ],
+    "storagePrecision" : "Mixed (Float16, Int8)",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.transpose" : 1,
+      "Ios18.log" : 1,
+      "Ios18.cast" : 2,
+      "Ios18.conv" : 1,
+      "Ios18.softmax" : 1,
+      "Ios18.constexprBlockwiseShiftScale" : 1
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 1024 × 1...188",
+        "shapeRange" : "[[1, 1], [1024, 1024], [1, 188]]",
+        "formattedType" : "MultiArray (Float32 1 × 1024 × 1)",
+        "type" : "MultiArray",
+        "shape" : "[1, 1024, 1]",
+        "name" : "encoder",
+        "shortDescription" : ""
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-02-27",
+      "com.github.apple.coremltools.source" : "torch==2.10.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "parakeet_ctc_decoder",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_decoder.mlmodelc/model.mil b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_decoder.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..dd9a58cb16b3e8ad9c9e8af736013ec4853b95ef
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_decoder.mlmodelc/model.mil
@@ -0,0 +1,24 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 1024, ?]> encoder) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"encoder", [1, 1024, 1]}}), ("RangeDims", {{"encoder", [[1, 1], [1024, 1024], [1, 188]]}})))] {
+            int32 var_4 = const()[name = string("op_4"), val = int32(-1)];
+            string var_18_pad_type_0 = const()[name = string("op_18_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_18_strides_0 = const()[name = string("op_18_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_18_pad_0 = const()[name = string("op_18_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_18_dilations_0 = const()[name = string("op_18_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_18_groups_0 = const()[name = string("op_18_groups_0"), val = int32(1)];
+            string encoder_to_fp16_dtype_0 = const()[name = string("encoder_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1025, 1024, 1]> module_decoder_layers_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1025, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), offset = tensor<int8, [1025, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1051904))), scale = tensor<fp16, [1025, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1049728))))[name = string("module_decoder_layers_0_weight_to_fp16_quantized")];
+            tensor<fp16, [1025]> module_decoder_layers_0_bias_to_fp16 = const()[name = string("module_decoder_layers_0_bias_to_fp16"), val = tensor<fp16, [1025]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1053056)))];
+            tensor<fp16, [1, 1024, ?]> encoder_to_fp16 = cast(dtype = encoder_to_fp16_dtype_0, x = encoder)[name = string("cast_1")];
+            tensor<fp16, [1, 1025, ?]> var_18_cast_fp16 = conv(bias = module_decoder_layers_0_bias_to_fp16, dilations = var_18_dilations_0, groups = var_18_groups_0, pad = var_18_pad_0, pad_type = var_18_pad_type_0, strides = var_18_strides_0, weight = module_decoder_layers_0_weight_to_fp16_quantized, x = encoder_to_fp16)[name = string("op_18_cast_fp16")];
+            tensor<int32, [3]> input_perm_0 = const()[name = string("input_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, ?, 1025]> input_cast_fp16 = transpose(perm = input_perm_0, x = var_18_cast_fp16)[name = string("transpose_0")];
+            tensor<fp16, [1, ?, 1025]> out_objects_softmax_cast_fp16 = softmax(axis = var_4, x = input_cast_fp16)[name = string("out_objects_softmax_cast_fp16")];
+            fp32 out_objects_epsilon_0 = const()[name = string("out_objects_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [1, ?, 1025]> out_objects_cast_fp16 = log(epsilon = out_objects_epsilon_0, x = out_objects_softmax_cast_fp16)[name = string("out_objects_cast_fp16")];
+            string out_objects_cast_fp16_to_fp32_dtype_0 = const()[name = string("out_objects_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [1, ?, 1025]> log_probs = cast(dtype = out_objects_cast_fp16_to_fp32_dtype_0, x = out_objects_cast_fp16)[name = string("cast_0")];
+        } -> (log_probs);
+}
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_decoder.mlmodelc/weights/weight.bin b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_decoder.mlmodelc/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..d975e03ea5aa1f6d9d6207c98a16bc58dd19cf3a
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_decoder.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a51b1b7459ddae4323b821ec75cbecc339867372f60602bb1e32d546a0817bf6
+size 1055170
diff --git a/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..a4d59585db22b27feba9d147de7f42ee42de08b8
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+++ b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 243
diff --git a/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..4c3d92beae3031e47a03c2adca9262da4c6d133b
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84828b5ad1820337049ab436c70bf66017b30153e26774dd986866e769b5a5b6
+size 496
diff --git a/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlmodelc/metadata.json b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..16229585aad50955bc81218e467cd3adf0b4b877
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlmodelc/metadata.json
@@ -0,0 +1,117 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "shortDescription" : "Parakeet CTC Mel+Encoder (15.0s window)",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 1024 × 188)",
+        "shortDescription" : "",
+        "shape" : "[1, 1024, 188]",
+        "name" : "encoder",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "encoder_length",
+        "type" : "MultiArray"
+      }
+    ],
+    "storagePrecision" : "Mixed (Float16, Int32, Int8)",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.constexprBlockwiseShiftScale" : 298,
+      "Stack" : 1,
+      "Ios18.conv" : 79,
+      "Ios18.mul" : 107,
+      "Ios18.cast" : 12,
+      "Ios18.layerNorm" : 120,
+      "Ios18.log" : 1,
+      "Ios18.equal" : 1,
+      "Ios18.floorDiv" : 4,
+      "Ios16.reduceSum" : 4,
+      "Ios18.logicalNot" : 3,
+      "Ios18.reshape" : 147,
+      "Pad" : 49,
+      "Ios18.concat" : 1,
+      "Ios18.add" : 180,
+      "Ios18.realDiv" : 3,
+      "Ios18.relu" : 3,
+      "Ios18.greaterEqual" : 1,
+      "Ios18.notEqual" : 1,
+      "Ios18.sub" : 7,
+      "Ios18.matmul" : 73,
+      "Ios18.silu" : 72,
+      "Ios18.expandDims" : 25,
+      "Ios18.linear" : 193,
+      "Ios18.sigmoid" : 24,
+      "Ios18.sliceByIndex" : 51,
+      "Ios18.transpose" : 172,
+      "Split" : 24,
+      "Ios18.sqrt" : 1,
+      "Ios18.softmax" : 24,
+      "Ios18.pow" : 2,
+      "Select" : 78,
+      "Ios18.logicalAnd" : 2,
+      "Ios18.less" : 6,
+      "Tile" : 9
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 240000)",
+        "shortDescription" : "",
+        "shape" : "[1, 240000]",
+        "name" : "audio_signal",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "audio_length",
+        "type" : "MultiArray"
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-02-27",
+      "com.github.apple.coremltools.source" : "torch==2.10.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "parakeet_ctc_mel_encoder",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlmodelc/model.mil b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..a470967905cfd3ef4b38ef5e1d687f7a8c2d1149
--- /dev/null
+++ b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlmodelc/model.mil
@@ -0,0 +1,3838 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}})]
+{
+    func main<ios18>(tensor<int32, [1]> audio_length, tensor<fp32, [1, 240000]> audio_signal) {
+            int32 var_20 = const()[name = string("op_20"), val = int32(0)];
+            int32 var_21 = const()[name = string("op_21"), val = int32(160)];
+            int32 var_22 = const()[name = string("op_22"), val = int32(1)];
+            int32 var_32 = const()[name = string("op_32"), val = int32(512)];
+            tensor<int32, [1]> var_33 = add(x = audio_length, y = var_32)[name = string("op_33")];
+            int32 var_34 = const()[name = string("op_34"), val = int32(512)];
+            tensor<int32, [1]> var_35 = sub(x = var_33, y = var_34)[name = string("op_35")];
+            tensor<int32, [1]> floor_div_0 = floor_div(x = var_35, y = var_21)[name = string("floor_div_0")];
+            tensor<bool, [1]> var_38 = equal(x = audio_length, y = var_20)[name = string("op_38")];
+            tensor<int32, [1]> var_39 = const()[name = string("op_39"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1]> seq_len = select(a = var_39, b = floor_div_0, cond = var_38)[name = string("seq_len")];
+            tensor<int32, [1, 240000]> var_43 = const()[name = string("op_43"), val = tensor<int32, [1, 240000]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<int32, [1]> var_44_axes_0 = const()[name = string("op_44_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1]> var_44 = expand_dims(axes = var_44_axes_0, x = audio_length)[name = string("op_44")];
+            tensor<bool, [1, 240000]> timemask = less(x = var_43, y = var_44)[name = string("timemask")];
+            tensor<int32, [2]> var_47_begin_0 = const()[name = string("op_47_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_47_end_0 = const()[name = string("op_47_end_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<bool, [2]> var_47_end_mask_0 = const()[name = string("op_47_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<bool, [2]> var_47_squeeze_mask_0 = const()[name = string("op_47_squeeze_mask_0"), val = tensor<bool, [2]>([false, true])];
+            string audio_signal_to_fp16_dtype_0 = const()[name = string("audio_signal_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 240000]> audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = string("cast_11")];
+            tensor<fp16, [1]> var_47_cast_fp16 = slice_by_index(begin = var_47_begin_0, end = var_47_end_0, end_mask = var_47_end_mask_0, squeeze_mask = var_47_squeeze_mask_0, x = audio_signal_to_fp16)[name = string("op_47_cast_fp16")];
+            tensor<int32, [1]> var_48_axes_0 = const()[name = string("op_48_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_48_cast_fp16 = expand_dims(axes = var_48_axes_0, x = var_47_cast_fp16)[name = string("op_48_cast_fp16")];
+            tensor<int32, [2]> var_50_begin_0 = const()[name = string("op_50_begin_0"), val = tensor<int32, [2]>([0, 1])];
+            tensor<int32, [2]> var_50_end_0 = const()[name = string("op_50_end_0"), val = tensor<int32, [2]>([1, 240000])];
+            tensor<bool, [2]> var_50_end_mask_0 = const()[name = string("op_50_end_mask_0"), val = tensor<bool, [2]>([true, true])];
+            tensor<fp16, [1, 239999]> var_50_cast_fp16 = slice_by_index(begin = var_50_begin_0, end = var_50_end_0, end_mask = var_50_end_mask_0, x = audio_signal_to_fp16)[name = string("op_50_cast_fp16")];
+            tensor<int32, [2]> var_52_begin_0 = const()[name = string("op_52_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_52_end_0 = const()[name = string("op_52_end_0"), val = tensor<int32, [2]>([1, 239999])];
+            tensor<bool, [2]> var_52_end_mask_0 = const()[name = string("op_52_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<fp16, [1, 239999]> var_52_cast_fp16 = slice_by_index(begin = var_52_begin_0, end = var_52_end_0, end_mask = var_52_end_mask_0, x = audio_signal_to_fp16)[name = string("op_52_cast_fp16")];
+            fp16 var_53_to_fp16 = const()[name = string("op_53_to_fp16"), val = fp16(0x1.f0cp-1)];
+            tensor<fp16, [1, 239999]> var_54_cast_fp16 = mul(x = var_52_cast_fp16, y = var_53_to_fp16)[name = string("op_54_cast_fp16")];
+            tensor<fp16, [1, 239999]> var_55_cast_fp16 = sub(x = var_50_cast_fp16, y = var_54_cast_fp16)[name = string("op_55_cast_fp16")];
+            bool x_3_interleave_0 = const()[name = string("x_3_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 240000]> x_3_cast_fp16 = concat(axis = var_22, interleave = x_3_interleave_0, values = (var_48_cast_fp16, var_55_cast_fp16))[name = string("x_3_cast_fp16")];
+            tensor<bool, [1, 240000]> var_58 = logical_not(x = timemask)[name = string("op_58")];
+            fp16 var_16_to_fp16 = const()[name = string("op_16_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 240000]> input_1_cast_fp16 = select(a = var_16_to_fp16, b = x_3_cast_fp16, cond = var_58)[name = string("input_1_cast_fp16")];
+            tensor<int32, [3]> var_63 = const()[name = string("op_63"), val = tensor<int32, [3]>([1, 1, 240000])];
+            tensor<fp16, [1, 1, 240000]> input_3_cast_fp16 = reshape(shape = var_63, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")];
+            tensor<int32, [6]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 256])];
+            string input_5_mode_0 = const()[name = string("input_5_mode_0"), val = string("constant")];
+            fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1, 240512]> input_5_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")];
+            tensor<int32, [2]> var_69 = const()[name = string("op_69"), val = tensor<int32, [2]>([1, 240512])];
+            tensor<fp16, [1, 240512]> input_7_cast_fp16 = reshape(shape = var_69, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")];
+            tensor<int32, [1]> expand_dims_10 = const()[name = string("expand_dims_10"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [1]> expand_dims_11_axes_0 = const()[name = string("expand_dims_11_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 240512]> expand_dims_11_cast_fp16 = expand_dims(axes = expand_dims_11_axes_0, x = input_7_cast_fp16)[name = string("expand_dims_11_cast_fp16")];
+            string conv_0_pad_type_0 = const()[name = string("conv_0_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv_0_pad_0 = const()[name = string("conv_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_0_dilations_0 = const()[name = string("conv_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 conv_0_groups_0 = const()[name = string("conv_0_groups_0"), val = int32(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_8_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [257, 1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(960128))), offset = tensor<int8, [257, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1092416))), scale = tensor<fp16, [257, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1091776))))[name = string("expand_dims_8_to_fp16_quantized")];
+            tensor<fp16, [1, 257, 1501]> 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_10, weight = expand_dims_8_to_fp16_quantized, x = expand_dims_11_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<int32, [2]> conv_1_pad_0 = const()[name = string("conv_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_1_dilations_0 = const()[name = string("conv_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 conv_1_groups_0 = const()[name = string("conv_1_groups_0"), val = int32(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_9_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [257, 1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1092800))), offset = tensor<int8, [257, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1225088))), scale = tensor<fp16, [257, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1224448))))[name = string("expand_dims_9_to_fp16_quantized")];
+            tensor<fp16, [1, 257, 1501]> 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_10, weight = expand_dims_9_to_fp16_quantized, x = expand_dims_11_cast_fp16)[name = string("conv_1_cast_fp16")];
+            int32 stack_0_axis_0 = const()[name = string("stack_0_axis_0"), val = int32(-1)];
+            tensor<fp16, [1, 257, 1501, 2]> 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_13_promoted_to_fp16 = const()[name = string("op_13_promoted_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1, 257, 1501, 2]> var_73_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_13_promoted_to_fp16)[name = string("op_73_cast_fp16")];
+            tensor<int32, [1]> var_75_axes_0 = const()[name = string("op_75_axes_0"), val = tensor<int32, [1]>([-1])];
+            bool var_75_keep_dims_0 = const()[name = string("op_75_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 257, 1501]> var_75_cast_fp16 = reduce_sum(axes = var_75_axes_0, keep_dims = var_75_keep_dims_0, x = var_73_cast_fp16)[name = string("op_75_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<fp16, [1, 80, 257]> const_9_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 80, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1225472))), offset = tensor<int8, [1, 80, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1246400))), scale = tensor<fp16, [1, 80, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1246144))))[name = string("const_9_to_fp16_quantized")];
+            tensor<fp16, [1, 80, 1501]> x_13_cast_fp16 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = const_9_to_fp16_quantized, y = var_75_cast_fp16)[name = string("x_13_cast_fp16")];
+            fp16 var_82_to_fp16 = const()[name = string("op_82_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [1, 80, 1501]> var_83_cast_fp16 = add(x = x_13_cast_fp16, y = var_82_to_fp16)[name = string("op_83_cast_fp16")];
+            fp32 x_15_epsilon_0 = const()[name = string("x_15_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [1, 80, 1501]> x_15_cast_fp16 = log(epsilon = x_15_epsilon_0, x = var_83_cast_fp16)[name = string("x_15_cast_fp16")];
+            tensor<int32, [1, 1501]> var_88 = const()[name = string("op_88"), val = tensor<int32, [1, 1501]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1246592)))];
+            tensor<int32, [1]> var_91_axes_0 = const()[name = string("op_91_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1]> var_91 = expand_dims(axes = var_91_axes_0, x = seq_len)[name = string("op_91")];
+            tensor<bool, [1, 1501]> valid_mask = less(x = var_88, y = var_91)[name = string("valid_mask")];
+            tensor<int32, [1]> var_93_axes_0 = const()[name = string("op_93_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_93 = expand_dims(axes = var_93_axes_0, x = valid_mask)[name = string("op_93")];
+            tensor<int32, [3]> var_93_after_broadcast_reps_0 = const()[name = string("op_93_after_broadcast_reps_0"), val = tensor<int32, [3]>([1, 80, 1])];
+            tensor<bool, [1, 80, 1501]> var_93_after_broadcast = tile(reps = var_93_after_broadcast_reps_0, x = var_93)[name = string("op_93_after_broadcast")];
+            tensor<fp16, [1, 80, 1501]> op_16_after_broadcast_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 80, 1501]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1252672))), offset = tensor<int8, [1, 1, 1]>([[[0]]]), scale = tensor<fp16, [1, 1, 1]>([[[0x0p+0]]]))[name = string("op_16_after_broadcast_to_fp16_quantized")];
+            tensor<fp16, [1, 80, 1501]> var_94_cast_fp16 = select(a = x_15_cast_fp16, b = op_16_after_broadcast_to_fp16_quantized, cond = var_93_after_broadcast)[name = string("op_94_cast_fp16")];
+            tensor<int32, [1]> x_mean_numerator_axes_0 = const()[name = string("x_mean_numerator_axes_0"), val = tensor<int32, [1]>([2])];
+            bool x_mean_numerator_keep_dims_0 = const()[name = string("x_mean_numerator_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 80]> x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_94_cast_fp16)[name = string("x_mean_numerator_cast_fp16")];
+            tensor<int32, [1]> x_mean_denominator_axes_0 = const()[name = string("x_mean_denominator_axes_0"), val = tensor<int32, [1]>([1])];
+            bool x_mean_denominator_keep_dims_0 = const()[name = string("x_mean_denominator_keep_dims_0"), val = bool(false)];
+            string cast_2_to_fp16_dtype_0 = const()[name = string("cast_2_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 1501]> valid_mask_to_fp16 = cast(dtype = cast_2_to_fp16_dtype_0, x = valid_mask)[name = string("cast_10")];
+            tensor<fp16, [1]> x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = string("x_mean_denominator_cast_fp16")];
+            tensor<int32, [1]> var_99_axes_0 = const()[name = string("op_99_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_99_cast_fp16 = expand_dims(axes = var_99_axes_0, x = x_mean_denominator_cast_fp16)[name = string("op_99_cast_fp16")];
+            tensor<fp16, [1, 80]> x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_99_cast_fp16)[name = string("x_mean_cast_fp16")];
+            tensor<int32, [1]> var_102_axes_0 = const()[name = string("op_102_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 80, 1]> var_102_cast_fp16 = expand_dims(axes = var_102_axes_0, x = x_mean_cast_fp16)[name = string("op_102_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> var_103_cast_fp16 = sub(x = x_15_cast_fp16, y = var_102_cast_fp16)[name = string("op_103_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> var_104_cast_fp16 = select(a = var_103_cast_fp16, b = op_16_after_broadcast_to_fp16_quantized, cond = var_93_after_broadcast)[name = string("op_104_cast_fp16")];
+            fp16 var_13_promoted_1_to_fp16 = const()[name = string("op_13_promoted_1_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1, 80, 1501]> var_105_cast_fp16 = pow(x = var_104_cast_fp16, y = var_13_promoted_1_to_fp16)[name = string("op_105_cast_fp16")];
+            tensor<int32, [1]> var_107_axes_0 = const()[name = string("op_107_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_107_keep_dims_0 = const()[name = string("op_107_keep_dims_0"), val = bool(false)];
+            tensor<fp16, [1, 80]> var_107_cast_fp16 = reduce_sum(axes = var_107_axes_0, keep_dims = var_107_keep_dims_0, x = var_105_cast_fp16)[name = string("op_107_cast_fp16")];
+            fp16 var_109_to_fp16 = const()[name = string("op_109_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1]> var_110_cast_fp16 = sub(x = var_99_cast_fp16, y = var_109_to_fp16)[name = string("op_110_cast_fp16")];
+            tensor<fp16, [1, 80]> var_111_cast_fp16 = real_div(x = var_107_cast_fp16, y = var_110_cast_fp16)[name = string("op_111_cast_fp16")];
+            tensor<fp16, [1, 80]> x_std_1_cast_fp16 = sqrt(x = var_111_cast_fp16)[name = string("x_std_1_cast_fp16")];
+            tensor<bool, [1, 80]> var_113_cast_fp16 = not_equal(x = x_std_1_cast_fp16, y = x_std_1_cast_fp16)[name = string("op_113_cast_fp16")];
+            tensor<fp16, [1, 80]> x_std_3_cast_fp16 = select(a = var_16_to_fp16, b = x_std_1_cast_fp16, cond = var_113_cast_fp16)[name = string("x_std_3_cast_fp16")];
+            fp16 var_7_to_fp16 = const()[name = string("op_7_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 80]> x_std_cast_fp16 = add(x = x_std_3_cast_fp16, y = var_7_to_fp16)[name = string("x_std_cast_fp16")];
+            tensor<int32, [1]> var_118_axes_0 = const()[name = string("op_118_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 80, 1]> var_118_cast_fp16 = expand_dims(axes = var_118_axes_0, x = x_std_cast_fp16)[name = string("op_118_cast_fp16")];
+            tensor<fp16, [1, 80, 1501]> x_17_cast_fp16 = real_div(x = var_103_cast_fp16, y = var_118_cast_fp16)[name = string("x_17_cast_fp16")];
+            tensor<bool, [1, 1501]> mask_3 = greater_equal(x = var_88, y = var_91)[name = string("mask_3")];
+            tensor<int32, [1]> var_127_axes_0 = const()[name = string("op_127_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_127 = expand_dims(axes = var_127_axes_0, x = mask_3)[name = string("op_127")];
+            tensor<fp16, [1, 80, 1501]> processed_signal_cast_fp16 = select(a = var_16_to_fp16, b = x_17_cast_fp16, cond = var_127)[name = string("processed_signal_cast_fp16")];
+            int32 var_152 = const()[name = string("op_152"), val = int32(-1)];
+            tensor<int32, [3]> x_19_perm_0 = const()[name = string("x_19_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> tensor_1_axes_0 = const()[name = string("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1501, 80]> x_19_cast_fp16 = transpose(perm = x_19_perm_0, x = processed_signal_cast_fp16)[name = string("transpose_315")];
+            tensor<fp16, [1, 1, 1501, 80]> tensor_1_cast_fp16 = expand_dims(axes = tensor_1_axes_0, x = x_19_cast_fp16)[name = string("tensor_1_cast_fp16")];
+            tensor<int32, [1]> var_242_axes_0 = const()[name = string("op_242_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 1501, 1]> var_242 = expand_dims(axes = var_242_axes_0, x = valid_mask)[name = string("op_242")];
+            tensor<int32, [3]> var_244_reps_0 = const()[name = string("op_244_reps_0"), val = tensor<int32, [3]>([1, 1, 80])];
+            tensor<bool, [1, 1501, 80]> var_244 = tile(reps = var_244_reps_0, x = var_242)[name = string("op_244")];
+            tensor<int32, [1]> var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_5_to_fp16_dtype_0 = const()[name = string("mask_5_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 1501, 80]> var_244_to_fp16 = cast(dtype = mask_5_to_fp16_dtype_0, x = var_244)[name = string("cast_9")];
+            tensor<fp16, [1, 1, 1501, 80]> var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = var_244_to_fp16)[name = string("op_250_cast_fp16")];
+            tensor<fp16, [1, 1, 1501, 80]> input_9_cast_fp16 = mul(x = tensor_1_cast_fp16, y = var_250_cast_fp16)[name = string("input_9_cast_fp16")];
+            string tensor_3_pad_type_0 = const()[name = string("tensor_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_3_pad_0 = const()[name = string("tensor_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_3_strides_0 = const()[name = string("tensor_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> tensor_3_dilations_0 = const()[name = string("tensor_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_3_groups_0 = const()[name = string("tensor_3_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372864))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1375808))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1375232))))[name = string("encoder_module_pre_encode_conv_0_weight_to_fp16_quantized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_0_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1376128)))];
+            tensor<fp16, [1, 256, 751, 40]> tensor_3_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_0_weight_to_fp16_quantized, x = input_9_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_261_promoted_to_fp16 = const()[name = string("op_261_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> seq_len_to_fp16 = cast(dtype = current_lengths_1_to_fp16_dtype_0, x = seq_len)[name = string("cast_8")];
+            tensor<fp16, [1]> var_262_cast_fp16 = add(x = seq_len_to_fp16, y = var_261_promoted_to_fp16)[name = string("op_262_cast_fp16")];
+            fp16 var_263_promoted_to_fp16 = const()[name = string("op_263_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_264_cast_fp16 = add(x = var_262_cast_fp16, y = var_263_promoted_to_fp16)[name = string("op_264_cast_fp16")];
+            fp16 var_265_promoted_to_fp16 = const()[name = string("op_265_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_266_cast_fp16 = sub(x = var_264_cast_fp16, y = var_265_promoted_to_fp16)[name = string("op_266_cast_fp16")];
+            fp16 var_154_promoted_to_fp16 = const()[name = string("op_154_promoted_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_1_cast_fp16 = floor_div(x = var_266_cast_fp16, y = var_154_promoted_to_fp16)[name = string("floor_div_1_cast_fp16")];
+            fp16 var_268_promoted_to_fp16 = const()[name = string("op_268_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_3_cast_fp16 = add(x = floor_div_1_cast_fp16, y = var_268_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<int32, [1, 751]> expand_dims_4 = const()[name = string("expand_dims_4"), val = tensor<int32, [1, 751]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1376704)))];
+            tensor<int32, [1]> var_277_axes_0 = const()[name = string("op_277_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_3_cast_fp16_to_int32 = cast(dtype = lengths_19_dtype_0, x = current_lengths_3_cast_fp16)[name = string("cast_7")];
+            tensor<int32, [1, 1]> var_277 = expand_dims(axes = var_277_axes_0, x = current_lengths_3_cast_fp16_to_int32)[name = string("op_277")];
+            tensor<bool, [1, 751]> time_mask_3 = less(x = expand_dims_4, y = var_277)[name = string("time_mask_3")];
+            tensor<int32, [1]> var_279_axes_0 = const()[name = string("op_279_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 751, 1]> var_279 = expand_dims(axes = var_279_axes_0, x = time_mask_3)[name = string("op_279")];
+            tensor<int32, [3]> var_281_reps_0 = const()[name = string("op_281_reps_0"), val = tensor<int32, [3]>([1, 1, 40])];
+            tensor<bool, [1, 751, 40]> var_281 = tile(reps = var_281_reps_0, x = var_279)[name = string("op_281")];
+            tensor<int32, [1]> var_287_axes_0 = const()[name = string("op_287_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_7_to_fp16_dtype_0 = const()[name = string("mask_7_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 751, 40]> var_281_to_fp16 = cast(dtype = mask_7_to_fp16_dtype_0, x = var_281)[name = string("cast_6")];
+            tensor<fp16, [1, 1, 751, 40]> var_287_cast_fp16 = expand_dims(axes = var_287_axes_0, x = var_281_to_fp16)[name = string("op_287_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_3_reps_0 = const()[name = string("expanded_mask_3_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 751, 40]> expanded_mask_3_cast_fp16 = tile(reps = expanded_mask_3_reps_0, x = var_287_cast_fp16)[name = string("expanded_mask_3_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> input_11_cast_fp16 = mul(x = tensor_3_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_11_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> tensor_5_cast_fp16 = relu(x = input_11_cast_fp16)[name = string("tensor_5_cast_fp16")];
+            tensor<fp16, [1, 256, 751, 40]> input_13_cast_fp16 = mul(x = tensor_5_cast_fp16, y = expanded_mask_3_cast_fp16)[name = string("input_13_cast_fp16")];
+            string tensor_7_pad_type_0 = const()[name = string("tensor_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_7_pad_0 = const()[name = string("tensor_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_7_strides_0 = const()[name = string("tensor_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 tensor_7_groups_0 = const()[name = string("tensor_7_groups_0"), val = int32(256)];
+            tensor<int32, [2]> tensor_7_dilations_0 = const()[name = string("tensor_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1379776))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1382720))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1382144))))[name = string("encoder_module_pre_encode_conv_2_weight_to_fp16_quantized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_2_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1383040)))];
+            tensor<fp16, [1, 256, 376, 20]> tensor_7_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_2_weight_to_fp16_quantized, x = input_13_cast_fp16)[name = string("tensor_7_cast_fp16")];
+            fp16 var_307_promoted_to_fp16 = const()[name = string("op_307_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_308_cast_fp16 = add(x = current_lengths_3_cast_fp16, y = var_307_promoted_to_fp16)[name = string("op_308_cast_fp16")];
+            fp16 var_309_promoted_to_fp16 = const()[name = string("op_309_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_310_cast_fp16 = add(x = var_308_cast_fp16, y = var_309_promoted_to_fp16)[name = string("op_310_cast_fp16")];
+            fp16 var_311_promoted_to_fp16 = const()[name = string("op_311_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_312_cast_fp16 = sub(x = var_310_cast_fp16, y = var_311_promoted_to_fp16)[name = string("op_312_cast_fp16")];
+            fp16 var_154_promoted_1_to_fp16 = const()[name = string("op_154_promoted_1_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_2_cast_fp16 = floor_div(x = var_312_cast_fp16, y = var_154_promoted_1_to_fp16)[name = string("floor_div_2_cast_fp16")];
+            fp16 var_314_promoted_to_fp16 = const()[name = string("op_314_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_5_cast_fp16 = add(x = floor_div_2_cast_fp16, y = var_314_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<int32, [1, 376]> expand_dims_5 = const()[name = string("expand_dims_5"), val = tensor<int32, [1, 376]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1383616)))];
+            tensor<int32, [1]> var_323_axes_0 = const()[name = string("op_323_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_5_cast_fp16_to_int32 = cast(dtype = lengths_21_dtype_0, x = current_lengths_5_cast_fp16)[name = string("cast_5")];
+            tensor<int32, [1, 1]> var_323 = expand_dims(axes = var_323_axes_0, x = current_lengths_5_cast_fp16_to_int32)[name = string("op_323")];
+            tensor<bool, [1, 376]> time_mask_5 = less(x = expand_dims_5, y = var_323)[name = string("time_mask_5")];
+            tensor<int32, [1]> var_325_axes_0 = const()[name = string("op_325_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 376, 1]> var_325 = expand_dims(axes = var_325_axes_0, x = time_mask_5)[name = string("op_325")];
+            tensor<int32, [3]> var_327_reps_0 = const()[name = string("op_327_reps_0"), val = tensor<int32, [3]>([1, 1, 20])];
+            tensor<bool, [1, 376, 20]> var_327 = tile(reps = var_327_reps_0, x = var_325)[name = string("op_327")];
+            tensor<int32, [1]> var_333_axes_0 = const()[name = string("op_333_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_9_to_fp16_dtype_0 = const()[name = string("mask_9_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 376, 20]> var_327_to_fp16 = cast(dtype = mask_9_to_fp16_dtype_0, x = var_327)[name = string("cast_4")];
+            tensor<fp16, [1, 1, 376, 20]> var_333_cast_fp16 = expand_dims(axes = var_333_axes_0, x = var_327_to_fp16)[name = string("op_333_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_7_reps_0 = const()[name = string("expanded_mask_7_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 376, 20]> expanded_mask_7_cast_fp16 = tile(reps = expanded_mask_7_reps_0, x = var_333_cast_fp16)[name = string("expanded_mask_7_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_15_cast_fp16 = mul(x = tensor_7_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_15_cast_fp16")];
+            string tensor_9_pad_type_0 = const()[name = string("tensor_9_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> tensor_9_strides_0 = const()[name = string("tensor_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> tensor_9_pad_0 = const()[name = string("tensor_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> tensor_9_dilations_0 = const()[name = string("tensor_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_9_groups_0 = const()[name = string("tensor_9_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 1, 1]> encoder_module_pre_encode_conv_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1385216))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1451392))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1450816))))[name = string("encoder_module_pre_encode_conv_3_weight_to_fp16_quantized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_3_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_3_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1451712)))];
+            tensor<fp16, [1, 256, 376, 20]> tensor_9_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_3_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("tensor_9_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_17_cast_fp16 = mul(x = tensor_9_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_17_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> tensor_11_cast_fp16 = relu(x = input_17_cast_fp16)[name = string("tensor_11_cast_fp16")];
+            tensor<fp16, [1, 256, 376, 20]> input_19_cast_fp16 = mul(x = tensor_11_cast_fp16, y = expanded_mask_7_cast_fp16)[name = string("input_19_cast_fp16")];
+            string tensor_13_pad_type_0 = const()[name = string("tensor_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> tensor_13_pad_0 = const()[name = string("tensor_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> tensor_13_strides_0 = const()[name = string("tensor_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 tensor_13_groups_0 = const()[name = string("tensor_13_groups_0"), val = int32(256)];
+            tensor<int32, [2]> tensor_13_dilations_0 = const()[name = string("tensor_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> encoder_module_pre_encode_conv_5_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1452288))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1455232))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1454656))))[name = string("encoder_module_pre_encode_conv_5_weight_to_fp16_quantized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_5_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_5_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1455552)))];
+            tensor<fp16, [1, 256, 188, 10]> tensor_13_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_5_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("tensor_13_cast_fp16")];
+            fp16 var_368_promoted_to_fp16 = const()[name = string("op_368_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_369_cast_fp16 = add(x = current_lengths_5_cast_fp16, y = var_368_promoted_to_fp16)[name = string("op_369_cast_fp16")];
+            fp16 var_370_promoted_to_fp16 = const()[name = string("op_370_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> var_371_cast_fp16 = add(x = var_369_cast_fp16, y = var_370_promoted_to_fp16)[name = string("op_371_cast_fp16")];
+            fp16 var_372_promoted_to_fp16 = const()[name = string("op_372_promoted_to_fp16"), val = fp16(0x1.8p+1)];
+            tensor<fp16, [1]> var_373_cast_fp16 = sub(x = var_371_cast_fp16, y = var_372_promoted_to_fp16)[name = string("op_373_cast_fp16")];
+            fp16 var_154_promoted_2_to_fp16 = const()[name = string("op_154_promoted_2_to_fp16"), val = fp16(0x1p+1)];
+            tensor<fp16, [1]> floor_div_3_cast_fp16 = floor_div(x = var_373_cast_fp16, y = var_154_promoted_2_to_fp16)[name = string("floor_div_3_cast_fp16")];
+            fp16 var_375_promoted_to_fp16 = const()[name = string("op_375_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1]> current_lengths_cast_fp16 = add(x = floor_div_3_cast_fp16, y = var_375_promoted_to_fp16)[name = string("current_lengths_cast_fp16")];
+            string lengths_dtype_0 = const()[name = string("lengths_dtype_0"), val = string("int32")];
+            tensor<int32, [1, 188]> expand_dims_6 = const()[name = string("expand_dims_6"), val = tensor<int32, [1, 188]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1456128)))];
+            tensor<int32, [1]> var_384_axes_0 = const()[name = string("op_384_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> current_lengths_cast_fp16_to_int32 = cast(dtype = lengths_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_3")];
+            tensor<int32, [1, 1]> var_384 = expand_dims(axes = var_384_axes_0, x = current_lengths_cast_fp16_to_int32)[name = string("op_384")];
+            tensor<bool, [1, 188]> time_mask = less(x = expand_dims_6, y = var_384)[name = string("time_mask")];
+            tensor<int32, [1]> var_386_axes_0 = const()[name = string("op_386_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, 188, 1]> var_386 = expand_dims(axes = var_386_axes_0, x = time_mask)[name = string("op_386")];
+            tensor<int32, [3]> var_388_reps_0 = const()[name = string("op_388_reps_0"), val = tensor<int32, [3]>([1, 1, 10])];
+            tensor<bool, [1, 188, 10]> var_388 = tile(reps = var_388_reps_0, x = var_386)[name = string("op_388")];
+            tensor<int32, [1]> var_394_axes_0 = const()[name = string("op_394_axes_0"), val = tensor<int32, [1]>([1])];
+            string mask_11_to_fp16_dtype_0 = const()[name = string("mask_11_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 188, 10]> var_388_to_fp16 = cast(dtype = mask_11_to_fp16_dtype_0, x = var_388)[name = string("cast_2")];
+            tensor<fp16, [1, 1, 188, 10]> var_394_cast_fp16 = expand_dims(axes = var_394_axes_0, x = var_388_to_fp16)[name = string("op_394_cast_fp16")];
+            tensor<int32, [4]> expanded_mask_13_reps_0 = const()[name = string("expanded_mask_13_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
+            tensor<fp16, [1, 256, 188, 10]> expanded_mask_13_cast_fp16 = tile(reps = expanded_mask_13_reps_0, x = var_394_cast_fp16)[name = string("expanded_mask_13_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> input_21_cast_fp16 = mul(x = tensor_13_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_21_cast_fp16")];
+            string tensor_15_pad_type_0 = const()[name = string("tensor_15_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> tensor_15_strides_0 = const()[name = string("tensor_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> tensor_15_pad_0 = const()[name = string("tensor_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> tensor_15_dilations_0 = const()[name = string("tensor_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 tensor_15_groups_0 = const()[name = string("tensor_15_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 1, 1]> encoder_module_pre_encode_conv_6_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1456960))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1523136))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1522560))))[name = string("encoder_module_pre_encode_conv_6_weight_to_fp16_quantized")];
+            tensor<fp16, [256]> encoder_module_pre_encode_conv_6_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_conv_6_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1523456)))];
+            tensor<fp16, [1, 256, 188, 10]> tensor_15_cast_fp16 = conv(bias = encoder_module_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_module_pre_encode_conv_6_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("tensor_15_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> input_23_cast_fp16 = mul(x = tensor_15_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("input_23_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> tensor_cast_fp16 = relu(x = input_23_cast_fp16)[name = string("tensor_cast_fp16")];
+            tensor<fp16, [1, 256, 188, 10]> x_21_cast_fp16 = mul(x = tensor_cast_fp16, y = expanded_mask_13_cast_fp16)[name = string("x_21_cast_fp16")];
+            tensor<int32, [4]> var_428_perm_0 = const()[name = string("op_428_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_429 = const()[name = string("op_429"), val = tensor<int32, [3]>([1, 188, -1])];
+            tensor<fp16, [1, 188, 256, 10]> var_428_cast_fp16 = transpose(perm = var_428_perm_0, x = x_21_cast_fp16)[name = string("transpose_314")];
+            tensor<fp16, [1, 188, 2560]> input_25_cast_fp16 = reshape(shape = var_429, x = var_428_cast_fp16)[name = string("input_25_cast_fp16")];
+            tensor<fp16, [1024, 2560]> encoder_module_pre_encode_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1524032))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4147648))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4145536))))[name = string("encoder_module_pre_encode_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_pre_encode_out_bias_to_fp16 = const()[name = string("encoder_module_pre_encode_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4148736)))];
+            tensor<fp16, [1, 188, 1024]> linear_0_cast_fp16 = linear(bias = encoder_module_pre_encode_out_bias_to_fp16, weight = encoder_module_pre_encode_out_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_0_cast_fp16")];
+            string padding_length_dtype_0 = const()[name = string("padding_length_dtype_0"), val = string("int32")];
+            fp16 var_440_to_fp16 = const()[name = string("op_440_to_fp16"), val = fp16(0x1p+5)];
+            tensor<fp16, [1, 188, 1024]> x_23_cast_fp16 = mul(x = linear_0_cast_fp16, y = var_440_to_fp16)[name = string("x_23_cast_fp16")];
+            tensor<int32, [1]> var_469_axes_0 = const()[name = string("op_469_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<int32, [1]> encoder_length = cast(dtype = padding_length_dtype_0, x = current_lengths_cast_fp16)[name = string("cast_1")];
+            tensor<int32, [1, 1]> var_469 = expand_dims(axes = var_469_axes_0, x = encoder_length)[name = string("op_469")];
+            tensor<bool, [1, 188]> pad_mask_1 = less(x = expand_dims_6, y = var_469)[name = string("pad_mask_1")];
+            tensor<int32, [1]> var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188]> var_471 = expand_dims(axes = var_471_axes_0, x = pad_mask_1)[name = string("op_471")];
+            tensor<int32, [3]> var_472 = const()[name = string("op_472"), val = tensor<int32, [3]>([1, 188, 1])];
+            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask_1 = tile(reps = var_472, x = var_471)[name = string("pad_mask_for_att_mask_1")];
+            tensor<int32, [3]> var_474_perm_0 = const()[name = string("op_474_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<bool, [1, 188, 188]> var_474 = transpose(perm = var_474_perm_0, x = pad_mask_for_att_mask_1)[name = string("transpose_313")];
+            tensor<bool, [1, 188, 188]> pad_mask_for_att_mask = logical_and(x = pad_mask_for_att_mask_1, y = var_474)[name = string("pad_mask_for_att_mask")];
+            tensor<bool, [1, 188, 188]> const_81 = const()[name = string("const_81"), val = tensor<bool, [1, 188, 188]>([[[true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true], [true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, true, 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true, true, true, true, true, true]]])];
+            tensor<bool, [1, 188, 188]> att_mask = logical_and(x = pad_mask_for_att_mask, y = const_81)[name = string("att_mask")];
+            tensor<bool, [1, 188, 188]> mask_13 = logical_not(x = att_mask)[name = string("mask_13")];
+            tensor<bool, [1, 188]> pad_mask = logical_not(x = pad_mask_1)[name = string("pad_mask")];
+            tensor<int32, [1]> input_29_axes_0 = const()[name = string("input_29_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4150848)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4152960)))];
+            fp16 var_166_to_fp16 = const()[name = string("op_166_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 188, 1024]> input_29_cast_fp16 = layer_norm(axes = input_29_axes_0, beta = encoder_module_layers_0_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_feed_forward1_weight_to_fp16, x = x_23_cast_fp16)[name = string("input_29_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4155072))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8357696))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8349440))))[name = string("encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8361856)))];
+            tensor<fp16, [1, 188, 4096]> linear_1_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_0_feed_forward1_linear1_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("linear_1_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_33_cast_fp16 = silu(x = linear_1_cast_fp16)[name = string("input_33_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8370112))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12566592))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12564480))))[name = string("encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12567680)))];
+            tensor<fp16, [1, 188, 1024]> linear_2_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_0_feed_forward1_linear2_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_2_cast_fp16")];
+            fp16 var_507_to_fp16 = const()[name = string("op_507_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_508_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_507_to_fp16)[name = string("op_508_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_39_cast_fp16 = add(x = x_23_cast_fp16, y = var_508_cast_fp16)[name = string("input_39_cast_fp16")];
+            tensor<int32, [1]> query_1_axes_0 = const()[name = string("query_1_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12569792)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12571904)))];
+            tensor<fp16, [1, 188, 1024]> query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = encoder_module_layers_0_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_self_att_weight_to_fp16, x = input_39_cast_fp16)[name = string("query_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12574016))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13624768))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13622656))))[name = string("encoder_module_layers_0_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13625856)))];
+            tensor<fp16, [1, 188, 1024]> linear_3_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_q_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = string("linear_3_cast_fp16")];
+            tensor<int32, [4]> var_525 = const()[name = string("op_525"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_1_cast_fp16 = reshape(shape = var_525, x = linear_3_cast_fp16)[name = string("q_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13627968))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14678720))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14676608))))[name = string("encoder_module_layers_0_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14679808)))];
+            tensor<fp16, [1, 188, 1024]> linear_4_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_k_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = string("linear_4_cast_fp16")];
+            tensor<int32, [4]> var_530 = const()[name = string("op_530"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_1_cast_fp16 = reshape(shape = var_530, x = linear_4_cast_fp16)[name = string("k_1_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14681920))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15732672))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15730560))))[name = string("encoder_module_layers_0_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15733760)))];
+            tensor<fp16, [1, 188, 1024]> linear_5_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_v_weight_to_fp16_quantized, x = query_1_cast_fp16)[name = string("linear_5_cast_fp16")];
+            tensor<int32, [4]> var_535 = const()[name = string("op_535"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_1_cast_fp16 = reshape(shape = var_535, x = linear_5_cast_fp16)[name = string("v_1_cast_fp16")];
+            tensor<int32, [4]> value_5_perm_0 = const()[name = string("value_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_0_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15735872)))];
+            tensor<fp16, [1, 188, 8, 128]> var_547_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_module_layers_0_self_attn_pos_bias_u_to_fp16)[name = string("op_547_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_0_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15737984)))];
+            tensor<fp16, [1, 188, 8, 128]> var_549_cast_fp16 = add(x = q_1_cast_fp16, y = encoder_module_layers_0_self_attn_pos_bias_v_to_fp16)[name = string("op_549_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_1_perm_0 = const()[name = string("q_with_bias_v_1_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_27_transpose_x_0 = const()[name = string("x_27_transpose_x_0"), val = bool(false)];
+            bool x_27_transpose_y_0 = const()[name = string("x_27_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_551_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15740096))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16124992))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16124160))))[name = string("op_551_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_1_cast_fp16 = transpose(perm = q_with_bias_v_1_perm_0, x = var_549_cast_fp16)[name = string("transpose_312")];
+            tensor<fp16, [1, 8, 188, 375]> x_27_cast_fp16 = matmul(transpose_x = x_27_transpose_x_0, transpose_y = x_27_transpose_y_0, x = q_with_bias_v_1_cast_fp16, y = op_551_to_fp16_quantized)[name = string("x_27_cast_fp16")];
+            tensor<int32, [8]> x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_29_mode_0 = const()[name = string("x_29_mode_0"), val = string("constant")];
+            fp16 const_88_to_fp16 = const()[name = string("const_88_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_29_cast_fp16 = pad(constant_val = const_88_to_fp16, mode = x_29_mode_0, pad = x_29_pad_0, x = x_27_cast_fp16)[name = string("x_29_cast_fp16")];
+            tensor<int32, [4]> var_559 = const()[name = string("op_559"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_31_cast_fp16 = reshape(shape = var_559, x = x_29_cast_fp16)[name = string("x_31_cast_fp16")];
+            tensor<int32, [4]> var_563_begin_0 = const()[name = string("op_563_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_563_end_0 = const()[name = string("op_563_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_563_end_mask_0 = const()[name = string("op_563_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_563_cast_fp16 = slice_by_index(begin = var_563_begin_0, end = var_563_end_0, end_mask = var_563_end_mask_0, x = x_31_cast_fp16)[name = string("op_563_cast_fp16")];
+            tensor<int32, [4]> var_564 = const()[name = string("op_564"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_1_cast_fp16 = reshape(shape = var_564, x = var_563_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<int32, [4]> transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_97 = transpose(perm = transpose_97_perm_0, x = k_1_cast_fp16)[name = string("transpose_310")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_96 = transpose(perm = transpose_96_perm_0, x = var_547_cast_fp16)[name = string("transpose_311")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_3_end_0 = const()[name = string("matrix_bd_3_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_573_cast_fp16 = add(x = matrix_ac_1_cast_fp16, y = matrix_bd_3_cast_fp16)[name = string("op_573_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<fp16, [1, 8, 188, 188]> _inversed_scores_1_cast_fp16 = mul(x = var_573_cast_fp16, y = _inversed_scores_1_y_0_to_fp16)[name = string("_inversed_scores_1_cast_fp16")];
+            tensor<int32, [1]> mask_15_axes_0 = const()[name = string("mask_15_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188, 188]> mask_15 = expand_dims(axes = mask_15_axes_0, x = mask_13)[name = string("mask_15")];
+            fp16 var_163_to_fp16 = const()[name = string("op_163_to_fp16"), val = fp16(-0x1.388p+13)];
+            tensor<fp16, [1, 8, 188, 188]> scores_3_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_1_cast_fp16, cond = mask_15)[name = string("scores_3_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_579_cast_fp16 = softmax(axis = var_152, x = scores_3_cast_fp16)[name = string("op_579_cast_fp16")];
+            fp16 var_164_to_fp16 = const()[name = string("op_164_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 188]> input_41_cast_fp16 = select(a = var_164_to_fp16, b = var_579_cast_fp16, cond = mask_15)[name = string("input_41_cast_fp16")];
+            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<fp16, [1, 8, 188, 128]> value_5_cast_fp16 = transpose(perm = value_5_perm_0, x = v_1_cast_fp16)[name = string("transpose_309")];
+            tensor<fp16, [1, 8, 188, 128]> x_33_cast_fp16 = matmul(transpose_x = x_33_transpose_x_0, transpose_y = x_33_transpose_y_0, x = input_41_cast_fp16, y = value_5_cast_fp16)[name = string("x_33_cast_fp16")];
+            tensor<int32, [4]> var_583_perm_0 = const()[name = string("op_583_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_584 = const()[name = string("op_584"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_583_cast_fp16 = transpose(perm = var_583_perm_0, x = x_33_cast_fp16)[name = string("transpose_308")];
+            tensor<fp16, [1, 188, 1024]> input_43_cast_fp16 = reshape(shape = var_584, x = var_583_cast_fp16)[name = string("input_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_0_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16125440))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17176192))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17174080))))[name = string("encoder_module_layers_0_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_0_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17177280)))];
+            tensor<fp16, [1, 188, 1024]> linear_7_cast_fp16 = linear(bias = encoder_module_layers_0_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_0_self_attn_linear_out_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("linear_7_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_47_cast_fp16 = add(x = input_39_cast_fp16, y = linear_7_cast_fp16)[name = string("input_47_cast_fp16")];
+            tensor<int32, [1]> x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17179392)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17181504)))];
+            tensor<fp16, [1, 188, 1024]> x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, beta = encoder_module_layers_0_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_conv_weight_to_fp16, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")];
+            tensor<int32, [3]> input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17183616))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19284992))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19280832))))[name = string("encoder_module_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19287104)))];
+            tensor<fp16, [1, 1024, 188]> input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_37_cast_fp16)[name = string("transpose_307")];
+            tensor<fp16, [1, 2048, 188]> input_51_cast_fp16 = conv(bias = encoder_module_layers_0_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_0_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")];
+            int32 x_39_split_num_splits_0 = const()[name = string("x_39_split_num_splits_0"), val = int32(2)];
+            int32 x_39_split_axis_0 = const()[name = string("x_39_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_39_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_39_split_cast_fp16_1 = split(axis = x_39_split_axis_0, num_splits = x_39_split_num_splits_0, x = input_51_cast_fp16)[name = string("x_39_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_39_split_1_sigmoid_cast_fp16 = sigmoid(x = x_39_split_cast_fp16_1)[name = string("x_39_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_39_cast_fp16 = mul(x = x_39_split_cast_fp16_0, y = x_39_split_1_sigmoid_cast_fp16)[name = string("x_39_cast_fp16")];
+            tensor<int32, [1]> var_608_axes_0 = const()[name = string("op_608_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 188]> var_608 = expand_dims(axes = var_608_axes_0, x = pad_mask)[name = string("op_608")];
+            tensor<fp16, [1, 1024, 188]> input_53_cast_fp16 = select(a = var_164_to_fp16, b = x_39_cast_fp16, cond = var_608)[name = string("input_53_cast_fp16")];
+            tensor<int32, [6]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("constant")];
+            fp16 const_91_to_fp16 = const()[name = string("const_91_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_55_cast_fp16 = pad(constant_val = const_91_to_fp16, mode = input_55_mode_0, pad = input_55_pad_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")];
+            string input_57_pad_type_0 = const()[name = string("input_57_pad_type_0"), val = string("valid")];
+            int32 input_57_groups_0 = const()[name = string("input_57_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_57_strides_0 = const()[name = string("input_57_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_57_dilations_0 = const()[name = string("input_57_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_322_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19291264))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19302656))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19300544))))[name = string("const_322_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_323_to_fp16 = const()[name = string("const_323_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19303744)))];
+            tensor<fp16, [1, 1024, 188]> input_59_cast_fp16 = conv(bias = const_323_to_fp16, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = const_322_to_fp16_quantized, x = input_55_cast_fp16)[name = string("input_59_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_61_cast_fp16 = silu(x = input_59_cast_fp16)[name = string("input_61_cast_fp16")];
+            string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19305856))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20356608))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20354496))))[name = string("encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20357696)))];
+            tensor<fp16, [1, 1024, 188]> x_41_cast_fp16 = conv(bias = encoder_module_layers_0_conv_pointwise_conv2_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = encoder_module_layers_0_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_61_cast_fp16)[name = string("x_41_cast_fp16")];
+            tensor<int32, [3]> input_63_perm_0 = const()[name = string("input_63_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_63_cast_fp16 = transpose(perm = input_63_perm_0, x = x_41_cast_fp16)[name = string("transpose_306")];
+            tensor<fp16, [1, 188, 1024]> input_65_cast_fp16 = add(x = input_47_cast_fp16, y = input_63_cast_fp16)[name = string("input_65_cast_fp16")];
+            tensor<int32, [1]> input_67_axes_0 = const()[name = string("input_67_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20359808)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20361920)))];
+            tensor<fp16, [1, 188, 1024]> input_67_cast_fp16 = layer_norm(axes = input_67_axes_0, beta = encoder_module_layers_0_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_feed_forward2_weight_to_fp16, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20364032))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24566656))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24558400))))[name = string("encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24570816)))];
+            tensor<fp16, [1, 188, 4096]> linear_8_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_0_feed_forward2_linear1_weight_to_fp16_quantized, x = input_67_cast_fp16)[name = string("linear_8_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_71_cast_fp16 = silu(x = linear_8_cast_fp16)[name = string("input_71_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24579072))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28775552))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28773440))))[name = string("encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28776640)))];
+            tensor<fp16, [1, 188, 1024]> linear_9_cast_fp16 = linear(bias = encoder_module_layers_0_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_0_feed_forward2_linear2_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_9_cast_fp16")];
+            fp16 var_650_to_fp16 = const()[name = string("op_650_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_651_cast_fp16 = mul(x = linear_9_cast_fp16, y = var_650_to_fp16)[name = string("op_651_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_77_cast_fp16 = add(x = input_65_cast_fp16, y = var_651_cast_fp16)[name = string("input_77_cast_fp16")];
+            tensor<int32, [1]> input_79_axes_0 = const()[name = string("input_79_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_0_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28778752)))];
+            tensor<fp16, [1024]> encoder_module_layers_0_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_0_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28780864)))];
+            tensor<fp16, [1, 188, 1024]> input_79_cast_fp16 = layer_norm(axes = input_79_axes_0, beta = encoder_module_layers_0_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_0_norm_out_weight_to_fp16, x = input_77_cast_fp16)[name = string("input_79_cast_fp16")];
+            tensor<int32, [1]> input_81_axes_0 = const()[name = string("input_81_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28782976)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28785088)))];
+            tensor<fp16, [1, 188, 1024]> input_81_cast_fp16 = layer_norm(axes = input_81_axes_0, beta = encoder_module_layers_1_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_feed_forward1_weight_to_fp16, x = input_79_cast_fp16)[name = string("input_81_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28787200))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32989824))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32981568))))[name = string("encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32993984)))];
+            tensor<fp16, [1, 188, 4096]> linear_10_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_1_feed_forward1_linear1_weight_to_fp16_quantized, x = input_81_cast_fp16)[name = string("linear_10_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_85_cast_fp16 = silu(x = linear_10_cast_fp16)[name = string("input_85_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33002240))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37198720))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37196608))))[name = string("encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37199808)))];
+            tensor<fp16, [1, 188, 1024]> linear_11_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_1_feed_forward1_linear2_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_11_cast_fp16")];
+            fp16 var_681_to_fp16 = const()[name = string("op_681_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_682_cast_fp16 = mul(x = linear_11_cast_fp16, y = var_681_to_fp16)[name = string("op_682_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_91_cast_fp16 = add(x = input_79_cast_fp16, y = var_682_cast_fp16)[name = string("input_91_cast_fp16")];
+            tensor<int32, [1]> query_3_axes_0 = const()[name = string("query_3_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37201920)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37204032)))];
+            tensor<fp16, [1, 188, 1024]> query_3_cast_fp16 = layer_norm(axes = query_3_axes_0, beta = encoder_module_layers_1_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_self_att_weight_to_fp16, x = input_91_cast_fp16)[name = string("query_3_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37206144))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38256896))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38254784))))[name = string("encoder_module_layers_1_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38257984)))];
+            tensor<fp16, [1, 188, 1024]> linear_12_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_q_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = string("linear_12_cast_fp16")];
+            tensor<int32, [4]> var_699 = const()[name = string("op_699"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_7_cast_fp16 = reshape(shape = var_699, x = linear_12_cast_fp16)[name = string("q_7_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38260096))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39310848))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39308736))))[name = string("encoder_module_layers_1_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39311936)))];
+            tensor<fp16, [1, 188, 1024]> linear_13_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_k_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = string("linear_13_cast_fp16")];
+            tensor<int32, [4]> var_704 = const()[name = string("op_704"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_5_cast_fp16 = reshape(shape = var_704, x = linear_13_cast_fp16)[name = string("k_5_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39314048))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40364800))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40362688))))[name = string("encoder_module_layers_1_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40365888)))];
+            tensor<fp16, [1, 188, 1024]> linear_14_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_v_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = string("linear_14_cast_fp16")];
+            tensor<int32, [4]> var_709 = const()[name = string("op_709"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_3_cast_fp16 = reshape(shape = var_709, x = linear_14_cast_fp16)[name = string("v_3_cast_fp16")];
+            tensor<int32, [4]> value_7_perm_0 = const()[name = string("value_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_1_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40368000)))];
+            tensor<fp16, [1, 188, 8, 128]> var_721_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_module_layers_1_self_attn_pos_bias_u_to_fp16)[name = string("op_721_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_1_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40370112)))];
+            tensor<fp16, [1, 188, 8, 128]> var_723_cast_fp16 = add(x = q_7_cast_fp16, y = encoder_module_layers_1_self_attn_pos_bias_v_to_fp16)[name = string("op_723_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_3_perm_0 = const()[name = string("q_with_bias_v_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_49_transpose_x_0 = const()[name = string("x_49_transpose_x_0"), val = bool(false)];
+            bool x_49_transpose_y_0 = const()[name = string("x_49_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_725_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40372224))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40757120))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40756288))))[name = string("op_725_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_3_cast_fp16 = transpose(perm = q_with_bias_v_3_perm_0, x = var_723_cast_fp16)[name = string("transpose_305")];
+            tensor<fp16, [1, 8, 188, 375]> x_49_cast_fp16 = matmul(transpose_x = x_49_transpose_x_0, transpose_y = x_49_transpose_y_0, x = q_with_bias_v_3_cast_fp16, y = op_725_to_fp16_quantized)[name = string("x_49_cast_fp16")];
+            tensor<int32, [8]> x_51_pad_0 = const()[name = string("x_51_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_51_mode_0 = const()[name = string("x_51_mode_0"), val = string("constant")];
+            fp16 const_98_to_fp16 = const()[name = string("const_98_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_51_cast_fp16 = pad(constant_val = const_98_to_fp16, mode = x_51_mode_0, pad = x_51_pad_0, x = x_49_cast_fp16)[name = string("x_51_cast_fp16")];
+            tensor<int32, [4]> var_733 = const()[name = string("op_733"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_53_cast_fp16 = reshape(shape = var_733, x = x_51_cast_fp16)[name = string("x_53_cast_fp16")];
+            tensor<int32, [4]> var_737_begin_0 = const()[name = string("op_737_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_737_end_0 = const()[name = string("op_737_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_737_end_mask_0 = const()[name = string("op_737_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_737_cast_fp16 = slice_by_index(begin = var_737_begin_0, end = var_737_end_0, end_mask = var_737_end_mask_0, x = x_53_cast_fp16)[name = string("op_737_cast_fp16")];
+            tensor<int32, [4]> var_738 = const()[name = string("op_738"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_5_cast_fp16 = reshape(shape = var_738, x = var_737_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<int32, [4]> transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_99 = transpose(perm = transpose_99_perm_0, x = k_5_cast_fp16)[name = string("transpose_303")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_98 = transpose(perm = transpose_98_perm_0, x = var_721_cast_fp16)[name = string("transpose_304")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_7_end_0 = const()[name = string("matrix_bd_7_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_747_cast_fp16 = add(x = matrix_ac_3_cast_fp16, y = matrix_bd_7_cast_fp16)[name = string("op_747_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<fp16, [1, 8, 188, 188]> _inversed_scores_5_cast_fp16 = mul(x = var_747_cast_fp16, y = _inversed_scores_5_y_0_to_fp16)[name = string("_inversed_scores_5_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_7_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_5_cast_fp16, cond = mask_15)[name = string("scores_7_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_753_cast_fp16 = softmax(axis = var_152, x = scores_7_cast_fp16)[name = string("op_753_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_93_cast_fp16 = select(a = var_164_to_fp16, b = var_753_cast_fp16, cond = mask_15)[name = string("input_93_cast_fp16")];
+            bool x_55_transpose_x_0 = const()[name = string("x_55_transpose_x_0"), val = bool(false)];
+            bool x_55_transpose_y_0 = const()[name = string("x_55_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_7_cast_fp16 = transpose(perm = value_7_perm_0, x = v_3_cast_fp16)[name = string("transpose_302")];
+            tensor<fp16, [1, 8, 188, 128]> x_55_cast_fp16 = matmul(transpose_x = x_55_transpose_x_0, transpose_y = x_55_transpose_y_0, x = input_93_cast_fp16, y = value_7_cast_fp16)[name = string("x_55_cast_fp16")];
+            tensor<int32, [4]> var_757_perm_0 = const()[name = string("op_757_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_758 = const()[name = string("op_758"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_757_cast_fp16 = transpose(perm = var_757_perm_0, x = x_55_cast_fp16)[name = string("transpose_301")];
+            tensor<fp16, [1, 188, 1024]> input_95_cast_fp16 = reshape(shape = var_758, x = var_757_cast_fp16)[name = string("input_95_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_1_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40757568))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41808320))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41806208))))[name = string("encoder_module_layers_1_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_1_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41809408)))];
+            tensor<fp16, [1, 188, 1024]> linear_16_cast_fp16 = linear(bias = encoder_module_layers_1_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_1_self_attn_linear_out_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_16_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_99_cast_fp16 = add(x = input_91_cast_fp16, y = linear_16_cast_fp16)[name = string("input_99_cast_fp16")];
+            tensor<int32, [1]> x_59_axes_0 = const()[name = string("x_59_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41811520)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41813632)))];
+            tensor<fp16, [1, 188, 1024]> x_59_cast_fp16 = layer_norm(axes = x_59_axes_0, beta = encoder_module_layers_1_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_conv_weight_to_fp16, x = input_99_cast_fp16)[name = string("x_59_cast_fp16")];
+            tensor<int32, [3]> input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41815744))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43917120))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43912960))))[name = string("encoder_module_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43919232)))];
+            tensor<fp16, [1, 1024, 188]> input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_59_cast_fp16)[name = string("transpose_300")];
+            tensor<fp16, [1, 2048, 188]> input_103_cast_fp16 = conv(bias = encoder_module_layers_1_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_1_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")];
+            int32 x_61_split_num_splits_0 = const()[name = string("x_61_split_num_splits_0"), val = int32(2)];
+            int32 x_61_split_axis_0 = const()[name = string("x_61_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_61_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_61_split_cast_fp16_1 = split(axis = x_61_split_axis_0, num_splits = x_61_split_num_splits_0, x = input_103_cast_fp16)[name = string("x_61_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_61_split_1_sigmoid_cast_fp16 = sigmoid(x = x_61_split_cast_fp16_1)[name = string("x_61_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_61_cast_fp16 = mul(x = x_61_split_cast_fp16_0, y = x_61_split_1_sigmoid_cast_fp16)[name = string("x_61_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_105_cast_fp16 = select(a = var_164_to_fp16, b = x_61_cast_fp16, cond = var_608)[name = string("input_105_cast_fp16")];
+            tensor<int32, [6]> input_107_pad_0 = const()[name = string("input_107_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_107_mode_0 = const()[name = string("input_107_mode_0"), val = string("constant")];
+            fp16 const_101_to_fp16 = const()[name = string("const_101_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_107_cast_fp16 = pad(constant_val = const_101_to_fp16, mode = input_107_mode_0, pad = input_107_pad_0, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")];
+            string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("valid")];
+            int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_324_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43923392))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43934784))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43932672))))[name = string("const_324_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_325_to_fp16 = const()[name = string("const_325_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43935872)))];
+            tensor<fp16, [1, 1024, 188]> input_111_cast_fp16 = conv(bias = const_325_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_324_to_fp16_quantized, x = input_107_cast_fp16)[name = string("input_111_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_113_cast_fp16 = silu(x = input_111_cast_fp16)[name = string("input_113_cast_fp16")];
+            string x_63_pad_type_0 = const()[name = string("x_63_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_63_strides_0 = const()[name = string("x_63_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_63_pad_0 = const()[name = string("x_63_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_63_dilations_0 = const()[name = string("x_63_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_63_groups_0 = const()[name = string("x_63_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43937984))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44988736))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44986624))))[name = string("encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44989824)))];
+            tensor<fp16, [1, 1024, 188]> x_63_cast_fp16 = conv(bias = encoder_module_layers_1_conv_pointwise_conv2_bias_to_fp16, dilations = x_63_dilations_0, groups = x_63_groups_0, pad = x_63_pad_0, pad_type = x_63_pad_type_0, strides = x_63_strides_0, weight = encoder_module_layers_1_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_113_cast_fp16)[name = string("x_63_cast_fp16")];
+            tensor<int32, [3]> input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_63_cast_fp16)[name = string("transpose_299")];
+            tensor<fp16, [1, 188, 1024]> input_117_cast_fp16 = add(x = input_99_cast_fp16, y = input_115_cast_fp16)[name = string("input_117_cast_fp16")];
+            tensor<int32, [1]> input_119_axes_0 = const()[name = string("input_119_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44991936)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44994048)))];
+            tensor<fp16, [1, 188, 1024]> input_119_cast_fp16 = layer_norm(axes = input_119_axes_0, beta = encoder_module_layers_1_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_feed_forward2_weight_to_fp16, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44996160))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49198784))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49190528))))[name = string("encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49202944)))];
+            tensor<fp16, [1, 188, 4096]> linear_17_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_1_feed_forward2_linear1_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_17_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_123_cast_fp16 = silu(x = linear_17_cast_fp16)[name = string("input_123_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49211200))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53407680))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53405568))))[name = string("encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53408768)))];
+            tensor<fp16, [1, 188, 1024]> linear_18_cast_fp16 = linear(bias = encoder_module_layers_1_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_1_feed_forward2_linear2_weight_to_fp16_quantized, x = input_123_cast_fp16)[name = string("linear_18_cast_fp16")];
+            fp16 var_824_to_fp16 = const()[name = string("op_824_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_825_cast_fp16 = mul(x = linear_18_cast_fp16, y = var_824_to_fp16)[name = string("op_825_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_129_cast_fp16 = add(x = input_117_cast_fp16, y = var_825_cast_fp16)[name = string("input_129_cast_fp16")];
+            tensor<int32, [1]> input_131_axes_0 = const()[name = string("input_131_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_1_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53410880)))];
+            tensor<fp16, [1024]> encoder_module_layers_1_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_1_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53412992)))];
+            tensor<fp16, [1, 188, 1024]> input_131_cast_fp16 = layer_norm(axes = input_131_axes_0, beta = encoder_module_layers_1_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_1_norm_out_weight_to_fp16, x = input_129_cast_fp16)[name = string("input_131_cast_fp16")];
+            tensor<int32, [1]> input_133_axes_0 = const()[name = string("input_133_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53415104)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53417216)))];
+            tensor<fp16, [1, 188, 1024]> input_133_cast_fp16 = layer_norm(axes = input_133_axes_0, beta = encoder_module_layers_2_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_feed_forward1_weight_to_fp16, x = input_131_cast_fp16)[name = string("input_133_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53419328))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57621952))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57613696))))[name = string("encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57626112)))];
+            tensor<fp16, [1, 188, 4096]> linear_19_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_2_feed_forward1_linear1_weight_to_fp16_quantized, x = input_133_cast_fp16)[name = string("linear_19_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_137_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("input_137_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57634368))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61830848))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61828736))))[name = string("encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61831936)))];
+            tensor<fp16, [1, 188, 1024]> linear_20_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_2_feed_forward1_linear2_weight_to_fp16_quantized, x = input_137_cast_fp16)[name = string("linear_20_cast_fp16")];
+            fp16 var_855_to_fp16 = const()[name = string("op_855_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_856_cast_fp16 = mul(x = linear_20_cast_fp16, y = var_855_to_fp16)[name = string("op_856_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_143_cast_fp16 = add(x = input_131_cast_fp16, y = var_856_cast_fp16)[name = string("input_143_cast_fp16")];
+            tensor<int32, [1]> query_5_axes_0 = const()[name = string("query_5_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61834048)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61836160)))];
+            tensor<fp16, [1, 188, 1024]> query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = encoder_module_layers_2_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_self_att_weight_to_fp16, x = input_143_cast_fp16)[name = string("query_5_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61838272))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62889024))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62886912))))[name = string("encoder_module_layers_2_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62890112)))];
+            tensor<fp16, [1, 188, 1024]> linear_21_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_q_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = string("linear_21_cast_fp16")];
+            tensor<int32, [4]> var_873 = const()[name = string("op_873"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_13_cast_fp16 = reshape(shape = var_873, x = linear_21_cast_fp16)[name = string("q_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62892224))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63942976))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63940864))))[name = string("encoder_module_layers_2_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63944064)))];
+            tensor<fp16, [1, 188, 1024]> linear_22_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_k_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = string("linear_22_cast_fp16")];
+            tensor<int32, [4]> var_878 = const()[name = string("op_878"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_9_cast_fp16 = reshape(shape = var_878, x = linear_22_cast_fp16)[name = string("k_9_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63946176))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64996928))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64994816))))[name = string("encoder_module_layers_2_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64998016)))];
+            tensor<fp16, [1, 188, 1024]> linear_23_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_v_weight_to_fp16_quantized, x = query_5_cast_fp16)[name = string("linear_23_cast_fp16")];
+            tensor<int32, [4]> var_883 = const()[name = string("op_883"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_5_cast_fp16 = reshape(shape = var_883, x = linear_23_cast_fp16)[name = string("v_5_cast_fp16")];
+            tensor<int32, [4]> value_9_perm_0 = const()[name = string("value_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_2_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65000128)))];
+            tensor<fp16, [1, 188, 8, 128]> var_895_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_module_layers_2_self_attn_pos_bias_u_to_fp16)[name = string("op_895_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_2_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65002240)))];
+            tensor<fp16, [1, 188, 8, 128]> var_897_cast_fp16 = add(x = q_13_cast_fp16, y = encoder_module_layers_2_self_attn_pos_bias_v_to_fp16)[name = string("op_897_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_5_perm_0 = const()[name = string("q_with_bias_v_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_71_transpose_x_0 = const()[name = string("x_71_transpose_x_0"), val = bool(false)];
+            bool x_71_transpose_y_0 = const()[name = string("x_71_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_899_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65004352))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65389248))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65388416))))[name = string("op_899_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_5_cast_fp16 = transpose(perm = q_with_bias_v_5_perm_0, x = var_897_cast_fp16)[name = string("transpose_298")];
+            tensor<fp16, [1, 8, 188, 375]> x_71_cast_fp16 = matmul(transpose_x = x_71_transpose_x_0, transpose_y = x_71_transpose_y_0, x = q_with_bias_v_5_cast_fp16, y = op_899_to_fp16_quantized)[name = string("x_71_cast_fp16")];
+            tensor<int32, [8]> x_73_pad_0 = const()[name = string("x_73_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("constant")];
+            fp16 const_108_to_fp16 = const()[name = string("const_108_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_73_cast_fp16 = pad(constant_val = const_108_to_fp16, mode = x_73_mode_0, pad = x_73_pad_0, x = x_71_cast_fp16)[name = string("x_73_cast_fp16")];
+            tensor<int32, [4]> var_907 = const()[name = string("op_907"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_75_cast_fp16 = reshape(shape = var_907, x = x_73_cast_fp16)[name = string("x_75_cast_fp16")];
+            tensor<int32, [4]> var_911_begin_0 = const()[name = string("op_911_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_911_end_0 = const()[name = string("op_911_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_911_end_mask_0 = const()[name = string("op_911_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_911_cast_fp16 = slice_by_index(begin = var_911_begin_0, end = var_911_end_0, end_mask = var_911_end_mask_0, x = x_75_cast_fp16)[name = string("op_911_cast_fp16")];
+            tensor<int32, [4]> var_912 = const()[name = string("op_912"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_9_cast_fp16 = reshape(shape = var_912, x = var_911_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<int32, [4]> transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_101 = transpose(perm = transpose_101_perm_0, x = k_9_cast_fp16)[name = string("transpose_296")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_100 = transpose(perm = transpose_100_perm_0, x = var_895_cast_fp16)[name = string("transpose_297")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_11_end_0 = const()[name = string("matrix_bd_11_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_921_cast_fp16 = add(x = matrix_ac_5_cast_fp16, y = matrix_bd_11_cast_fp16)[name = string("op_921_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<fp16, [1, 8, 188, 188]> _inversed_scores_9_cast_fp16 = mul(x = var_921_cast_fp16, y = _inversed_scores_9_y_0_to_fp16)[name = string("_inversed_scores_9_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_11_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_9_cast_fp16, cond = mask_15)[name = string("scores_11_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_927_cast_fp16 = softmax(axis = var_152, x = scores_11_cast_fp16)[name = string("op_927_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_145_cast_fp16 = select(a = var_164_to_fp16, b = var_927_cast_fp16, cond = mask_15)[name = string("input_145_cast_fp16")];
+            bool x_77_transpose_x_0 = const()[name = string("x_77_transpose_x_0"), val = bool(false)];
+            bool x_77_transpose_y_0 = const()[name = string("x_77_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_9_cast_fp16 = transpose(perm = value_9_perm_0, x = v_5_cast_fp16)[name = string("transpose_295")];
+            tensor<fp16, [1, 8, 188, 128]> x_77_cast_fp16 = matmul(transpose_x = x_77_transpose_x_0, transpose_y = x_77_transpose_y_0, x = input_145_cast_fp16, y = value_9_cast_fp16)[name = string("x_77_cast_fp16")];
+            tensor<int32, [4]> var_931_perm_0 = const()[name = string("op_931_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_932 = const()[name = string("op_932"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_931_cast_fp16 = transpose(perm = var_931_perm_0, x = x_77_cast_fp16)[name = string("transpose_294")];
+            tensor<fp16, [1, 188, 1024]> input_147_cast_fp16 = reshape(shape = var_932, x = var_931_cast_fp16)[name = string("input_147_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_2_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65389696))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66440448))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66438336))))[name = string("encoder_module_layers_2_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_2_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66441536)))];
+            tensor<fp16, [1, 188, 1024]> linear_25_cast_fp16 = linear(bias = encoder_module_layers_2_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_2_self_attn_linear_out_weight_to_fp16_quantized, x = input_147_cast_fp16)[name = string("linear_25_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_151_cast_fp16 = add(x = input_143_cast_fp16, y = linear_25_cast_fp16)[name = string("input_151_cast_fp16")];
+            tensor<int32, [1]> x_81_axes_0 = const()[name = string("x_81_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66443648)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66445760)))];
+            tensor<fp16, [1, 188, 1024]> x_81_cast_fp16 = layer_norm(axes = x_81_axes_0, beta = encoder_module_layers_2_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_conv_weight_to_fp16, x = input_151_cast_fp16)[name = string("x_81_cast_fp16")];
+            tensor<int32, [3]> input_153_perm_0 = const()[name = string("input_153_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_155_pad_type_0 = const()[name = string("input_155_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_155_strides_0 = const()[name = string("input_155_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_155_pad_0 = const()[name = string("input_155_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_155_dilations_0 = const()[name = string("input_155_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_155_groups_0 = const()[name = string("input_155_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66447872))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68549248))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68545088))))[name = string("encoder_module_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68551360)))];
+            tensor<fp16, [1, 1024, 188]> input_153_cast_fp16 = transpose(perm = input_153_perm_0, x = x_81_cast_fp16)[name = string("transpose_293")];
+            tensor<fp16, [1, 2048, 188]> input_155_cast_fp16 = conv(bias = encoder_module_layers_2_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_2_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_153_cast_fp16)[name = string("input_155_cast_fp16")];
+            int32 x_83_split_num_splits_0 = const()[name = string("x_83_split_num_splits_0"), val = int32(2)];
+            int32 x_83_split_axis_0 = const()[name = string("x_83_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_83_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_83_split_cast_fp16_1 = split(axis = x_83_split_axis_0, num_splits = x_83_split_num_splits_0, x = input_155_cast_fp16)[name = string("x_83_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_83_split_1_sigmoid_cast_fp16 = sigmoid(x = x_83_split_cast_fp16_1)[name = string("x_83_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_83_cast_fp16 = mul(x = x_83_split_cast_fp16_0, y = x_83_split_1_sigmoid_cast_fp16)[name = string("x_83_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_157_cast_fp16 = select(a = var_164_to_fp16, b = x_83_cast_fp16, cond = var_608)[name = string("input_157_cast_fp16")];
+            tensor<int32, [6]> input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_159_mode_0 = const()[name = string("input_159_mode_0"), val = string("constant")];
+            fp16 const_111_to_fp16 = const()[name = string("const_111_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_159_cast_fp16 = pad(constant_val = const_111_to_fp16, mode = input_159_mode_0, pad = input_159_pad_0, x = input_157_cast_fp16)[name = string("input_159_cast_fp16")];
+            string input_161_pad_type_0 = const()[name = string("input_161_pad_type_0"), val = string("valid")];
+            int32 input_161_groups_0 = const()[name = string("input_161_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_161_strides_0 = const()[name = string("input_161_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_161_pad_0 = const()[name = string("input_161_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_161_dilations_0 = const()[name = string("input_161_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_326_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68555520))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68566912))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68564800))))[name = string("const_326_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_327_to_fp16 = const()[name = string("const_327_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68568000)))];
+            tensor<fp16, [1, 1024, 188]> input_163_cast_fp16 = conv(bias = const_327_to_fp16, dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = const_326_to_fp16_quantized, x = input_159_cast_fp16)[name = string("input_163_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_165_cast_fp16 = silu(x = input_163_cast_fp16)[name = string("input_165_cast_fp16")];
+            string x_85_pad_type_0 = const()[name = string("x_85_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_85_strides_0 = const()[name = string("x_85_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_85_pad_0 = const()[name = string("x_85_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_85_dilations_0 = const()[name = string("x_85_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_85_groups_0 = const()[name = string("x_85_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68570112))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69620864))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69618752))))[name = string("encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69621952)))];
+            tensor<fp16, [1, 1024, 188]> x_85_cast_fp16 = conv(bias = encoder_module_layers_2_conv_pointwise_conv2_bias_to_fp16, dilations = x_85_dilations_0, groups = x_85_groups_0, pad = x_85_pad_0, pad_type = x_85_pad_type_0, strides = x_85_strides_0, weight = encoder_module_layers_2_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("x_85_cast_fp16")];
+            tensor<int32, [3]> input_167_perm_0 = const()[name = string("input_167_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_167_cast_fp16 = transpose(perm = input_167_perm_0, x = x_85_cast_fp16)[name = string("transpose_292")];
+            tensor<fp16, [1, 188, 1024]> input_169_cast_fp16 = add(x = input_151_cast_fp16, y = input_167_cast_fp16)[name = string("input_169_cast_fp16")];
+            tensor<int32, [1]> input_171_axes_0 = const()[name = string("input_171_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69624064)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69626176)))];
+            tensor<fp16, [1, 188, 1024]> input_171_cast_fp16 = layer_norm(axes = input_171_axes_0, beta = encoder_module_layers_2_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_feed_forward2_weight_to_fp16, x = input_169_cast_fp16)[name = string("input_171_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69628288))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73830912))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73822656))))[name = string("encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73835072)))];
+            tensor<fp16, [1, 188, 4096]> linear_26_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_2_feed_forward2_linear1_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_26_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_175_cast_fp16 = silu(x = linear_26_cast_fp16)[name = string("input_175_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73843328))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78039808))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78037696))))[name = string("encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78040896)))];
+            tensor<fp16, [1, 188, 1024]> linear_27_cast_fp16 = linear(bias = encoder_module_layers_2_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_2_feed_forward2_linear2_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_27_cast_fp16")];
+            fp16 var_998_to_fp16 = const()[name = string("op_998_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_999_cast_fp16 = mul(x = linear_27_cast_fp16, y = var_998_to_fp16)[name = string("op_999_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_181_cast_fp16 = add(x = input_169_cast_fp16, y = var_999_cast_fp16)[name = string("input_181_cast_fp16")];
+            tensor<int32, [1]> input_183_axes_0 = const()[name = string("input_183_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_2_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78043008)))];
+            tensor<fp16, [1024]> encoder_module_layers_2_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_2_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78045120)))];
+            tensor<fp16, [1, 188, 1024]> input_183_cast_fp16 = layer_norm(axes = input_183_axes_0, beta = encoder_module_layers_2_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_2_norm_out_weight_to_fp16, x = input_181_cast_fp16)[name = string("input_183_cast_fp16")];
+            tensor<int32, [1]> input_185_axes_0 = const()[name = string("input_185_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78047232)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78049344)))];
+            tensor<fp16, [1, 188, 1024]> input_185_cast_fp16 = layer_norm(axes = input_185_axes_0, beta = encoder_module_layers_3_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_feed_forward1_weight_to_fp16, x = input_183_cast_fp16)[name = string("input_185_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78051456))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82254080))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82245824))))[name = string("encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82258240)))];
+            tensor<fp16, [1, 188, 4096]> linear_28_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_3_feed_forward1_linear1_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_28_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_189_cast_fp16 = silu(x = linear_28_cast_fp16)[name = string("input_189_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82266496))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86462976))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86460864))))[name = string("encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86464064)))];
+            tensor<fp16, [1, 188, 1024]> linear_29_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_3_feed_forward1_linear2_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_29_cast_fp16")];
+            fp16 var_1029_to_fp16 = const()[name = string("op_1029_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1030_cast_fp16 = mul(x = linear_29_cast_fp16, y = var_1029_to_fp16)[name = string("op_1030_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_195_cast_fp16 = add(x = input_183_cast_fp16, y = var_1030_cast_fp16)[name = string("input_195_cast_fp16")];
+            tensor<int32, [1]> query_7_axes_0 = const()[name = string("query_7_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86466176)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86468288)))];
+            tensor<fp16, [1, 188, 1024]> query_7_cast_fp16 = layer_norm(axes = query_7_axes_0, beta = encoder_module_layers_3_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_self_att_weight_to_fp16, x = input_195_cast_fp16)[name = string("query_7_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86470400))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87521152))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87519040))))[name = string("encoder_module_layers_3_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87522240)))];
+            tensor<fp16, [1, 188, 1024]> linear_30_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_q_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = string("linear_30_cast_fp16")];
+            tensor<int32, [4]> var_1047 = const()[name = string("op_1047"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_19_cast_fp16 = reshape(shape = var_1047, x = linear_30_cast_fp16)[name = string("q_19_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(87524352))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88575104))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88572992))))[name = string("encoder_module_layers_3_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88576192)))];
+            tensor<fp16, [1, 188, 1024]> linear_31_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_k_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = string("linear_31_cast_fp16")];
+            tensor<int32, [4]> var_1052 = const()[name = string("op_1052"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_13_cast_fp16 = reshape(shape = var_1052, x = linear_31_cast_fp16)[name = string("k_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88578304))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89629056))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89626944))))[name = string("encoder_module_layers_3_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89630144)))];
+            tensor<fp16, [1, 188, 1024]> linear_32_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_v_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = string("linear_32_cast_fp16")];
+            tensor<int32, [4]> var_1057 = const()[name = string("op_1057"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_7_cast_fp16 = reshape(shape = var_1057, x = linear_32_cast_fp16)[name = string("v_7_cast_fp16")];
+            tensor<int32, [4]> value_11_perm_0 = const()[name = string("value_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_3_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89632256)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1069_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_module_layers_3_self_attn_pos_bias_u_to_fp16)[name = string("op_1069_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_3_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89634368)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1071_cast_fp16 = add(x = q_19_cast_fp16, y = encoder_module_layers_3_self_attn_pos_bias_v_to_fp16)[name = string("op_1071_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_7_perm_0 = const()[name = string("q_with_bias_v_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_93_transpose_x_0 = const()[name = string("x_93_transpose_x_0"), val = bool(false)];
+            bool x_93_transpose_y_0 = const()[name = string("x_93_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1073_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89636480))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90021376))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90020544))))[name = string("op_1073_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_7_cast_fp16 = transpose(perm = q_with_bias_v_7_perm_0, x = var_1071_cast_fp16)[name = string("transpose_291")];
+            tensor<fp16, [1, 8, 188, 375]> x_93_cast_fp16 = matmul(transpose_x = x_93_transpose_x_0, transpose_y = x_93_transpose_y_0, x = q_with_bias_v_7_cast_fp16, y = op_1073_to_fp16_quantized)[name = string("x_93_cast_fp16")];
+            tensor<int32, [8]> x_95_pad_0 = const()[name = string("x_95_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("constant")];
+            fp16 const_118_to_fp16 = const()[name = string("const_118_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_95_cast_fp16 = pad(constant_val = const_118_to_fp16, mode = x_95_mode_0, pad = x_95_pad_0, x = x_93_cast_fp16)[name = string("x_95_cast_fp16")];
+            tensor<int32, [4]> var_1081 = const()[name = string("op_1081"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_97_cast_fp16 = reshape(shape = var_1081, x = x_95_cast_fp16)[name = string("x_97_cast_fp16")];
+            tensor<int32, [4]> var_1085_begin_0 = const()[name = string("op_1085_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1085_end_0 = const()[name = string("op_1085_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1085_end_mask_0 = const()[name = string("op_1085_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1085_cast_fp16 = slice_by_index(begin = var_1085_begin_0, end = var_1085_end_0, end_mask = var_1085_end_mask_0, x = x_97_cast_fp16)[name = string("op_1085_cast_fp16")];
+            tensor<int32, [4]> var_1086 = const()[name = string("op_1086"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_13_cast_fp16 = reshape(shape = var_1086, x = var_1085_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<int32, [4]> transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_103 = transpose(perm = transpose_103_perm_0, x = k_13_cast_fp16)[name = string("transpose_289")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1069_cast_fp16)[name = string("transpose_290")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_15_end_0 = const()[name = string("matrix_bd_15_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1095_cast_fp16 = add(x = matrix_ac_7_cast_fp16, y = matrix_bd_15_cast_fp16)[name = string("op_1095_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<fp16, [1, 8, 188, 188]> _inversed_scores_13_cast_fp16 = mul(x = var_1095_cast_fp16, y = _inversed_scores_13_y_0_to_fp16)[name = string("_inversed_scores_13_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_15_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_13_cast_fp16, cond = mask_15)[name = string("scores_15_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1101_cast_fp16 = softmax(axis = var_152, x = scores_15_cast_fp16)[name = string("op_1101_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_197_cast_fp16 = select(a = var_164_to_fp16, b = var_1101_cast_fp16, cond = mask_15)[name = string("input_197_cast_fp16")];
+            bool x_99_transpose_x_0 = const()[name = string("x_99_transpose_x_0"), val = bool(false)];
+            bool x_99_transpose_y_0 = const()[name = string("x_99_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_11_cast_fp16 = transpose(perm = value_11_perm_0, x = v_7_cast_fp16)[name = string("transpose_288")];
+            tensor<fp16, [1, 8, 188, 128]> x_99_cast_fp16 = matmul(transpose_x = x_99_transpose_x_0, transpose_y = x_99_transpose_y_0, x = input_197_cast_fp16, y = value_11_cast_fp16)[name = string("x_99_cast_fp16")];
+            tensor<int32, [4]> var_1105_perm_0 = const()[name = string("op_1105_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1106 = const()[name = string("op_1106"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1105_cast_fp16 = transpose(perm = var_1105_perm_0, x = x_99_cast_fp16)[name = string("transpose_287")];
+            tensor<fp16, [1, 188, 1024]> input_199_cast_fp16 = reshape(shape = var_1106, x = var_1105_cast_fp16)[name = string("input_199_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_3_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90021824))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91072576))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91070464))))[name = string("encoder_module_layers_3_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_3_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91073664)))];
+            tensor<fp16, [1, 188, 1024]> linear_34_cast_fp16 = linear(bias = encoder_module_layers_3_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_3_self_attn_linear_out_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_34_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_203_cast_fp16 = add(x = input_195_cast_fp16, y = linear_34_cast_fp16)[name = string("input_203_cast_fp16")];
+            tensor<int32, [1]> x_103_axes_0 = const()[name = string("x_103_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91075776)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91077888)))];
+            tensor<fp16, [1, 188, 1024]> x_103_cast_fp16 = layer_norm(axes = x_103_axes_0, beta = encoder_module_layers_3_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_conv_weight_to_fp16, x = input_203_cast_fp16)[name = string("x_103_cast_fp16")];
+            tensor<int32, [3]> input_205_perm_0 = const()[name = string("input_205_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_207_pad_type_0 = const()[name = string("input_207_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_207_strides_0 = const()[name = string("input_207_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_207_pad_0 = const()[name = string("input_207_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_207_dilations_0 = const()[name = string("input_207_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_207_groups_0 = const()[name = string("input_207_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91080000))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93181376))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93177216))))[name = string("encoder_module_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93183488)))];
+            tensor<fp16, [1, 1024, 188]> input_205_cast_fp16 = transpose(perm = input_205_perm_0, x = x_103_cast_fp16)[name = string("transpose_286")];
+            tensor<fp16, [1, 2048, 188]> input_207_cast_fp16 = conv(bias = encoder_module_layers_3_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_3_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("input_207_cast_fp16")];
+            int32 x_105_split_num_splits_0 = const()[name = string("x_105_split_num_splits_0"), val = int32(2)];
+            int32 x_105_split_axis_0 = const()[name = string("x_105_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_105_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_105_split_cast_fp16_1 = split(axis = x_105_split_axis_0, num_splits = x_105_split_num_splits_0, x = input_207_cast_fp16)[name = string("x_105_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_105_split_1_sigmoid_cast_fp16 = sigmoid(x = x_105_split_cast_fp16_1)[name = string("x_105_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_105_cast_fp16 = mul(x = x_105_split_cast_fp16_0, y = x_105_split_1_sigmoid_cast_fp16)[name = string("x_105_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_209_cast_fp16 = select(a = var_164_to_fp16, b = x_105_cast_fp16, cond = var_608)[name = string("input_209_cast_fp16")];
+            tensor<int32, [6]> input_211_pad_0 = const()[name = string("input_211_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_211_mode_0 = const()[name = string("input_211_mode_0"), val = string("constant")];
+            fp16 const_121_to_fp16 = const()[name = string("const_121_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_211_cast_fp16 = pad(constant_val = const_121_to_fp16, mode = input_211_mode_0, pad = input_211_pad_0, x = input_209_cast_fp16)[name = string("input_211_cast_fp16")];
+            string input_213_pad_type_0 = const()[name = string("input_213_pad_type_0"), val = string("valid")];
+            int32 input_213_groups_0 = const()[name = string("input_213_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_213_strides_0 = const()[name = string("input_213_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_213_pad_0 = const()[name = string("input_213_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_213_dilations_0 = const()[name = string("input_213_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_328_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93187648))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93199040))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93196928))))[name = string("const_328_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_329_to_fp16 = const()[name = string("const_329_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93200128)))];
+            tensor<fp16, [1, 1024, 188]> input_215_cast_fp16 = conv(bias = const_329_to_fp16, dilations = input_213_dilations_0, groups = input_213_groups_0, pad = input_213_pad_0, pad_type = input_213_pad_type_0, strides = input_213_strides_0, weight = const_328_to_fp16_quantized, x = input_211_cast_fp16)[name = string("input_215_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_217_cast_fp16 = silu(x = input_215_cast_fp16)[name = string("input_217_cast_fp16")];
+            string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93202240))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94252992))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94250880))))[name = string("encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94254080)))];
+            tensor<fp16, [1, 1024, 188]> x_107_cast_fp16 = conv(bias = encoder_module_layers_3_conv_pointwise_conv2_bias_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = encoder_module_layers_3_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_217_cast_fp16)[name = string("x_107_cast_fp16")];
+            tensor<int32, [3]> input_219_perm_0 = const()[name = string("input_219_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_219_cast_fp16 = transpose(perm = input_219_perm_0, x = x_107_cast_fp16)[name = string("transpose_285")];
+            tensor<fp16, [1, 188, 1024]> input_221_cast_fp16 = add(x = input_203_cast_fp16, y = input_219_cast_fp16)[name = string("input_221_cast_fp16")];
+            tensor<int32, [1]> input_223_axes_0 = const()[name = string("input_223_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94256192)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94258304)))];
+            tensor<fp16, [1, 188, 1024]> input_223_cast_fp16 = layer_norm(axes = input_223_axes_0, beta = encoder_module_layers_3_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_feed_forward2_weight_to_fp16, x = input_221_cast_fp16)[name = string("input_223_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94260416))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98463040))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98454784))))[name = string("encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98467200)))];
+            tensor<fp16, [1, 188, 4096]> linear_35_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_3_feed_forward2_linear1_weight_to_fp16_quantized, x = input_223_cast_fp16)[name = string("linear_35_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_227_cast_fp16 = silu(x = linear_35_cast_fp16)[name = string("input_227_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98475456))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102671936))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102669824))))[name = string("encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102673024)))];
+            tensor<fp16, [1, 188, 1024]> linear_36_cast_fp16 = linear(bias = encoder_module_layers_3_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_3_feed_forward2_linear2_weight_to_fp16_quantized, x = input_227_cast_fp16)[name = string("linear_36_cast_fp16")];
+            fp16 var_1172_to_fp16 = const()[name = string("op_1172_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1173_cast_fp16 = mul(x = linear_36_cast_fp16, y = var_1172_to_fp16)[name = string("op_1173_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_233_cast_fp16 = add(x = input_221_cast_fp16, y = var_1173_cast_fp16)[name = string("input_233_cast_fp16")];
+            tensor<int32, [1]> input_235_axes_0 = const()[name = string("input_235_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_3_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102675136)))];
+            tensor<fp16, [1024]> encoder_module_layers_3_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_3_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102677248)))];
+            tensor<fp16, [1, 188, 1024]> input_235_cast_fp16 = layer_norm(axes = input_235_axes_0, beta = encoder_module_layers_3_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_3_norm_out_weight_to_fp16, x = input_233_cast_fp16)[name = string("input_235_cast_fp16")];
+            tensor<int32, [1]> input_237_axes_0 = const()[name = string("input_237_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102679360)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102681472)))];
+            tensor<fp16, [1, 188, 1024]> input_237_cast_fp16 = layer_norm(axes = input_237_axes_0, beta = encoder_module_layers_4_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_feed_forward1_weight_to_fp16, x = input_235_cast_fp16)[name = string("input_237_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102683584))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106886208))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106877952))))[name = string("encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106890368)))];
+            tensor<fp16, [1, 188, 4096]> linear_37_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_4_feed_forward1_linear1_weight_to_fp16_quantized, x = input_237_cast_fp16)[name = string("linear_37_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_241_cast_fp16 = silu(x = linear_37_cast_fp16)[name = string("input_241_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106898624))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111095104))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111092992))))[name = string("encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111096192)))];
+            tensor<fp16, [1, 188, 1024]> linear_38_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_4_feed_forward1_linear2_weight_to_fp16_quantized, x = input_241_cast_fp16)[name = string("linear_38_cast_fp16")];
+            fp16 var_1203_to_fp16 = const()[name = string("op_1203_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1204_cast_fp16 = mul(x = linear_38_cast_fp16, y = var_1203_to_fp16)[name = string("op_1204_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_247_cast_fp16 = add(x = input_235_cast_fp16, y = var_1204_cast_fp16)[name = string("input_247_cast_fp16")];
+            tensor<int32, [1]> query_9_axes_0 = const()[name = string("query_9_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111098304)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111100416)))];
+            tensor<fp16, [1, 188, 1024]> query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = encoder_module_layers_4_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_self_att_weight_to_fp16, x = input_247_cast_fp16)[name = string("query_9_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111102528))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112153280))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112151168))))[name = string("encoder_module_layers_4_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112154368)))];
+            tensor<fp16, [1, 188, 1024]> linear_39_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_q_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = string("linear_39_cast_fp16")];
+            tensor<int32, [4]> var_1221 = const()[name = string("op_1221"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_25_cast_fp16 = reshape(shape = var_1221, x = linear_39_cast_fp16)[name = string("q_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112156480))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113207232))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113205120))))[name = string("encoder_module_layers_4_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113208320)))];
+            tensor<fp16, [1, 188, 1024]> linear_40_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_k_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = string("linear_40_cast_fp16")];
+            tensor<int32, [4]> var_1226 = const()[name = string("op_1226"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_17_cast_fp16 = reshape(shape = var_1226, x = linear_40_cast_fp16)[name = string("k_17_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113210432))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114261184))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114259072))))[name = string("encoder_module_layers_4_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114262272)))];
+            tensor<fp16, [1, 188, 1024]> linear_41_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_v_weight_to_fp16_quantized, x = query_9_cast_fp16)[name = string("linear_41_cast_fp16")];
+            tensor<int32, [4]> var_1231 = const()[name = string("op_1231"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_9_cast_fp16 = reshape(shape = var_1231, x = linear_41_cast_fp16)[name = string("v_9_cast_fp16")];
+            tensor<int32, [4]> value_13_perm_0 = const()[name = string("value_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_4_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114264384)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1243_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_module_layers_4_self_attn_pos_bias_u_to_fp16)[name = string("op_1243_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_4_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114266496)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1245_cast_fp16 = add(x = q_25_cast_fp16, y = encoder_module_layers_4_self_attn_pos_bias_v_to_fp16)[name = string("op_1245_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_9_perm_0 = const()[name = string("q_with_bias_v_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_115_transpose_x_0 = const()[name = string("x_115_transpose_x_0"), val = bool(false)];
+            bool x_115_transpose_y_0 = const()[name = string("x_115_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1247_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114268608))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114653504))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114652672))))[name = string("op_1247_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_9_cast_fp16 = transpose(perm = q_with_bias_v_9_perm_0, x = var_1245_cast_fp16)[name = string("transpose_284")];
+            tensor<fp16, [1, 8, 188, 375]> x_115_cast_fp16 = matmul(transpose_x = x_115_transpose_x_0, transpose_y = x_115_transpose_y_0, x = q_with_bias_v_9_cast_fp16, y = op_1247_to_fp16_quantized)[name = string("x_115_cast_fp16")];
+            tensor<int32, [8]> x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_117_mode_0 = const()[name = string("x_117_mode_0"), val = string("constant")];
+            fp16 const_128_to_fp16 = const()[name = string("const_128_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_117_cast_fp16 = pad(constant_val = const_128_to_fp16, mode = x_117_mode_0, pad = x_117_pad_0, x = x_115_cast_fp16)[name = string("x_117_cast_fp16")];
+            tensor<int32, [4]> var_1255 = const()[name = string("op_1255"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_119_cast_fp16 = reshape(shape = var_1255, x = x_117_cast_fp16)[name = string("x_119_cast_fp16")];
+            tensor<int32, [4]> var_1259_begin_0 = const()[name = string("op_1259_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1259_end_0 = const()[name = string("op_1259_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1259_end_mask_0 = const()[name = string("op_1259_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1259_cast_fp16 = slice_by_index(begin = var_1259_begin_0, end = var_1259_end_0, end_mask = var_1259_end_mask_0, x = x_119_cast_fp16)[name = string("op_1259_cast_fp16")];
+            tensor<int32, [4]> var_1260 = const()[name = string("op_1260"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_17_cast_fp16 = reshape(shape = var_1260, x = var_1259_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<int32, [4]> transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_105 = transpose(perm = transpose_105_perm_0, x = k_17_cast_fp16)[name = string("transpose_282")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_104 = transpose(perm = transpose_104_perm_0, x = var_1243_cast_fp16)[name = string("transpose_283")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_19_end_0 = const()[name = string("matrix_bd_19_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1269_cast_fp16 = add(x = matrix_ac_9_cast_fp16, y = matrix_bd_19_cast_fp16)[name = string("op_1269_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<fp16, [1, 8, 188, 188]> _inversed_scores_17_cast_fp16 = mul(x = var_1269_cast_fp16, y = _inversed_scores_17_y_0_to_fp16)[name = string("_inversed_scores_17_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_19_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_17_cast_fp16, cond = mask_15)[name = string("scores_19_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1275_cast_fp16 = softmax(axis = var_152, x = scores_19_cast_fp16)[name = string("op_1275_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_249_cast_fp16 = select(a = var_164_to_fp16, b = var_1275_cast_fp16, cond = mask_15)[name = string("input_249_cast_fp16")];
+            bool x_121_transpose_x_0 = const()[name = string("x_121_transpose_x_0"), val = bool(false)];
+            bool x_121_transpose_y_0 = const()[name = string("x_121_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_13_cast_fp16 = transpose(perm = value_13_perm_0, x = v_9_cast_fp16)[name = string("transpose_281")];
+            tensor<fp16, [1, 8, 188, 128]> x_121_cast_fp16 = matmul(transpose_x = x_121_transpose_x_0, transpose_y = x_121_transpose_y_0, x = input_249_cast_fp16, y = value_13_cast_fp16)[name = string("x_121_cast_fp16")];
+            tensor<int32, [4]> var_1279_perm_0 = const()[name = string("op_1279_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1280 = const()[name = string("op_1280"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1279_cast_fp16 = transpose(perm = var_1279_perm_0, x = x_121_cast_fp16)[name = string("transpose_280")];
+            tensor<fp16, [1, 188, 1024]> input_251_cast_fp16 = reshape(shape = var_1280, x = var_1279_cast_fp16)[name = string("input_251_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_4_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114653952))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115704704))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115702592))))[name = string("encoder_module_layers_4_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_4_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115705792)))];
+            tensor<fp16, [1, 188, 1024]> linear_43_cast_fp16 = linear(bias = encoder_module_layers_4_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_4_self_attn_linear_out_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_43_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_255_cast_fp16 = add(x = input_247_cast_fp16, y = linear_43_cast_fp16)[name = string("input_255_cast_fp16")];
+            tensor<int32, [1]> x_125_axes_0 = const()[name = string("x_125_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115707904)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115710016)))];
+            tensor<fp16, [1, 188, 1024]> x_125_cast_fp16 = layer_norm(axes = x_125_axes_0, beta = encoder_module_layers_4_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_conv_weight_to_fp16, x = input_255_cast_fp16)[name = string("x_125_cast_fp16")];
+            tensor<int32, [3]> input_257_perm_0 = const()[name = string("input_257_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_259_pad_type_0 = const()[name = string("input_259_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_259_strides_0 = const()[name = string("input_259_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_259_pad_0 = const()[name = string("input_259_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_259_dilations_0 = const()[name = string("input_259_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_259_groups_0 = const()[name = string("input_259_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115712128))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117813504))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117809344))))[name = string("encoder_module_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117815616)))];
+            tensor<fp16, [1, 1024, 188]> input_257_cast_fp16 = transpose(perm = input_257_perm_0, x = x_125_cast_fp16)[name = string("transpose_279")];
+            tensor<fp16, [1, 2048, 188]> input_259_cast_fp16 = conv(bias = encoder_module_layers_4_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_4_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_257_cast_fp16)[name = string("input_259_cast_fp16")];
+            int32 x_127_split_num_splits_0 = const()[name = string("x_127_split_num_splits_0"), val = int32(2)];
+            int32 x_127_split_axis_0 = const()[name = string("x_127_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_127_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_127_split_cast_fp16_1 = split(axis = x_127_split_axis_0, num_splits = x_127_split_num_splits_0, x = input_259_cast_fp16)[name = string("x_127_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_127_split_1_sigmoid_cast_fp16 = sigmoid(x = x_127_split_cast_fp16_1)[name = string("x_127_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_127_cast_fp16 = mul(x = x_127_split_cast_fp16_0, y = x_127_split_1_sigmoid_cast_fp16)[name = string("x_127_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_261_cast_fp16 = select(a = var_164_to_fp16, b = x_127_cast_fp16, cond = var_608)[name = string("input_261_cast_fp16")];
+            tensor<int32, [6]> input_263_pad_0 = const()[name = string("input_263_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_263_mode_0 = const()[name = string("input_263_mode_0"), val = string("constant")];
+            fp16 const_131_to_fp16 = const()[name = string("const_131_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_263_cast_fp16 = pad(constant_val = const_131_to_fp16, mode = input_263_mode_0, pad = input_263_pad_0, x = input_261_cast_fp16)[name = string("input_263_cast_fp16")];
+            string input_265_pad_type_0 = const()[name = string("input_265_pad_type_0"), val = string("valid")];
+            int32 input_265_groups_0 = const()[name = string("input_265_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_265_strides_0 = const()[name = string("input_265_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_265_pad_0 = const()[name = string("input_265_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_265_dilations_0 = const()[name = string("input_265_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_330_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117819776))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117831168))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117829056))))[name = string("const_330_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_331_to_fp16 = const()[name = string("const_331_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117832256)))];
+            tensor<fp16, [1, 1024, 188]> input_267_cast_fp16 = conv(bias = const_331_to_fp16, dilations = input_265_dilations_0, groups = input_265_groups_0, pad = input_265_pad_0, pad_type = input_265_pad_type_0, strides = input_265_strides_0, weight = const_330_to_fp16_quantized, x = input_263_cast_fp16)[name = string("input_267_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_269_cast_fp16 = silu(x = input_267_cast_fp16)[name = string("input_269_cast_fp16")];
+            string x_129_pad_type_0 = const()[name = string("x_129_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_129_strides_0 = const()[name = string("x_129_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_129_pad_0 = const()[name = string("x_129_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_129_dilations_0 = const()[name = string("x_129_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_129_groups_0 = const()[name = string("x_129_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117834368))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118885120))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118883008))))[name = string("encoder_module_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118886208)))];
+            tensor<fp16, [1, 1024, 188]> x_129_cast_fp16 = conv(bias = encoder_module_layers_4_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_4_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_269_cast_fp16)[name = string("x_129_cast_fp16")];
+            tensor<int32, [3]> input_271_perm_0 = const()[name = string("input_271_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_271_cast_fp16 = transpose(perm = input_271_perm_0, x = x_129_cast_fp16)[name = string("transpose_278")];
+            tensor<fp16, [1, 188, 1024]> input_273_cast_fp16 = add(x = input_255_cast_fp16, y = input_271_cast_fp16)[name = string("input_273_cast_fp16")];
+            tensor<int32, [1]> input_275_axes_0 = const()[name = string("input_275_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118888320)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118890432)))];
+            tensor<fp16, [1, 188, 1024]> input_275_cast_fp16 = layer_norm(axes = input_275_axes_0, beta = encoder_module_layers_4_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_feed_forward2_weight_to_fp16, x = input_273_cast_fp16)[name = string("input_275_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(118892544))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123095168))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123086912))))[name = string("encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123099328)))];
+            tensor<fp16, [1, 188, 4096]> linear_44_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_4_feed_forward2_linear1_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_44_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_279_cast_fp16 = silu(x = linear_44_cast_fp16)[name = string("input_279_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123107584))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127304064))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127301952))))[name = string("encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127305152)))];
+            tensor<fp16, [1, 188, 1024]> linear_45_cast_fp16 = linear(bias = encoder_module_layers_4_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_4_feed_forward2_linear2_weight_to_fp16_quantized, x = input_279_cast_fp16)[name = string("linear_45_cast_fp16")];
+            fp16 var_1346_to_fp16 = const()[name = string("op_1346_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1347_cast_fp16 = mul(x = linear_45_cast_fp16, y = var_1346_to_fp16)[name = string("op_1347_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_285_cast_fp16 = add(x = input_273_cast_fp16, y = var_1347_cast_fp16)[name = string("input_285_cast_fp16")];
+            tensor<int32, [1]> input_287_axes_0 = const()[name = string("input_287_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_4_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127307264)))];
+            tensor<fp16, [1024]> encoder_module_layers_4_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_4_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127309376)))];
+            tensor<fp16, [1, 188, 1024]> input_287_cast_fp16 = layer_norm(axes = input_287_axes_0, beta = encoder_module_layers_4_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_4_norm_out_weight_to_fp16, x = input_285_cast_fp16)[name = string("input_287_cast_fp16")];
+            tensor<int32, [1]> input_289_axes_0 = const()[name = string("input_289_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127311488)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127313600)))];
+            tensor<fp16, [1, 188, 1024]> input_289_cast_fp16 = layer_norm(axes = input_289_axes_0, beta = encoder_module_layers_5_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_feed_forward1_weight_to_fp16, x = input_287_cast_fp16)[name = string("input_289_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127315712))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131518336))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131510080))))[name = string("encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131522496)))];
+            tensor<fp16, [1, 188, 4096]> linear_46_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_5_feed_forward1_linear1_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_46_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_293_cast_fp16 = silu(x = linear_46_cast_fp16)[name = string("input_293_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(131530752))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135727232))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135725120))))[name = string("encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135728320)))];
+            tensor<fp16, [1, 188, 1024]> linear_47_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_5_feed_forward1_linear2_weight_to_fp16_quantized, x = input_293_cast_fp16)[name = string("linear_47_cast_fp16")];
+            fp16 var_1377_to_fp16 = const()[name = string("op_1377_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1378_cast_fp16 = mul(x = linear_47_cast_fp16, y = var_1377_to_fp16)[name = string("op_1378_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_299_cast_fp16 = add(x = input_287_cast_fp16, y = var_1378_cast_fp16)[name = string("input_299_cast_fp16")];
+            tensor<int32, [1]> query_11_axes_0 = const()[name = string("query_11_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135730432)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135732544)))];
+            tensor<fp16, [1, 188, 1024]> query_11_cast_fp16 = layer_norm(axes = query_11_axes_0, beta = encoder_module_layers_5_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_self_att_weight_to_fp16, x = input_299_cast_fp16)[name = string("query_11_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(135734656))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136785408))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136783296))))[name = string("encoder_module_layers_5_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136786496)))];
+            tensor<fp16, [1, 188, 1024]> linear_48_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_q_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = string("linear_48_cast_fp16")];
+            tensor<int32, [4]> var_1395 = const()[name = string("op_1395"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_31_cast_fp16 = reshape(shape = var_1395, x = linear_48_cast_fp16)[name = string("q_31_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136788608))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137839360))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137837248))))[name = string("encoder_module_layers_5_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137840448)))];
+            tensor<fp16, [1, 188, 1024]> linear_49_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_k_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = string("linear_49_cast_fp16")];
+            tensor<int32, [4]> var_1400 = const()[name = string("op_1400"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_21_cast_fp16 = reshape(shape = var_1400, x = linear_49_cast_fp16)[name = string("k_21_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137842560))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138893312))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138891200))))[name = string("encoder_module_layers_5_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138894400)))];
+            tensor<fp16, [1, 188, 1024]> linear_50_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_v_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = string("linear_50_cast_fp16")];
+            tensor<int32, [4]> var_1405 = const()[name = string("op_1405"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_11_cast_fp16 = reshape(shape = var_1405, x = linear_50_cast_fp16)[name = string("v_11_cast_fp16")];
+            tensor<int32, [4]> value_15_perm_0 = const()[name = string("value_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_5_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138896512)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1417_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_module_layers_5_self_attn_pos_bias_u_to_fp16)[name = string("op_1417_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_5_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138898624)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1419_cast_fp16 = add(x = q_31_cast_fp16, y = encoder_module_layers_5_self_attn_pos_bias_v_to_fp16)[name = string("op_1419_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_11_perm_0 = const()[name = string("q_with_bias_v_11_perm_0"), val = tensor<int32, [4]>([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<fp16, [1, 8, 128, 375]> op_1421_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138900736))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139285632))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139284800))))[name = string("op_1421_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_11_cast_fp16 = transpose(perm = q_with_bias_v_11_perm_0, x = var_1419_cast_fp16)[name = string("transpose_277")];
+            tensor<fp16, [1, 8, 188, 375]> 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_1421_to_fp16_quantized)[name = string("x_137_cast_fp16")];
+            tensor<int32, [8]> x_139_pad_0 = const()[name = string("x_139_pad_0"), val = tensor<int32, [8]>([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_138_to_fp16 = const()[name = string("const_138_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_139_cast_fp16 = pad(constant_val = const_138_to_fp16, mode = x_139_mode_0, pad = x_139_pad_0, x = x_137_cast_fp16)[name = string("x_139_cast_fp16")];
+            tensor<int32, [4]> var_1429 = const()[name = string("op_1429"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_141_cast_fp16 = reshape(shape = var_1429, x = x_139_cast_fp16)[name = string("x_141_cast_fp16")];
+            tensor<int32, [4]> var_1433_begin_0 = const()[name = string("op_1433_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1433_end_0 = const()[name = string("op_1433_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1433_end_mask_0 = const()[name = string("op_1433_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1433_cast_fp16 = slice_by_index(begin = var_1433_begin_0, end = var_1433_end_0, end_mask = var_1433_end_mask_0, x = x_141_cast_fp16)[name = string("op_1433_cast_fp16")];
+            tensor<int32, [4]> var_1434 = const()[name = string("op_1434"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_21_cast_fp16 = reshape(shape = var_1434, x = var_1433_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<int32, [4]> transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_107 = transpose(perm = transpose_107_perm_0, x = k_21_cast_fp16)[name = string("transpose_275")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1417_cast_fp16)[name = string("transpose_276")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_23_end_0 = const()[name = string("matrix_bd_23_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1443_cast_fp16 = add(x = matrix_ac_11_cast_fp16, y = matrix_bd_23_cast_fp16)[name = string("op_1443_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<fp16, [1, 8, 188, 188]> _inversed_scores_21_cast_fp16 = mul(x = var_1443_cast_fp16, y = _inversed_scores_21_y_0_to_fp16)[name = string("_inversed_scores_21_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_23_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_21_cast_fp16, cond = mask_15)[name = string("scores_23_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1449_cast_fp16 = softmax(axis = var_152, x = scores_23_cast_fp16)[name = string("op_1449_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_301_cast_fp16 = select(a = var_164_to_fp16, b = var_1449_cast_fp16, cond = mask_15)[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<fp16, [1, 8, 188, 128]> value_15_cast_fp16 = transpose(perm = value_15_perm_0, x = v_11_cast_fp16)[name = string("transpose_274")];
+            tensor<fp16, [1, 8, 188, 128]> 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_15_cast_fp16)[name = string("x_143_cast_fp16")];
+            tensor<int32, [4]> var_1453_perm_0 = const()[name = string("op_1453_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1454 = const()[name = string("op_1454"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1453_cast_fp16 = transpose(perm = var_1453_perm_0, x = x_143_cast_fp16)[name = string("transpose_273")];
+            tensor<fp16, [1, 188, 1024]> input_303_cast_fp16 = reshape(shape = var_1454, x = var_1453_cast_fp16)[name = string("input_303_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_5_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139286080))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140336832))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140334720))))[name = string("encoder_module_layers_5_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_5_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140337920)))];
+            tensor<fp16, [1, 188, 1024]> linear_52_cast_fp16 = linear(bias = encoder_module_layers_5_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_5_self_attn_linear_out_weight_to_fp16_quantized, x = input_303_cast_fp16)[name = string("linear_52_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_307_cast_fp16 = add(x = input_299_cast_fp16, y = linear_52_cast_fp16)[name = string("input_307_cast_fp16")];
+            tensor<int32, [1]> x_147_axes_0 = const()[name = string("x_147_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140340032)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140342144)))];
+            tensor<fp16, [1, 188, 1024]> x_147_cast_fp16 = layer_norm(axes = x_147_axes_0, beta = encoder_module_layers_5_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_conv_weight_to_fp16, x = input_307_cast_fp16)[name = string("x_147_cast_fp16")];
+            tensor<int32, [3]> input_309_perm_0 = const()[name = string("input_309_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_311_pad_type_0 = const()[name = string("input_311_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_311_strides_0 = const()[name = string("input_311_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_311_pad_0 = const()[name = string("input_311_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_311_dilations_0 = const()[name = string("input_311_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_311_groups_0 = const()[name = string("input_311_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140344256))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142445632))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142441472))))[name = string("encoder_module_layers_5_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142447744)))];
+            tensor<fp16, [1, 1024, 188]> input_309_cast_fp16 = transpose(perm = input_309_perm_0, x = x_147_cast_fp16)[name = string("transpose_272")];
+            tensor<fp16, [1, 2048, 188]> input_311_cast_fp16 = conv(bias = encoder_module_layers_5_conv_pointwise_conv1_bias_to_fp16, 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_module_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<fp16, [1, 1024, 188]> x_149_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> input_313_cast_fp16 = select(a = var_164_to_fp16, b = x_149_cast_fp16, cond = var_608)[name = string("input_313_cast_fp16")];
+            tensor<int32, [6]> input_315_pad_0 = const()[name = string("input_315_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_315_mode_0 = const()[name = string("input_315_mode_0"), val = string("constant")];
+            fp16 const_141_to_fp16 = const()[name = string("const_141_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_315_cast_fp16 = pad(constant_val = const_141_to_fp16, mode = input_315_mode_0, pad = input_315_pad_0, x = input_313_cast_fp16)[name = string("input_315_cast_fp16")];
+            string input_317_pad_type_0 = const()[name = string("input_317_pad_type_0"), val = string("valid")];
+            int32 input_317_groups_0 = const()[name = string("input_317_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_317_strides_0 = const()[name = string("input_317_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_317_pad_0 = const()[name = string("input_317_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_317_dilations_0 = const()[name = string("input_317_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_332_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142451904))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142463296))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142461184))))[name = string("const_332_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_333_to_fp16 = const()[name = string("const_333_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142464384)))];
+            tensor<fp16, [1, 1024, 188]> input_319_cast_fp16 = conv(bias = const_333_to_fp16, dilations = input_317_dilations_0, groups = input_317_groups_0, pad = input_317_pad_0, pad_type = input_317_pad_type_0, strides = input_317_strides_0, weight = const_332_to_fp16_quantized, x = input_315_cast_fp16)[name = string("input_319_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_321_cast_fp16 = silu(x = input_319_cast_fp16)[name = string("input_321_cast_fp16")];
+            string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142466496))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143517248))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143515136))))[name = string("encoder_module_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143518336)))];
+            tensor<fp16, [1, 1024, 188]> x_151_cast_fp16 = conv(bias = encoder_module_layers_5_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_5_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_321_cast_fp16)[name = string("x_151_cast_fp16")];
+            tensor<int32, [3]> input_323_perm_0 = const()[name = string("input_323_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_323_cast_fp16 = transpose(perm = input_323_perm_0, x = x_151_cast_fp16)[name = string("transpose_271")];
+            tensor<fp16, [1, 188, 1024]> input_325_cast_fp16 = add(x = input_307_cast_fp16, y = input_323_cast_fp16)[name = string("input_325_cast_fp16")];
+            tensor<int32, [1]> input_327_axes_0 = const()[name = string("input_327_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143520448)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143522560)))];
+            tensor<fp16, [1, 188, 1024]> input_327_cast_fp16 = layer_norm(axes = input_327_axes_0, beta = encoder_module_layers_5_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_feed_forward2_weight_to_fp16, x = input_325_cast_fp16)[name = string("input_327_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(143524672))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147727296))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147719040))))[name = string("encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147731456)))];
+            tensor<fp16, [1, 188, 4096]> linear_53_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_5_feed_forward2_linear1_weight_to_fp16_quantized, x = input_327_cast_fp16)[name = string("linear_53_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_331_cast_fp16 = silu(x = linear_53_cast_fp16)[name = string("input_331_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147739712))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151936192))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151934080))))[name = string("encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151937280)))];
+            tensor<fp16, [1, 188, 1024]> linear_54_cast_fp16 = linear(bias = encoder_module_layers_5_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_5_feed_forward2_linear2_weight_to_fp16_quantized, x = input_331_cast_fp16)[name = string("linear_54_cast_fp16")];
+            fp16 var_1520_to_fp16 = const()[name = string("op_1520_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1521_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_1520_to_fp16)[name = string("op_1521_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_337_cast_fp16 = add(x = input_325_cast_fp16, y = var_1521_cast_fp16)[name = string("input_337_cast_fp16")];
+            tensor<int32, [1]> input_339_axes_0 = const()[name = string("input_339_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_5_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151939392)))];
+            tensor<fp16, [1024]> encoder_module_layers_5_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_5_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151941504)))];
+            tensor<fp16, [1, 188, 1024]> input_339_cast_fp16 = layer_norm(axes = input_339_axes_0, beta = encoder_module_layers_5_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_5_norm_out_weight_to_fp16, x = input_337_cast_fp16)[name = string("input_339_cast_fp16")];
+            tensor<int32, [1]> input_341_axes_0 = const()[name = string("input_341_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151943616)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151945728)))];
+            tensor<fp16, [1, 188, 1024]> input_341_cast_fp16 = layer_norm(axes = input_341_axes_0, beta = encoder_module_layers_6_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_feed_forward1_weight_to_fp16, x = input_339_cast_fp16)[name = string("input_341_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151947840))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156150464))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156142208))))[name = string("encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156154624)))];
+            tensor<fp16, [1, 188, 4096]> linear_55_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_6_feed_forward1_linear1_weight_to_fp16_quantized, x = input_341_cast_fp16)[name = string("linear_55_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_345_cast_fp16 = silu(x = linear_55_cast_fp16)[name = string("input_345_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156162880))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160359360))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160357248))))[name = string("encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160360448)))];
+            tensor<fp16, [1, 188, 1024]> linear_56_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_6_feed_forward1_linear2_weight_to_fp16_quantized, x = input_345_cast_fp16)[name = string("linear_56_cast_fp16")];
+            fp16 var_1551_to_fp16 = const()[name = string("op_1551_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1552_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_1551_to_fp16)[name = string("op_1552_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_351_cast_fp16 = add(x = input_339_cast_fp16, y = var_1552_cast_fp16)[name = string("input_351_cast_fp16")];
+            tensor<int32, [1]> query_13_axes_0 = const()[name = string("query_13_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160362560)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160364672)))];
+            tensor<fp16, [1, 188, 1024]> query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = encoder_module_layers_6_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_self_att_weight_to_fp16, x = input_351_cast_fp16)[name = string("query_13_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160366784))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161417536))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161415424))))[name = string("encoder_module_layers_6_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161418624)))];
+            tensor<fp16, [1, 188, 1024]> linear_57_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_q_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = string("linear_57_cast_fp16")];
+            tensor<int32, [4]> var_1569 = const()[name = string("op_1569"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_37_cast_fp16 = reshape(shape = var_1569, x = linear_57_cast_fp16)[name = string("q_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(161420736))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162471488))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162469376))))[name = string("encoder_module_layers_6_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162472576)))];
+            tensor<fp16, [1, 188, 1024]> linear_58_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_k_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = string("linear_58_cast_fp16")];
+            tensor<int32, [4]> var_1574 = const()[name = string("op_1574"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_25_cast_fp16 = reshape(shape = var_1574, x = linear_58_cast_fp16)[name = string("k_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(162474688))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163525440))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163523328))))[name = string("encoder_module_layers_6_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163526528)))];
+            tensor<fp16, [1, 188, 1024]> linear_59_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_v_weight_to_fp16_quantized, x = query_13_cast_fp16)[name = string("linear_59_cast_fp16")];
+            tensor<int32, [4]> var_1579 = const()[name = string("op_1579"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_13_cast_fp16 = reshape(shape = var_1579, x = linear_59_cast_fp16)[name = string("v_13_cast_fp16")];
+            tensor<int32, [4]> value_17_perm_0 = const()[name = string("value_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_6_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163528640)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1591_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_module_layers_6_self_attn_pos_bias_u_to_fp16)[name = string("op_1591_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_6_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163530752)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1593_cast_fp16 = add(x = q_37_cast_fp16, y = encoder_module_layers_6_self_attn_pos_bias_v_to_fp16)[name = string("op_1593_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_13_perm_0 = const()[name = string("q_with_bias_v_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_159_transpose_x_0 = const()[name = string("x_159_transpose_x_0"), val = bool(false)];
+            bool x_159_transpose_y_0 = const()[name = string("x_159_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1595_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163532864))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163917760))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163916928))))[name = string("op_1595_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_13_cast_fp16 = transpose(perm = q_with_bias_v_13_perm_0, x = var_1593_cast_fp16)[name = string("transpose_270")];
+            tensor<fp16, [1, 8, 188, 375]> x_159_cast_fp16 = matmul(transpose_x = x_159_transpose_x_0, transpose_y = x_159_transpose_y_0, x = q_with_bias_v_13_cast_fp16, y = op_1595_to_fp16_quantized)[name = string("x_159_cast_fp16")];
+            tensor<int32, [8]> x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_161_mode_0 = const()[name = string("x_161_mode_0"), val = string("constant")];
+            fp16 const_148_to_fp16 = const()[name = string("const_148_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_161_cast_fp16 = pad(constant_val = const_148_to_fp16, mode = x_161_mode_0, pad = x_161_pad_0, x = x_159_cast_fp16)[name = string("x_161_cast_fp16")];
+            tensor<int32, [4]> var_1603 = const()[name = string("op_1603"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_163_cast_fp16 = reshape(shape = var_1603, x = x_161_cast_fp16)[name = string("x_163_cast_fp16")];
+            tensor<int32, [4]> var_1607_begin_0 = const()[name = string("op_1607_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1607_end_0 = const()[name = string("op_1607_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1607_end_mask_0 = const()[name = string("op_1607_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1607_cast_fp16 = slice_by_index(begin = var_1607_begin_0, end = var_1607_end_0, end_mask = var_1607_end_mask_0, x = x_163_cast_fp16)[name = string("op_1607_cast_fp16")];
+            tensor<int32, [4]> var_1608 = const()[name = string("op_1608"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_25_cast_fp16 = reshape(shape = var_1608, x = var_1607_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<int32, [4]> transpose_108_perm_0 = const()[name = string("transpose_108_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_109_perm_0 = const()[name = string("transpose_109_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_109 = transpose(perm = transpose_109_perm_0, x = k_25_cast_fp16)[name = string("transpose_268")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_108 = transpose(perm = transpose_108_perm_0, x = var_1591_cast_fp16)[name = string("transpose_269")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_27_end_0 = const()[name = string("matrix_bd_27_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1617_cast_fp16 = add(x = matrix_ac_13_cast_fp16, y = matrix_bd_27_cast_fp16)[name = string("op_1617_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<fp16, [1, 8, 188, 188]> _inversed_scores_25_cast_fp16 = mul(x = var_1617_cast_fp16, y = _inversed_scores_25_y_0_to_fp16)[name = string("_inversed_scores_25_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_27_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_25_cast_fp16, cond = mask_15)[name = string("scores_27_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1623_cast_fp16 = softmax(axis = var_152, x = scores_27_cast_fp16)[name = string("op_1623_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_353_cast_fp16 = select(a = var_164_to_fp16, b = var_1623_cast_fp16, cond = mask_15)[name = string("input_353_cast_fp16")];
+            bool x_165_transpose_x_0 = const()[name = string("x_165_transpose_x_0"), val = bool(false)];
+            bool x_165_transpose_y_0 = const()[name = string("x_165_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_17_cast_fp16 = transpose(perm = value_17_perm_0, x = v_13_cast_fp16)[name = string("transpose_267")];
+            tensor<fp16, [1, 8, 188, 128]> x_165_cast_fp16 = matmul(transpose_x = x_165_transpose_x_0, transpose_y = x_165_transpose_y_0, x = input_353_cast_fp16, y = value_17_cast_fp16)[name = string("x_165_cast_fp16")];
+            tensor<int32, [4]> var_1627_perm_0 = const()[name = string("op_1627_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1628 = const()[name = string("op_1628"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1627_cast_fp16 = transpose(perm = var_1627_perm_0, x = x_165_cast_fp16)[name = string("transpose_266")];
+            tensor<fp16, [1, 188, 1024]> input_355_cast_fp16 = reshape(shape = var_1628, x = var_1627_cast_fp16)[name = string("input_355_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_6_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163918208))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164968960))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164966848))))[name = string("encoder_module_layers_6_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_6_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164970048)))];
+            tensor<fp16, [1, 188, 1024]> linear_61_cast_fp16 = linear(bias = encoder_module_layers_6_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_6_self_attn_linear_out_weight_to_fp16_quantized, x = input_355_cast_fp16)[name = string("linear_61_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_359_cast_fp16 = add(x = input_351_cast_fp16, y = linear_61_cast_fp16)[name = string("input_359_cast_fp16")];
+            tensor<int32, [1]> x_169_axes_0 = const()[name = string("x_169_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164972160)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164974272)))];
+            tensor<fp16, [1, 188, 1024]> x_169_cast_fp16 = layer_norm(axes = x_169_axes_0, beta = encoder_module_layers_6_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_conv_weight_to_fp16, x = input_359_cast_fp16)[name = string("x_169_cast_fp16")];
+            tensor<int32, [3]> input_361_perm_0 = const()[name = string("input_361_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_363_pad_type_0 = const()[name = string("input_363_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_363_strides_0 = const()[name = string("input_363_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_363_pad_0 = const()[name = string("input_363_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_363_dilations_0 = const()[name = string("input_363_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_363_groups_0 = const()[name = string("input_363_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164976384))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167077760))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167073600))))[name = string("encoder_module_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167079872)))];
+            tensor<fp16, [1, 1024, 188]> input_361_cast_fp16 = transpose(perm = input_361_perm_0, x = x_169_cast_fp16)[name = string("transpose_265")];
+            tensor<fp16, [1, 2048, 188]> input_363_cast_fp16 = conv(bias = encoder_module_layers_6_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_6_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_361_cast_fp16)[name = string("input_363_cast_fp16")];
+            int32 x_171_split_num_splits_0 = const()[name = string("x_171_split_num_splits_0"), val = int32(2)];
+            int32 x_171_split_axis_0 = const()[name = string("x_171_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_171_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_171_split_cast_fp16_1 = split(axis = x_171_split_axis_0, num_splits = x_171_split_num_splits_0, x = input_363_cast_fp16)[name = string("x_171_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_171_split_1_sigmoid_cast_fp16 = sigmoid(x = x_171_split_cast_fp16_1)[name = string("x_171_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_171_cast_fp16 = mul(x = x_171_split_cast_fp16_0, y = x_171_split_1_sigmoid_cast_fp16)[name = string("x_171_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_365_cast_fp16 = select(a = var_164_to_fp16, b = x_171_cast_fp16, cond = var_608)[name = string("input_365_cast_fp16")];
+            tensor<int32, [6]> input_367_pad_0 = const()[name = string("input_367_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_367_mode_0 = const()[name = string("input_367_mode_0"), val = string("constant")];
+            fp16 const_151_to_fp16 = const()[name = string("const_151_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_367_cast_fp16 = pad(constant_val = const_151_to_fp16, mode = input_367_mode_0, pad = input_367_pad_0, x = input_365_cast_fp16)[name = string("input_367_cast_fp16")];
+            string input_369_pad_type_0 = const()[name = string("input_369_pad_type_0"), val = string("valid")];
+            int32 input_369_groups_0 = const()[name = string("input_369_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_369_strides_0 = const()[name = string("input_369_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_369_pad_0 = const()[name = string("input_369_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_369_dilations_0 = const()[name = string("input_369_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_334_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167084032))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167095424))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167093312))))[name = string("const_334_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_335_to_fp16 = const()[name = string("const_335_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167096512)))];
+            tensor<fp16, [1, 1024, 188]> input_371_cast_fp16 = conv(bias = const_335_to_fp16, dilations = input_369_dilations_0, groups = input_369_groups_0, pad = input_369_pad_0, pad_type = input_369_pad_type_0, strides = input_369_strides_0, weight = const_334_to_fp16_quantized, x = input_367_cast_fp16)[name = string("input_371_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_373_cast_fp16 = silu(x = input_371_cast_fp16)[name = string("input_373_cast_fp16")];
+            string x_173_pad_type_0 = const()[name = string("x_173_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_173_strides_0 = const()[name = string("x_173_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_173_pad_0 = const()[name = string("x_173_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_173_dilations_0 = const()[name = string("x_173_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_173_groups_0 = const()[name = string("x_173_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167098624))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168149376))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168147264))))[name = string("encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168150464)))];
+            tensor<fp16, [1, 1024, 188]> x_173_cast_fp16 = conv(bias = encoder_module_layers_6_conv_pointwise_conv2_bias_to_fp16, dilations = x_173_dilations_0, groups = x_173_groups_0, pad = x_173_pad_0, pad_type = x_173_pad_type_0, strides = x_173_strides_0, weight = encoder_module_layers_6_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_373_cast_fp16)[name = string("x_173_cast_fp16")];
+            tensor<int32, [3]> input_375_perm_0 = const()[name = string("input_375_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_375_cast_fp16 = transpose(perm = input_375_perm_0, x = x_173_cast_fp16)[name = string("transpose_264")];
+            tensor<fp16, [1, 188, 1024]> input_377_cast_fp16 = add(x = input_359_cast_fp16, y = input_375_cast_fp16)[name = string("input_377_cast_fp16")];
+            tensor<int32, [1]> input_379_axes_0 = const()[name = string("input_379_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168152576)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168154688)))];
+            tensor<fp16, [1, 188, 1024]> input_379_cast_fp16 = layer_norm(axes = input_379_axes_0, beta = encoder_module_layers_6_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_feed_forward2_weight_to_fp16, x = input_377_cast_fp16)[name = string("input_379_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(168156800))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172359424))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172351168))))[name = string("encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172363584)))];
+            tensor<fp16, [1, 188, 4096]> linear_62_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_6_feed_forward2_linear1_weight_to_fp16_quantized, x = input_379_cast_fp16)[name = string("linear_62_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_383_cast_fp16 = silu(x = linear_62_cast_fp16)[name = string("input_383_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172371840))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176568320))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176566208))))[name = string("encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176569408)))];
+            tensor<fp16, [1, 188, 1024]> linear_63_cast_fp16 = linear(bias = encoder_module_layers_6_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_6_feed_forward2_linear2_weight_to_fp16_quantized, x = input_383_cast_fp16)[name = string("linear_63_cast_fp16")];
+            fp16 var_1694_to_fp16 = const()[name = string("op_1694_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1695_cast_fp16 = mul(x = linear_63_cast_fp16, y = var_1694_to_fp16)[name = string("op_1695_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_389_cast_fp16 = add(x = input_377_cast_fp16, y = var_1695_cast_fp16)[name = string("input_389_cast_fp16")];
+            tensor<int32, [1]> input_391_axes_0 = const()[name = string("input_391_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_6_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176571520)))];
+            tensor<fp16, [1024]> encoder_module_layers_6_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_6_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176573632)))];
+            tensor<fp16, [1, 188, 1024]> input_391_cast_fp16 = layer_norm(axes = input_391_axes_0, beta = encoder_module_layers_6_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_6_norm_out_weight_to_fp16, x = input_389_cast_fp16)[name = string("input_391_cast_fp16")];
+            tensor<int32, [1]> input_393_axes_0 = const()[name = string("input_393_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176575744)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176577856)))];
+            tensor<fp16, [1, 188, 1024]> input_393_cast_fp16 = layer_norm(axes = input_393_axes_0, beta = encoder_module_layers_7_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_feed_forward1_weight_to_fp16, x = input_391_cast_fp16)[name = string("input_393_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176579968))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180782592))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180774336))))[name = string("encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180786752)))];
+            tensor<fp16, [1, 188, 4096]> linear_64_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_7_feed_forward1_linear1_weight_to_fp16_quantized, x = input_393_cast_fp16)[name = string("linear_64_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_397_cast_fp16 = silu(x = linear_64_cast_fp16)[name = string("input_397_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180795008))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184991488))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184989376))))[name = string("encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184992576)))];
+            tensor<fp16, [1, 188, 1024]> linear_65_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_7_feed_forward1_linear2_weight_to_fp16_quantized, x = input_397_cast_fp16)[name = string("linear_65_cast_fp16")];
+            fp16 var_1725_to_fp16 = const()[name = string("op_1725_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1726_cast_fp16 = mul(x = linear_65_cast_fp16, y = var_1725_to_fp16)[name = string("op_1726_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_403_cast_fp16 = add(x = input_391_cast_fp16, y = var_1726_cast_fp16)[name = string("input_403_cast_fp16")];
+            tensor<int32, [1]> query_15_axes_0 = const()[name = string("query_15_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184994688)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184996800)))];
+            tensor<fp16, [1, 188, 1024]> query_15_cast_fp16 = layer_norm(axes = query_15_axes_0, beta = encoder_module_layers_7_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_self_att_weight_to_fp16, x = input_403_cast_fp16)[name = string("query_15_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184998912))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186049664))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186047552))))[name = string("encoder_module_layers_7_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186050752)))];
+            tensor<fp16, [1, 188, 1024]> linear_66_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_q_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = string("linear_66_cast_fp16")];
+            tensor<int32, [4]> var_1743 = const()[name = string("op_1743"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_43_cast_fp16 = reshape(shape = var_1743, x = linear_66_cast_fp16)[name = string("q_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186052864))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187103616))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187101504))))[name = string("encoder_module_layers_7_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187104704)))];
+            tensor<fp16, [1, 188, 1024]> linear_67_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_k_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = string("linear_67_cast_fp16")];
+            tensor<int32, [4]> var_1748 = const()[name = string("op_1748"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_29_cast_fp16 = reshape(shape = var_1748, x = linear_67_cast_fp16)[name = string("k_29_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187106816))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188157568))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188155456))))[name = string("encoder_module_layers_7_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188158656)))];
+            tensor<fp16, [1, 188, 1024]> linear_68_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_v_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = string("linear_68_cast_fp16")];
+            tensor<int32, [4]> var_1753 = const()[name = string("op_1753"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_15_cast_fp16 = reshape(shape = var_1753, x = linear_68_cast_fp16)[name = string("v_15_cast_fp16")];
+            tensor<int32, [4]> value_19_perm_0 = const()[name = string("value_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_7_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188160768)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1765_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_module_layers_7_self_attn_pos_bias_u_to_fp16)[name = string("op_1765_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_7_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188162880)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1767_cast_fp16 = add(x = q_43_cast_fp16, y = encoder_module_layers_7_self_attn_pos_bias_v_to_fp16)[name = string("op_1767_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_15_perm_0 = const()[name = string("q_with_bias_v_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_181_transpose_x_0 = const()[name = string("x_181_transpose_x_0"), val = bool(false)];
+            bool x_181_transpose_y_0 = const()[name = string("x_181_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1769_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188164992))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188549888))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188549056))))[name = string("op_1769_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_15_cast_fp16 = transpose(perm = q_with_bias_v_15_perm_0, x = var_1767_cast_fp16)[name = string("transpose_263")];
+            tensor<fp16, [1, 8, 188, 375]> x_181_cast_fp16 = matmul(transpose_x = x_181_transpose_x_0, transpose_y = x_181_transpose_y_0, x = q_with_bias_v_15_cast_fp16, y = op_1769_to_fp16_quantized)[name = string("x_181_cast_fp16")];
+            tensor<int32, [8]> x_183_pad_0 = const()[name = string("x_183_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_183_mode_0 = const()[name = string("x_183_mode_0"), val = string("constant")];
+            fp16 const_158_to_fp16 = const()[name = string("const_158_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_183_cast_fp16 = pad(constant_val = const_158_to_fp16, mode = x_183_mode_0, pad = x_183_pad_0, x = x_181_cast_fp16)[name = string("x_183_cast_fp16")];
+            tensor<int32, [4]> var_1777 = const()[name = string("op_1777"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_185_cast_fp16 = reshape(shape = var_1777, x = x_183_cast_fp16)[name = string("x_185_cast_fp16")];
+            tensor<int32, [4]> var_1781_begin_0 = const()[name = string("op_1781_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1781_end_0 = const()[name = string("op_1781_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1781_end_mask_0 = const()[name = string("op_1781_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1781_cast_fp16 = slice_by_index(begin = var_1781_begin_0, end = var_1781_end_0, end_mask = var_1781_end_mask_0, x = x_185_cast_fp16)[name = string("op_1781_cast_fp16")];
+            tensor<int32, [4]> var_1782 = const()[name = string("op_1782"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_29_cast_fp16 = reshape(shape = var_1782, x = var_1781_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<int32, [4]> transpose_110_perm_0 = const()[name = string("transpose_110_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_111_perm_0 = const()[name = string("transpose_111_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_111 = transpose(perm = transpose_111_perm_0, x = k_29_cast_fp16)[name = string("transpose_261")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_110 = transpose(perm = transpose_110_perm_0, x = var_1765_cast_fp16)[name = string("transpose_262")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_31_end_0 = const()[name = string("matrix_bd_31_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1791_cast_fp16 = add(x = matrix_ac_15_cast_fp16, y = matrix_bd_31_cast_fp16)[name = string("op_1791_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<fp16, [1, 8, 188, 188]> _inversed_scores_29_cast_fp16 = mul(x = var_1791_cast_fp16, y = _inversed_scores_29_y_0_to_fp16)[name = string("_inversed_scores_29_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_31_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_29_cast_fp16, cond = mask_15)[name = string("scores_31_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1797_cast_fp16 = softmax(axis = var_152, x = scores_31_cast_fp16)[name = string("op_1797_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_405_cast_fp16 = select(a = var_164_to_fp16, b = var_1797_cast_fp16, cond = mask_15)[name = string("input_405_cast_fp16")];
+            bool x_187_transpose_x_0 = const()[name = string("x_187_transpose_x_0"), val = bool(false)];
+            bool x_187_transpose_y_0 = const()[name = string("x_187_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_19_cast_fp16 = transpose(perm = value_19_perm_0, x = v_15_cast_fp16)[name = string("transpose_260")];
+            tensor<fp16, [1, 8, 188, 128]> x_187_cast_fp16 = matmul(transpose_x = x_187_transpose_x_0, transpose_y = x_187_transpose_y_0, x = input_405_cast_fp16, y = value_19_cast_fp16)[name = string("x_187_cast_fp16")];
+            tensor<int32, [4]> var_1801_perm_0 = const()[name = string("op_1801_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1802 = const()[name = string("op_1802"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1801_cast_fp16 = transpose(perm = var_1801_perm_0, x = x_187_cast_fp16)[name = string("transpose_259")];
+            tensor<fp16, [1, 188, 1024]> input_407_cast_fp16 = reshape(shape = var_1802, x = var_1801_cast_fp16)[name = string("input_407_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_7_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188550336))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189601088))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189598976))))[name = string("encoder_module_layers_7_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_7_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189602176)))];
+            tensor<fp16, [1, 188, 1024]> linear_70_cast_fp16 = linear(bias = encoder_module_layers_7_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_7_self_attn_linear_out_weight_to_fp16_quantized, x = input_407_cast_fp16)[name = string("linear_70_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_411_cast_fp16 = add(x = input_403_cast_fp16, y = linear_70_cast_fp16)[name = string("input_411_cast_fp16")];
+            tensor<int32, [1]> x_191_axes_0 = const()[name = string("x_191_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189604288)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189606400)))];
+            tensor<fp16, [1, 188, 1024]> x_191_cast_fp16 = layer_norm(axes = x_191_axes_0, beta = encoder_module_layers_7_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_conv_weight_to_fp16, x = input_411_cast_fp16)[name = string("x_191_cast_fp16")];
+            tensor<int32, [3]> input_413_perm_0 = const()[name = string("input_413_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_415_pad_type_0 = const()[name = string("input_415_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_415_strides_0 = const()[name = string("input_415_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_415_pad_0 = const()[name = string("input_415_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_415_dilations_0 = const()[name = string("input_415_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_415_groups_0 = const()[name = string("input_415_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(189608512))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191709888))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191705728))))[name = string("encoder_module_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191712000)))];
+            tensor<fp16, [1, 1024, 188]> input_413_cast_fp16 = transpose(perm = input_413_perm_0, x = x_191_cast_fp16)[name = string("transpose_258")];
+            tensor<fp16, [1, 2048, 188]> input_415_cast_fp16 = conv(bias = encoder_module_layers_7_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_7_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_413_cast_fp16)[name = string("input_415_cast_fp16")];
+            int32 x_193_split_num_splits_0 = const()[name = string("x_193_split_num_splits_0"), val = int32(2)];
+            int32 x_193_split_axis_0 = const()[name = string("x_193_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_193_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_193_split_cast_fp16_1 = split(axis = x_193_split_axis_0, num_splits = x_193_split_num_splits_0, x = input_415_cast_fp16)[name = string("x_193_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_193_split_1_sigmoid_cast_fp16 = sigmoid(x = x_193_split_cast_fp16_1)[name = string("x_193_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_193_cast_fp16 = mul(x = x_193_split_cast_fp16_0, y = x_193_split_1_sigmoid_cast_fp16)[name = string("x_193_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_417_cast_fp16 = select(a = var_164_to_fp16, b = x_193_cast_fp16, cond = var_608)[name = string("input_417_cast_fp16")];
+            tensor<int32, [6]> input_419_pad_0 = const()[name = string("input_419_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_419_mode_0 = const()[name = string("input_419_mode_0"), val = string("constant")];
+            fp16 const_161_to_fp16 = const()[name = string("const_161_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_419_cast_fp16 = pad(constant_val = const_161_to_fp16, mode = input_419_mode_0, pad = input_419_pad_0, x = input_417_cast_fp16)[name = string("input_419_cast_fp16")];
+            string input_421_pad_type_0 = const()[name = string("input_421_pad_type_0"), val = string("valid")];
+            int32 input_421_groups_0 = const()[name = string("input_421_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_421_strides_0 = const()[name = string("input_421_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_421_pad_0 = const()[name = string("input_421_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_421_dilations_0 = const()[name = string("input_421_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_336_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191716160))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191727552))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191725440))))[name = string("const_336_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_337_to_fp16 = const()[name = string("const_337_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191728640)))];
+            tensor<fp16, [1, 1024, 188]> input_423_cast_fp16 = conv(bias = const_337_to_fp16, dilations = input_421_dilations_0, groups = input_421_groups_0, pad = input_421_pad_0, pad_type = input_421_pad_type_0, strides = input_421_strides_0, weight = const_336_to_fp16_quantized, x = input_419_cast_fp16)[name = string("input_423_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_425_cast_fp16 = silu(x = input_423_cast_fp16)[name = string("input_425_cast_fp16")];
+            string x_195_pad_type_0 = const()[name = string("x_195_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_195_strides_0 = const()[name = string("x_195_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_195_pad_0 = const()[name = string("x_195_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_195_dilations_0 = const()[name = string("x_195_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_195_groups_0 = const()[name = string("x_195_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191730752))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192781504))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192779392))))[name = string("encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192782592)))];
+            tensor<fp16, [1, 1024, 188]> x_195_cast_fp16 = conv(bias = encoder_module_layers_7_conv_pointwise_conv2_bias_to_fp16, dilations = x_195_dilations_0, groups = x_195_groups_0, pad = x_195_pad_0, pad_type = x_195_pad_type_0, strides = x_195_strides_0, weight = encoder_module_layers_7_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_425_cast_fp16)[name = string("x_195_cast_fp16")];
+            tensor<int32, [3]> input_427_perm_0 = const()[name = string("input_427_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_427_cast_fp16 = transpose(perm = input_427_perm_0, x = x_195_cast_fp16)[name = string("transpose_257")];
+            tensor<fp16, [1, 188, 1024]> input_429_cast_fp16 = add(x = input_411_cast_fp16, y = input_427_cast_fp16)[name = string("input_429_cast_fp16")];
+            tensor<int32, [1]> input_431_axes_0 = const()[name = string("input_431_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192784704)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192786816)))];
+            tensor<fp16, [1, 188, 1024]> input_431_cast_fp16 = layer_norm(axes = input_431_axes_0, beta = encoder_module_layers_7_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_feed_forward2_weight_to_fp16, x = input_429_cast_fp16)[name = string("input_431_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192788928))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196991552))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196983296))))[name = string("encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196995712)))];
+            tensor<fp16, [1, 188, 4096]> linear_71_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_7_feed_forward2_linear1_weight_to_fp16_quantized, x = input_431_cast_fp16)[name = string("linear_71_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_435_cast_fp16 = silu(x = linear_71_cast_fp16)[name = string("input_435_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197003968))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201200448))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201198336))))[name = string("encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201201536)))];
+            tensor<fp16, [1, 188, 1024]> linear_72_cast_fp16 = linear(bias = encoder_module_layers_7_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_7_feed_forward2_linear2_weight_to_fp16_quantized, x = input_435_cast_fp16)[name = string("linear_72_cast_fp16")];
+            fp16 var_1868_to_fp16 = const()[name = string("op_1868_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1869_cast_fp16 = mul(x = linear_72_cast_fp16, y = var_1868_to_fp16)[name = string("op_1869_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_441_cast_fp16 = add(x = input_429_cast_fp16, y = var_1869_cast_fp16)[name = string("input_441_cast_fp16")];
+            tensor<int32, [1]> input_443_axes_0 = const()[name = string("input_443_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_7_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201203648)))];
+            tensor<fp16, [1024]> encoder_module_layers_7_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_7_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201205760)))];
+            tensor<fp16, [1, 188, 1024]> input_443_cast_fp16 = layer_norm(axes = input_443_axes_0, beta = encoder_module_layers_7_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_7_norm_out_weight_to_fp16, x = input_441_cast_fp16)[name = string("input_443_cast_fp16")];
+            tensor<int32, [1]> input_445_axes_0 = const()[name = string("input_445_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201207872)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201209984)))];
+            tensor<fp16, [1, 188, 1024]> input_445_cast_fp16 = layer_norm(axes = input_445_axes_0, beta = encoder_module_layers_8_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_feed_forward1_weight_to_fp16, x = input_443_cast_fp16)[name = string("input_445_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(201212096))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205414720))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205406464))))[name = string("encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205418880)))];
+            tensor<fp16, [1, 188, 4096]> linear_73_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_8_feed_forward1_linear1_weight_to_fp16_quantized, x = input_445_cast_fp16)[name = string("linear_73_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_449_cast_fp16 = silu(x = linear_73_cast_fp16)[name = string("input_449_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205427136))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209623616))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209621504))))[name = string("encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209624704)))];
+            tensor<fp16, [1, 188, 1024]> linear_74_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_8_feed_forward1_linear2_weight_to_fp16_quantized, x = input_449_cast_fp16)[name = string("linear_74_cast_fp16")];
+            fp16 var_1899_to_fp16 = const()[name = string("op_1899_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_1900_cast_fp16 = mul(x = linear_74_cast_fp16, y = var_1899_to_fp16)[name = string("op_1900_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_455_cast_fp16 = add(x = input_443_cast_fp16, y = var_1900_cast_fp16)[name = string("input_455_cast_fp16")];
+            tensor<int32, [1]> query_17_axes_0 = const()[name = string("query_17_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209626816)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209628928)))];
+            tensor<fp16, [1, 188, 1024]> query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = encoder_module_layers_8_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_self_att_weight_to_fp16, x = input_455_cast_fp16)[name = string("query_17_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209631040))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210681792))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210679680))))[name = string("encoder_module_layers_8_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210682880)))];
+            tensor<fp16, [1, 188, 1024]> linear_75_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_q_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = string("linear_75_cast_fp16")];
+            tensor<int32, [4]> var_1917 = const()[name = string("op_1917"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_49_cast_fp16 = reshape(shape = var_1917, x = linear_75_cast_fp16)[name = string("q_49_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210684992))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211735744))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211733632))))[name = string("encoder_module_layers_8_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211736832)))];
+            tensor<fp16, [1, 188, 1024]> linear_76_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_k_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = string("linear_76_cast_fp16")];
+            tensor<int32, [4]> var_1922 = const()[name = string("op_1922"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_33_cast_fp16 = reshape(shape = var_1922, x = linear_76_cast_fp16)[name = string("k_33_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(211738944))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212789696))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212787584))))[name = string("encoder_module_layers_8_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212790784)))];
+            tensor<fp16, [1, 188, 1024]> linear_77_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_v_weight_to_fp16_quantized, x = query_17_cast_fp16)[name = string("linear_77_cast_fp16")];
+            tensor<int32, [4]> var_1927 = const()[name = string("op_1927"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_17_cast_fp16 = reshape(shape = var_1927, x = linear_77_cast_fp16)[name = string("v_17_cast_fp16")];
+            tensor<int32, [4]> value_21_perm_0 = const()[name = string("value_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_8_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212792896)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1939_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_module_layers_8_self_attn_pos_bias_u_to_fp16)[name = string("op_1939_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_8_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212795008)))];
+            tensor<fp16, [1, 188, 8, 128]> var_1941_cast_fp16 = add(x = q_49_cast_fp16, y = encoder_module_layers_8_self_attn_pos_bias_v_to_fp16)[name = string("op_1941_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_17_perm_0 = const()[name = string("q_with_bias_v_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_203_transpose_x_0 = const()[name = string("x_203_transpose_x_0"), val = bool(false)];
+            bool x_203_transpose_y_0 = const()[name = string("x_203_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_1943_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(212797120))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213182016))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213181184))))[name = string("op_1943_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_17_cast_fp16 = transpose(perm = q_with_bias_v_17_perm_0, x = var_1941_cast_fp16)[name = string("transpose_256")];
+            tensor<fp16, [1, 8, 188, 375]> x_203_cast_fp16 = matmul(transpose_x = x_203_transpose_x_0, transpose_y = x_203_transpose_y_0, x = q_with_bias_v_17_cast_fp16, y = op_1943_to_fp16_quantized)[name = string("x_203_cast_fp16")];
+            tensor<int32, [8]> x_205_pad_0 = const()[name = string("x_205_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_205_mode_0 = const()[name = string("x_205_mode_0"), val = string("constant")];
+            fp16 const_168_to_fp16 = const()[name = string("const_168_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_205_cast_fp16 = pad(constant_val = const_168_to_fp16, mode = x_205_mode_0, pad = x_205_pad_0, x = x_203_cast_fp16)[name = string("x_205_cast_fp16")];
+            tensor<int32, [4]> var_1951 = const()[name = string("op_1951"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_207_cast_fp16 = reshape(shape = var_1951, x = x_205_cast_fp16)[name = string("x_207_cast_fp16")];
+            tensor<int32, [4]> var_1955_begin_0 = const()[name = string("op_1955_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_1955_end_0 = const()[name = string("op_1955_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_1955_end_mask_0 = const()[name = string("op_1955_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_1955_cast_fp16 = slice_by_index(begin = var_1955_begin_0, end = var_1955_end_0, end_mask = var_1955_end_mask_0, x = x_207_cast_fp16)[name = string("op_1955_cast_fp16")];
+            tensor<int32, [4]> var_1956 = const()[name = string("op_1956"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_33_cast_fp16 = reshape(shape = var_1956, x = var_1955_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<int32, [4]> transpose_112_perm_0 = const()[name = string("transpose_112_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_113_perm_0 = const()[name = string("transpose_113_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_113 = transpose(perm = transpose_113_perm_0, x = k_33_cast_fp16)[name = string("transpose_254")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_112 = transpose(perm = transpose_112_perm_0, x = var_1939_cast_fp16)[name = string("transpose_255")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_35_end_0 = const()[name = string("matrix_bd_35_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_1965_cast_fp16 = add(x = matrix_ac_17_cast_fp16, y = matrix_bd_35_cast_fp16)[name = string("op_1965_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<fp16, [1, 8, 188, 188]> _inversed_scores_33_cast_fp16 = mul(x = var_1965_cast_fp16, y = _inversed_scores_33_y_0_to_fp16)[name = string("_inversed_scores_33_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_35_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_33_cast_fp16, cond = mask_15)[name = string("scores_35_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_1971_cast_fp16 = softmax(axis = var_152, x = scores_35_cast_fp16)[name = string("op_1971_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_457_cast_fp16 = select(a = var_164_to_fp16, b = var_1971_cast_fp16, cond = mask_15)[name = string("input_457_cast_fp16")];
+            bool x_209_transpose_x_0 = const()[name = string("x_209_transpose_x_0"), val = bool(false)];
+            bool x_209_transpose_y_0 = const()[name = string("x_209_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_21_cast_fp16 = transpose(perm = value_21_perm_0, x = v_17_cast_fp16)[name = string("transpose_253")];
+            tensor<fp16, [1, 8, 188, 128]> x_209_cast_fp16 = matmul(transpose_x = x_209_transpose_x_0, transpose_y = x_209_transpose_y_0, x = input_457_cast_fp16, y = value_21_cast_fp16)[name = string("x_209_cast_fp16")];
+            tensor<int32, [4]> var_1975_perm_0 = const()[name = string("op_1975_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_1976 = const()[name = string("op_1976"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_1975_cast_fp16 = transpose(perm = var_1975_perm_0, x = x_209_cast_fp16)[name = string("transpose_252")];
+            tensor<fp16, [1, 188, 1024]> input_459_cast_fp16 = reshape(shape = var_1976, x = var_1975_cast_fp16)[name = string("input_459_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_8_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(213182464))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214233216))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214231104))))[name = string("encoder_module_layers_8_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_8_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214234304)))];
+            tensor<fp16, [1, 188, 1024]> linear_79_cast_fp16 = linear(bias = encoder_module_layers_8_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_8_self_attn_linear_out_weight_to_fp16_quantized, x = input_459_cast_fp16)[name = string("linear_79_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_463_cast_fp16 = add(x = input_455_cast_fp16, y = linear_79_cast_fp16)[name = string("input_463_cast_fp16")];
+            tensor<int32, [1]> x_213_axes_0 = const()[name = string("x_213_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214236416)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214238528)))];
+            tensor<fp16, [1, 188, 1024]> x_213_cast_fp16 = layer_norm(axes = x_213_axes_0, beta = encoder_module_layers_8_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_conv_weight_to_fp16, x = input_463_cast_fp16)[name = string("x_213_cast_fp16")];
+            tensor<int32, [3]> input_465_perm_0 = const()[name = string("input_465_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_467_pad_type_0 = const()[name = string("input_467_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_467_strides_0 = const()[name = string("input_467_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_467_pad_0 = const()[name = string("input_467_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_467_dilations_0 = const()[name = string("input_467_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_467_groups_0 = const()[name = string("input_467_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214240640))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216342016))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216337856))))[name = string("encoder_module_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216344128)))];
+            tensor<fp16, [1, 1024, 188]> input_465_cast_fp16 = transpose(perm = input_465_perm_0, x = x_213_cast_fp16)[name = string("transpose_251")];
+            tensor<fp16, [1, 2048, 188]> input_467_cast_fp16 = conv(bias = encoder_module_layers_8_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_8_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_465_cast_fp16)[name = string("input_467_cast_fp16")];
+            int32 x_215_split_num_splits_0 = const()[name = string("x_215_split_num_splits_0"), val = int32(2)];
+            int32 x_215_split_axis_0 = const()[name = string("x_215_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_215_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_215_split_cast_fp16_1 = split(axis = x_215_split_axis_0, num_splits = x_215_split_num_splits_0, x = input_467_cast_fp16)[name = string("x_215_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_215_split_1_sigmoid_cast_fp16 = sigmoid(x = x_215_split_cast_fp16_1)[name = string("x_215_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_215_cast_fp16 = mul(x = x_215_split_cast_fp16_0, y = x_215_split_1_sigmoid_cast_fp16)[name = string("x_215_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_469_cast_fp16 = select(a = var_164_to_fp16, b = x_215_cast_fp16, cond = var_608)[name = string("input_469_cast_fp16")];
+            tensor<int32, [6]> input_471_pad_0 = const()[name = string("input_471_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_471_mode_0 = const()[name = string("input_471_mode_0"), val = string("constant")];
+            fp16 const_171_to_fp16 = const()[name = string("const_171_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_471_cast_fp16 = pad(constant_val = const_171_to_fp16, mode = input_471_mode_0, pad = input_471_pad_0, x = input_469_cast_fp16)[name = string("input_471_cast_fp16")];
+            string input_473_pad_type_0 = const()[name = string("input_473_pad_type_0"), val = string("valid")];
+            int32 input_473_groups_0 = const()[name = string("input_473_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_473_strides_0 = const()[name = string("input_473_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_473_pad_0 = const()[name = string("input_473_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_473_dilations_0 = const()[name = string("input_473_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_338_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216348288))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216359680))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216357568))))[name = string("const_338_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_339_to_fp16 = const()[name = string("const_339_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216360768)))];
+            tensor<fp16, [1, 1024, 188]> input_475_cast_fp16 = conv(bias = const_339_to_fp16, dilations = input_473_dilations_0, groups = input_473_groups_0, pad = input_473_pad_0, pad_type = input_473_pad_type_0, strides = input_473_strides_0, weight = const_338_to_fp16_quantized, x = input_471_cast_fp16)[name = string("input_475_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_477_cast_fp16 = silu(x = input_475_cast_fp16)[name = string("input_477_cast_fp16")];
+            string x_217_pad_type_0 = const()[name = string("x_217_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_217_strides_0 = const()[name = string("x_217_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_217_pad_0 = const()[name = string("x_217_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_217_dilations_0 = const()[name = string("x_217_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_217_groups_0 = const()[name = string("x_217_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216362880))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217413632))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217411520))))[name = string("encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217414720)))];
+            tensor<fp16, [1, 1024, 188]> x_217_cast_fp16 = conv(bias = encoder_module_layers_8_conv_pointwise_conv2_bias_to_fp16, dilations = x_217_dilations_0, groups = x_217_groups_0, pad = x_217_pad_0, pad_type = x_217_pad_type_0, strides = x_217_strides_0, weight = encoder_module_layers_8_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_477_cast_fp16)[name = string("x_217_cast_fp16")];
+            tensor<int32, [3]> input_479_perm_0 = const()[name = string("input_479_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_479_cast_fp16 = transpose(perm = input_479_perm_0, x = x_217_cast_fp16)[name = string("transpose_250")];
+            tensor<fp16, [1, 188, 1024]> input_481_cast_fp16 = add(x = input_463_cast_fp16, y = input_479_cast_fp16)[name = string("input_481_cast_fp16")];
+            tensor<int32, [1]> input_483_axes_0 = const()[name = string("input_483_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217416832)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217418944)))];
+            tensor<fp16, [1, 188, 1024]> input_483_cast_fp16 = layer_norm(axes = input_483_axes_0, beta = encoder_module_layers_8_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_feed_forward2_weight_to_fp16, x = input_481_cast_fp16)[name = string("input_483_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(217421056))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221623680))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221615424))))[name = string("encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221627840)))];
+            tensor<fp16, [1, 188, 4096]> linear_80_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_8_feed_forward2_linear1_weight_to_fp16_quantized, x = input_483_cast_fp16)[name = string("linear_80_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_487_cast_fp16 = silu(x = linear_80_cast_fp16)[name = string("input_487_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221636096))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225832576))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225830464))))[name = string("encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225833664)))];
+            tensor<fp16, [1, 188, 1024]> linear_81_cast_fp16 = linear(bias = encoder_module_layers_8_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_8_feed_forward2_linear2_weight_to_fp16_quantized, x = input_487_cast_fp16)[name = string("linear_81_cast_fp16")];
+            fp16 var_2042_to_fp16 = const()[name = string("op_2042_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2043_cast_fp16 = mul(x = linear_81_cast_fp16, y = var_2042_to_fp16)[name = string("op_2043_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_493_cast_fp16 = add(x = input_481_cast_fp16, y = var_2043_cast_fp16)[name = string("input_493_cast_fp16")];
+            tensor<int32, [1]> input_495_axes_0 = const()[name = string("input_495_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_8_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225835776)))];
+            tensor<fp16, [1024]> encoder_module_layers_8_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_8_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225837888)))];
+            tensor<fp16, [1, 188, 1024]> input_495_cast_fp16 = layer_norm(axes = input_495_axes_0, beta = encoder_module_layers_8_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_8_norm_out_weight_to_fp16, x = input_493_cast_fp16)[name = string("input_495_cast_fp16")];
+            tensor<int32, [1]> input_497_axes_0 = const()[name = string("input_497_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225840000)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225842112)))];
+            tensor<fp16, [1, 188, 1024]> input_497_cast_fp16 = layer_norm(axes = input_497_axes_0, beta = encoder_module_layers_9_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_feed_forward1_weight_to_fp16, x = input_495_cast_fp16)[name = string("input_497_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(225844224))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230046848))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230038592))))[name = string("encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230051008)))];
+            tensor<fp16, [1, 188, 4096]> linear_82_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_9_feed_forward1_linear1_weight_to_fp16_quantized, x = input_497_cast_fp16)[name = string("linear_82_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_501_cast_fp16 = silu(x = linear_82_cast_fp16)[name = string("input_501_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(230059264))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234255744))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234253632))))[name = string("encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234256832)))];
+            tensor<fp16, [1, 188, 1024]> linear_83_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_9_feed_forward1_linear2_weight_to_fp16_quantized, x = input_501_cast_fp16)[name = string("linear_83_cast_fp16")];
+            fp16 var_2073_to_fp16 = const()[name = string("op_2073_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2074_cast_fp16 = mul(x = linear_83_cast_fp16, y = var_2073_to_fp16)[name = string("op_2074_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_507_cast_fp16 = add(x = input_495_cast_fp16, y = var_2074_cast_fp16)[name = string("input_507_cast_fp16")];
+            tensor<int32, [1]> query_19_axes_0 = const()[name = string("query_19_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234258944)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234261056)))];
+            tensor<fp16, [1, 188, 1024]> query_19_cast_fp16 = layer_norm(axes = query_19_axes_0, beta = encoder_module_layers_9_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_self_att_weight_to_fp16, x = input_507_cast_fp16)[name = string("query_19_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(234263168))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235313920))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235311808))))[name = string("encoder_module_layers_9_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235315008)))];
+            tensor<fp16, [1, 188, 1024]> linear_84_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_q_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = string("linear_84_cast_fp16")];
+            tensor<int32, [4]> var_2091 = const()[name = string("op_2091"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_55_cast_fp16 = reshape(shape = var_2091, x = linear_84_cast_fp16)[name = string("q_55_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235317120))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236367872))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236365760))))[name = string("encoder_module_layers_9_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236368960)))];
+            tensor<fp16, [1, 188, 1024]> linear_85_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_k_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = string("linear_85_cast_fp16")];
+            tensor<int32, [4]> var_2096 = const()[name = string("op_2096"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_37_cast_fp16 = reshape(shape = var_2096, x = linear_85_cast_fp16)[name = string("k_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236371072))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237421824))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237419712))))[name = string("encoder_module_layers_9_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237422912)))];
+            tensor<fp16, [1, 188, 1024]> linear_86_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_v_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = string("linear_86_cast_fp16")];
+            tensor<int32, [4]> var_2101 = const()[name = string("op_2101"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_19_cast_fp16 = reshape(shape = var_2101, x = linear_86_cast_fp16)[name = string("v_19_cast_fp16")];
+            tensor<int32, [4]> value_23_perm_0 = const()[name = string("value_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_9_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237425024)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2113_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_module_layers_9_self_attn_pos_bias_u_to_fp16)[name = string("op_2113_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_9_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237427136)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2115_cast_fp16 = add(x = q_55_cast_fp16, y = encoder_module_layers_9_self_attn_pos_bias_v_to_fp16)[name = string("op_2115_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_19_perm_0 = const()[name = string("q_with_bias_v_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_225_transpose_x_0 = const()[name = string("x_225_transpose_x_0"), val = bool(false)];
+            bool x_225_transpose_y_0 = const()[name = string("x_225_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2117_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237429248))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237814144))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237813312))))[name = string("op_2117_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_19_cast_fp16 = transpose(perm = q_with_bias_v_19_perm_0, x = var_2115_cast_fp16)[name = string("transpose_249")];
+            tensor<fp16, [1, 8, 188, 375]> x_225_cast_fp16 = matmul(transpose_x = x_225_transpose_x_0, transpose_y = x_225_transpose_y_0, x = q_with_bias_v_19_cast_fp16, y = op_2117_to_fp16_quantized)[name = string("x_225_cast_fp16")];
+            tensor<int32, [8]> x_227_pad_0 = const()[name = string("x_227_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_227_mode_0 = const()[name = string("x_227_mode_0"), val = string("constant")];
+            fp16 const_178_to_fp16 = const()[name = string("const_178_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_227_cast_fp16 = pad(constant_val = const_178_to_fp16, mode = x_227_mode_0, pad = x_227_pad_0, x = x_225_cast_fp16)[name = string("x_227_cast_fp16")];
+            tensor<int32, [4]> var_2125 = const()[name = string("op_2125"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_229_cast_fp16 = reshape(shape = var_2125, x = x_227_cast_fp16)[name = string("x_229_cast_fp16")];
+            tensor<int32, [4]> var_2129_begin_0 = const()[name = string("op_2129_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2129_end_0 = const()[name = string("op_2129_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2129_end_mask_0 = const()[name = string("op_2129_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2129_cast_fp16 = slice_by_index(begin = var_2129_begin_0, end = var_2129_end_0, end_mask = var_2129_end_mask_0, x = x_229_cast_fp16)[name = string("op_2129_cast_fp16")];
+            tensor<int32, [4]> var_2130 = const()[name = string("op_2130"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_37_cast_fp16 = reshape(shape = var_2130, x = var_2129_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<int32, [4]> transpose_114_perm_0 = const()[name = string("transpose_114_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_115_perm_0 = const()[name = string("transpose_115_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_115 = transpose(perm = transpose_115_perm_0, x = k_37_cast_fp16)[name = string("transpose_247")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_114 = transpose(perm = transpose_114_perm_0, x = var_2113_cast_fp16)[name = string("transpose_248")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_39_begin_0 = const()[name = string("matrix_bd_39_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_39_end_0 = const()[name = string("matrix_bd_39_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_39_end_mask_0 = const()[name = string("matrix_bd_39_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2139_cast_fp16 = add(x = matrix_ac_19_cast_fp16, y = matrix_bd_39_cast_fp16)[name = string("op_2139_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<fp16, [1, 8, 188, 188]> _inversed_scores_37_cast_fp16 = mul(x = var_2139_cast_fp16, y = _inversed_scores_37_y_0_to_fp16)[name = string("_inversed_scores_37_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_39_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_37_cast_fp16, cond = mask_15)[name = string("scores_39_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2145_cast_fp16 = softmax(axis = var_152, x = scores_39_cast_fp16)[name = string("op_2145_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_509_cast_fp16 = select(a = var_164_to_fp16, b = var_2145_cast_fp16, cond = mask_15)[name = string("input_509_cast_fp16")];
+            bool x_231_transpose_x_0 = const()[name = string("x_231_transpose_x_0"), val = bool(false)];
+            bool x_231_transpose_y_0 = const()[name = string("x_231_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_23_cast_fp16 = transpose(perm = value_23_perm_0, x = v_19_cast_fp16)[name = string("transpose_246")];
+            tensor<fp16, [1, 8, 188, 128]> x_231_cast_fp16 = matmul(transpose_x = x_231_transpose_x_0, transpose_y = x_231_transpose_y_0, x = input_509_cast_fp16, y = value_23_cast_fp16)[name = string("x_231_cast_fp16")];
+            tensor<int32, [4]> var_2149_perm_0 = const()[name = string("op_2149_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2150 = const()[name = string("op_2150"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2149_cast_fp16 = transpose(perm = var_2149_perm_0, x = x_231_cast_fp16)[name = string("transpose_245")];
+            tensor<fp16, [1, 188, 1024]> input_511_cast_fp16 = reshape(shape = var_2150, x = var_2149_cast_fp16)[name = string("input_511_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_9_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(237814592))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238865344))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238863232))))[name = string("encoder_module_layers_9_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_9_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238866432)))];
+            tensor<fp16, [1, 188, 1024]> linear_88_cast_fp16 = linear(bias = encoder_module_layers_9_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_9_self_attn_linear_out_weight_to_fp16_quantized, x = input_511_cast_fp16)[name = string("linear_88_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_515_cast_fp16 = add(x = input_507_cast_fp16, y = linear_88_cast_fp16)[name = string("input_515_cast_fp16")];
+            tensor<int32, [1]> x_235_axes_0 = const()[name = string("x_235_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238868544)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238870656)))];
+            tensor<fp16, [1, 188, 1024]> x_235_cast_fp16 = layer_norm(axes = x_235_axes_0, beta = encoder_module_layers_9_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_conv_weight_to_fp16, x = input_515_cast_fp16)[name = string("x_235_cast_fp16")];
+            tensor<int32, [3]> input_517_perm_0 = const()[name = string("input_517_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_519_pad_type_0 = const()[name = string("input_519_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_519_strides_0 = const()[name = string("input_519_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_519_pad_0 = const()[name = string("input_519_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_519_dilations_0 = const()[name = string("input_519_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_519_groups_0 = const()[name = string("input_519_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(238872768))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240974144))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240969984))))[name = string("encoder_module_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240976256)))];
+            tensor<fp16, [1, 1024, 188]> input_517_cast_fp16 = transpose(perm = input_517_perm_0, x = x_235_cast_fp16)[name = string("transpose_244")];
+            tensor<fp16, [1, 2048, 188]> input_519_cast_fp16 = conv(bias = encoder_module_layers_9_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_9_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_517_cast_fp16)[name = string("input_519_cast_fp16")];
+            int32 x_237_split_num_splits_0 = const()[name = string("x_237_split_num_splits_0"), val = int32(2)];
+            int32 x_237_split_axis_0 = const()[name = string("x_237_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_237_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_237_split_cast_fp16_1 = split(axis = x_237_split_axis_0, num_splits = x_237_split_num_splits_0, x = input_519_cast_fp16)[name = string("x_237_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_237_split_1_sigmoid_cast_fp16 = sigmoid(x = x_237_split_cast_fp16_1)[name = string("x_237_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_237_cast_fp16 = mul(x = x_237_split_cast_fp16_0, y = x_237_split_1_sigmoid_cast_fp16)[name = string("x_237_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_521_cast_fp16 = select(a = var_164_to_fp16, b = x_237_cast_fp16, cond = var_608)[name = string("input_521_cast_fp16")];
+            tensor<int32, [6]> input_523_pad_0 = const()[name = string("input_523_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_523_mode_0 = const()[name = string("input_523_mode_0"), val = string("constant")];
+            fp16 const_181_to_fp16 = const()[name = string("const_181_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_523_cast_fp16 = pad(constant_val = const_181_to_fp16, mode = input_523_mode_0, pad = input_523_pad_0, x = input_521_cast_fp16)[name = string("input_523_cast_fp16")];
+            string input_525_pad_type_0 = const()[name = string("input_525_pad_type_0"), val = string("valid")];
+            int32 input_525_groups_0 = const()[name = string("input_525_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_525_strides_0 = const()[name = string("input_525_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_525_pad_0 = const()[name = string("input_525_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_525_dilations_0 = const()[name = string("input_525_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_340_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240980416))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240991808))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240989696))))[name = string("const_340_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_341_to_fp16 = const()[name = string("const_341_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240992896)))];
+            tensor<fp16, [1, 1024, 188]> input_527_cast_fp16 = conv(bias = const_341_to_fp16, dilations = input_525_dilations_0, groups = input_525_groups_0, pad = input_525_pad_0, pad_type = input_525_pad_type_0, strides = input_525_strides_0, weight = const_340_to_fp16_quantized, x = input_523_cast_fp16)[name = string("input_527_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_529_cast_fp16 = silu(x = input_527_cast_fp16)[name = string("input_529_cast_fp16")];
+            string x_239_pad_type_0 = const()[name = string("x_239_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_239_strides_0 = const()[name = string("x_239_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_239_pad_0 = const()[name = string("x_239_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_239_dilations_0 = const()[name = string("x_239_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_239_groups_0 = const()[name = string("x_239_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240995008))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242045760))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242043648))))[name = string("encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242046848)))];
+            tensor<fp16, [1, 1024, 188]> x_239_cast_fp16 = conv(bias = encoder_module_layers_9_conv_pointwise_conv2_bias_to_fp16, dilations = x_239_dilations_0, groups = x_239_groups_0, pad = x_239_pad_0, pad_type = x_239_pad_type_0, strides = x_239_strides_0, weight = encoder_module_layers_9_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_529_cast_fp16)[name = string("x_239_cast_fp16")];
+            tensor<int32, [3]> input_531_perm_0 = const()[name = string("input_531_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_531_cast_fp16 = transpose(perm = input_531_perm_0, x = x_239_cast_fp16)[name = string("transpose_243")];
+            tensor<fp16, [1, 188, 1024]> input_533_cast_fp16 = add(x = input_515_cast_fp16, y = input_531_cast_fp16)[name = string("input_533_cast_fp16")];
+            tensor<int32, [1]> input_535_axes_0 = const()[name = string("input_535_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242048960)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242051072)))];
+            tensor<fp16, [1, 188, 1024]> input_535_cast_fp16 = layer_norm(axes = input_535_axes_0, beta = encoder_module_layers_9_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_feed_forward2_weight_to_fp16, x = input_533_cast_fp16)[name = string("input_535_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(242053184))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246255808))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246247552))))[name = string("encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246259968)))];
+            tensor<fp16, [1, 188, 4096]> linear_89_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_9_feed_forward2_linear1_weight_to_fp16_quantized, x = input_535_cast_fp16)[name = string("linear_89_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_539_cast_fp16 = silu(x = linear_89_cast_fp16)[name = string("input_539_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(246268224))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250464704))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250462592))))[name = string("encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250465792)))];
+            tensor<fp16, [1, 188, 1024]> linear_90_cast_fp16 = linear(bias = encoder_module_layers_9_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_9_feed_forward2_linear2_weight_to_fp16_quantized, x = input_539_cast_fp16)[name = string("linear_90_cast_fp16")];
+            fp16 var_2216_to_fp16 = const()[name = string("op_2216_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2217_cast_fp16 = mul(x = linear_90_cast_fp16, y = var_2216_to_fp16)[name = string("op_2217_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_545_cast_fp16 = add(x = input_533_cast_fp16, y = var_2217_cast_fp16)[name = string("input_545_cast_fp16")];
+            tensor<int32, [1]> input_547_axes_0 = const()[name = string("input_547_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_9_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250467904)))];
+            tensor<fp16, [1024]> encoder_module_layers_9_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_9_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250470016)))];
+            tensor<fp16, [1, 188, 1024]> input_547_cast_fp16 = layer_norm(axes = input_547_axes_0, beta = encoder_module_layers_9_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_9_norm_out_weight_to_fp16, x = input_545_cast_fp16)[name = string("input_547_cast_fp16")];
+            tensor<int32, [1]> input_549_axes_0 = const()[name = string("input_549_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250472128)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250474240)))];
+            tensor<fp16, [1, 188, 1024]> input_549_cast_fp16 = layer_norm(axes = input_549_axes_0, beta = encoder_module_layers_10_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_feed_forward1_weight_to_fp16, x = input_547_cast_fp16)[name = string("input_549_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(250476352))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254678976))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254670720))))[name = string("encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254683136)))];
+            tensor<fp16, [1, 188, 4096]> linear_91_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_10_feed_forward1_linear1_weight_to_fp16_quantized, x = input_549_cast_fp16)[name = string("linear_91_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_553_cast_fp16 = silu(x = linear_91_cast_fp16)[name = string("input_553_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(254691392))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258887872))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258885760))))[name = string("encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258888960)))];
+            tensor<fp16, [1, 188, 1024]> linear_92_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_10_feed_forward1_linear2_weight_to_fp16_quantized, x = input_553_cast_fp16)[name = string("linear_92_cast_fp16")];
+            fp16 var_2247_to_fp16 = const()[name = string("op_2247_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2248_cast_fp16 = mul(x = linear_92_cast_fp16, y = var_2247_to_fp16)[name = string("op_2248_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_559_cast_fp16 = add(x = input_547_cast_fp16, y = var_2248_cast_fp16)[name = string("input_559_cast_fp16")];
+            tensor<int32, [1]> query_21_axes_0 = const()[name = string("query_21_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258891072)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258893184)))];
+            tensor<fp16, [1, 188, 1024]> query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = encoder_module_layers_10_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_self_att_weight_to_fp16, x = input_559_cast_fp16)[name = string("query_21_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258895296))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259946048))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259943936))))[name = string("encoder_module_layers_10_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259947136)))];
+            tensor<fp16, [1, 188, 1024]> linear_93_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_q_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = string("linear_93_cast_fp16")];
+            tensor<int32, [4]> var_2265 = const()[name = string("op_2265"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_61_cast_fp16 = reshape(shape = var_2265, x = linear_93_cast_fp16)[name = string("q_61_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259949248))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261000000))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260997888))))[name = string("encoder_module_layers_10_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261001088)))];
+            tensor<fp16, [1, 188, 1024]> linear_94_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_k_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = string("linear_94_cast_fp16")];
+            tensor<int32, [4]> var_2270 = const()[name = string("op_2270"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_41_cast_fp16 = reshape(shape = var_2270, x = linear_94_cast_fp16)[name = string("k_41_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261003200))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262053952))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262051840))))[name = string("encoder_module_layers_10_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262055040)))];
+            tensor<fp16, [1, 188, 1024]> linear_95_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_v_weight_to_fp16_quantized, x = query_21_cast_fp16)[name = string("linear_95_cast_fp16")];
+            tensor<int32, [4]> var_2275 = const()[name = string("op_2275"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_21_cast_fp16 = reshape(shape = var_2275, x = linear_95_cast_fp16)[name = string("v_21_cast_fp16")];
+            tensor<int32, [4]> value_25_perm_0 = const()[name = string("value_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_10_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262057152)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2287_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_module_layers_10_self_attn_pos_bias_u_to_fp16)[name = string("op_2287_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_10_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262059264)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2289_cast_fp16 = add(x = q_61_cast_fp16, y = encoder_module_layers_10_self_attn_pos_bias_v_to_fp16)[name = string("op_2289_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_21_perm_0 = const()[name = string("q_with_bias_v_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            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<fp16, [1, 8, 128, 375]> op_2291_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262061376))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262446272))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262445440))))[name = string("op_2291_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_21_cast_fp16 = transpose(perm = q_with_bias_v_21_perm_0, x = var_2289_cast_fp16)[name = string("transpose_242")];
+            tensor<fp16, [1, 8, 188, 375]> x_247_cast_fp16 = matmul(transpose_x = x_247_transpose_x_0, transpose_y = x_247_transpose_y_0, x = q_with_bias_v_21_cast_fp16, y = op_2291_to_fp16_quantized)[name = string("x_247_cast_fp16")];
+            tensor<int32, [8]> x_249_pad_0 = const()[name = string("x_249_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_249_mode_0 = const()[name = string("x_249_mode_0"), val = string("constant")];
+            fp16 const_188_to_fp16 = const()[name = string("const_188_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_249_cast_fp16 = pad(constant_val = const_188_to_fp16, mode = x_249_mode_0, pad = x_249_pad_0, x = x_247_cast_fp16)[name = string("x_249_cast_fp16")];
+            tensor<int32, [4]> var_2299 = const()[name = string("op_2299"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_251_cast_fp16 = reshape(shape = var_2299, x = x_249_cast_fp16)[name = string("x_251_cast_fp16")];
+            tensor<int32, [4]> var_2303_begin_0 = const()[name = string("op_2303_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2303_end_0 = const()[name = string("op_2303_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2303_end_mask_0 = const()[name = string("op_2303_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2303_cast_fp16 = slice_by_index(begin = var_2303_begin_0, end = var_2303_end_0, end_mask = var_2303_end_mask_0, x = x_251_cast_fp16)[name = string("op_2303_cast_fp16")];
+            tensor<int32, [4]> var_2304 = const()[name = string("op_2304"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_41_cast_fp16 = reshape(shape = var_2304, x = var_2303_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<int32, [4]> transpose_116_perm_0 = const()[name = string("transpose_116_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_117_perm_0 = const()[name = string("transpose_117_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_117 = transpose(perm = transpose_117_perm_0, x = k_41_cast_fp16)[name = string("transpose_240")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_116 = transpose(perm = transpose_116_perm_0, x = var_2287_cast_fp16)[name = string("transpose_241")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_43_begin_0 = const()[name = string("matrix_bd_43_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_43_end_0 = const()[name = string("matrix_bd_43_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_43_end_mask_0 = const()[name = string("matrix_bd_43_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2313_cast_fp16 = add(x = matrix_ac_21_cast_fp16, y = matrix_bd_43_cast_fp16)[name = string("op_2313_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<fp16, [1, 8, 188, 188]> _inversed_scores_41_cast_fp16 = mul(x = var_2313_cast_fp16, y = _inversed_scores_41_y_0_to_fp16)[name = string("_inversed_scores_41_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_43_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_41_cast_fp16, cond = mask_15)[name = string("scores_43_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2319_cast_fp16 = softmax(axis = var_152, x = scores_43_cast_fp16)[name = string("op_2319_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_561_cast_fp16 = select(a = var_164_to_fp16, b = var_2319_cast_fp16, cond = mask_15)[name = string("input_561_cast_fp16")];
+            bool x_253_transpose_x_0 = const()[name = string("x_253_transpose_x_0"), val = bool(false)];
+            bool x_253_transpose_y_0 = const()[name = string("x_253_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_25_cast_fp16 = transpose(perm = value_25_perm_0, x = v_21_cast_fp16)[name = string("transpose_239")];
+            tensor<fp16, [1, 8, 188, 128]> x_253_cast_fp16 = matmul(transpose_x = x_253_transpose_x_0, transpose_y = x_253_transpose_y_0, x = input_561_cast_fp16, y = value_25_cast_fp16)[name = string("x_253_cast_fp16")];
+            tensor<int32, [4]> var_2323_perm_0 = const()[name = string("op_2323_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2324 = const()[name = string("op_2324"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2323_cast_fp16 = transpose(perm = var_2323_perm_0, x = x_253_cast_fp16)[name = string("transpose_238")];
+            tensor<fp16, [1, 188, 1024]> input_563_cast_fp16 = reshape(shape = var_2324, x = var_2323_cast_fp16)[name = string("input_563_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_10_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262446720))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263497472))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263495360))))[name = string("encoder_module_layers_10_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_10_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263498560)))];
+            tensor<fp16, [1, 188, 1024]> linear_97_cast_fp16 = linear(bias = encoder_module_layers_10_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_10_self_attn_linear_out_weight_to_fp16_quantized, x = input_563_cast_fp16)[name = string("linear_97_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_567_cast_fp16 = add(x = input_559_cast_fp16, y = linear_97_cast_fp16)[name = string("input_567_cast_fp16")];
+            tensor<int32, [1]> x_257_axes_0 = const()[name = string("x_257_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263500672)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263502784)))];
+            tensor<fp16, [1, 188, 1024]> x_257_cast_fp16 = layer_norm(axes = x_257_axes_0, beta = encoder_module_layers_10_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_conv_weight_to_fp16, x = input_567_cast_fp16)[name = string("x_257_cast_fp16")];
+            tensor<int32, [3]> input_569_perm_0 = const()[name = string("input_569_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_571_pad_type_0 = const()[name = string("input_571_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_571_strides_0 = const()[name = string("input_571_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_571_pad_0 = const()[name = string("input_571_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_571_dilations_0 = const()[name = string("input_571_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_571_groups_0 = const()[name = string("input_571_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263504896))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265606272))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265602112))))[name = string("encoder_module_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265608384)))];
+            tensor<fp16, [1, 1024, 188]> input_569_cast_fp16 = transpose(perm = input_569_perm_0, x = x_257_cast_fp16)[name = string("transpose_237")];
+            tensor<fp16, [1, 2048, 188]> input_571_cast_fp16 = conv(bias = encoder_module_layers_10_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_10_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_569_cast_fp16)[name = string("input_571_cast_fp16")];
+            int32 x_259_split_num_splits_0 = const()[name = string("x_259_split_num_splits_0"), val = int32(2)];
+            int32 x_259_split_axis_0 = const()[name = string("x_259_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_259_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_259_split_cast_fp16_1 = split(axis = x_259_split_axis_0, num_splits = x_259_split_num_splits_0, x = input_571_cast_fp16)[name = string("x_259_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_259_split_1_sigmoid_cast_fp16 = sigmoid(x = x_259_split_cast_fp16_1)[name = string("x_259_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_259_cast_fp16 = mul(x = x_259_split_cast_fp16_0, y = x_259_split_1_sigmoid_cast_fp16)[name = string("x_259_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_573_cast_fp16 = select(a = var_164_to_fp16, b = x_259_cast_fp16, cond = var_608)[name = string("input_573_cast_fp16")];
+            tensor<int32, [6]> input_575_pad_0 = const()[name = string("input_575_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_575_mode_0 = const()[name = string("input_575_mode_0"), val = string("constant")];
+            fp16 const_191_to_fp16 = const()[name = string("const_191_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_575_cast_fp16 = pad(constant_val = const_191_to_fp16, mode = input_575_mode_0, pad = input_575_pad_0, x = input_573_cast_fp16)[name = string("input_575_cast_fp16")];
+            string input_577_pad_type_0 = const()[name = string("input_577_pad_type_0"), val = string("valid")];
+            int32 input_577_groups_0 = const()[name = string("input_577_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_577_strides_0 = const()[name = string("input_577_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_577_pad_0 = const()[name = string("input_577_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_577_dilations_0 = const()[name = string("input_577_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_342_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265612544))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265623936))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265621824))))[name = string("const_342_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_343_to_fp16 = const()[name = string("const_343_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265625024)))];
+            tensor<fp16, [1, 1024, 188]> input_579_cast_fp16 = conv(bias = const_343_to_fp16, dilations = input_577_dilations_0, groups = input_577_groups_0, pad = input_577_pad_0, pad_type = input_577_pad_type_0, strides = input_577_strides_0, weight = const_342_to_fp16_quantized, x = input_575_cast_fp16)[name = string("input_579_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_581_cast_fp16 = silu(x = input_579_cast_fp16)[name = string("input_581_cast_fp16")];
+            string x_261_pad_type_0 = const()[name = string("x_261_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_261_strides_0 = const()[name = string("x_261_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_261_pad_0 = const()[name = string("x_261_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_261_dilations_0 = const()[name = string("x_261_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_261_groups_0 = const()[name = string("x_261_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265627136))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266677888))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266675776))))[name = string("encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266678976)))];
+            tensor<fp16, [1, 1024, 188]> x_261_cast_fp16 = conv(bias = encoder_module_layers_10_conv_pointwise_conv2_bias_to_fp16, dilations = x_261_dilations_0, groups = x_261_groups_0, pad = x_261_pad_0, pad_type = x_261_pad_type_0, strides = x_261_strides_0, weight = encoder_module_layers_10_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_581_cast_fp16)[name = string("x_261_cast_fp16")];
+            tensor<int32, [3]> input_583_perm_0 = const()[name = string("input_583_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_583_cast_fp16 = transpose(perm = input_583_perm_0, x = x_261_cast_fp16)[name = string("transpose_236")];
+            tensor<fp16, [1, 188, 1024]> input_585_cast_fp16 = add(x = input_567_cast_fp16, y = input_583_cast_fp16)[name = string("input_585_cast_fp16")];
+            tensor<int32, [1]> input_587_axes_0 = const()[name = string("input_587_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266681088)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266683200)))];
+            tensor<fp16, [1, 188, 1024]> input_587_cast_fp16 = layer_norm(axes = input_587_axes_0, beta = encoder_module_layers_10_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_feed_forward2_weight_to_fp16, x = input_585_cast_fp16)[name = string("input_587_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(266685312))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270887936))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270879680))))[name = string("encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270892096)))];
+            tensor<fp16, [1, 188, 4096]> linear_98_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_10_feed_forward2_linear1_weight_to_fp16_quantized, x = input_587_cast_fp16)[name = string("linear_98_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_591_cast_fp16 = silu(x = linear_98_cast_fp16)[name = string("input_591_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270900352))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275096832))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275094720))))[name = string("encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275097920)))];
+            tensor<fp16, [1, 188, 1024]> linear_99_cast_fp16 = linear(bias = encoder_module_layers_10_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_10_feed_forward2_linear2_weight_to_fp16_quantized, x = input_591_cast_fp16)[name = string("linear_99_cast_fp16")];
+            fp16 var_2390_to_fp16 = const()[name = string("op_2390_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2391_cast_fp16 = mul(x = linear_99_cast_fp16, y = var_2390_to_fp16)[name = string("op_2391_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_597_cast_fp16 = add(x = input_585_cast_fp16, y = var_2391_cast_fp16)[name = string("input_597_cast_fp16")];
+            tensor<int32, [1]> input_599_axes_0 = const()[name = string("input_599_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_10_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275100032)))];
+            tensor<fp16, [1024]> encoder_module_layers_10_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_10_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275102144)))];
+            tensor<fp16, [1, 188, 1024]> input_599_cast_fp16 = layer_norm(axes = input_599_axes_0, beta = encoder_module_layers_10_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_10_norm_out_weight_to_fp16, x = input_597_cast_fp16)[name = string("input_599_cast_fp16")];
+            tensor<int32, [1]> input_601_axes_0 = const()[name = string("input_601_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275104256)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275106368)))];
+            tensor<fp16, [1, 188, 1024]> input_601_cast_fp16 = layer_norm(axes = input_601_axes_0, beta = encoder_module_layers_11_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_feed_forward1_weight_to_fp16, x = input_599_cast_fp16)[name = string("input_601_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(275108480))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279311104))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279302848))))[name = string("encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279315264)))];
+            tensor<fp16, [1, 188, 4096]> linear_100_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_11_feed_forward1_linear1_weight_to_fp16_quantized, x = input_601_cast_fp16)[name = string("linear_100_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_605_cast_fp16 = silu(x = linear_100_cast_fp16)[name = string("input_605_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(279323520))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283520000))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283517888))))[name = string("encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283521088)))];
+            tensor<fp16, [1, 188, 1024]> linear_101_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_11_feed_forward1_linear2_weight_to_fp16_quantized, x = input_605_cast_fp16)[name = string("linear_101_cast_fp16")];
+            fp16 var_2421_to_fp16 = const()[name = string("op_2421_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2422_cast_fp16 = mul(x = linear_101_cast_fp16, y = var_2421_to_fp16)[name = string("op_2422_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_611_cast_fp16 = add(x = input_599_cast_fp16, y = var_2422_cast_fp16)[name = string("input_611_cast_fp16")];
+            tensor<int32, [1]> query_23_axes_0 = const()[name = string("query_23_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283523200)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283525312)))];
+            tensor<fp16, [1, 188, 1024]> query_23_cast_fp16 = layer_norm(axes = query_23_axes_0, beta = encoder_module_layers_11_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_self_att_weight_to_fp16, x = input_611_cast_fp16)[name = string("query_23_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(283527424))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284578176))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284576064))))[name = string("encoder_module_layers_11_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284579264)))];
+            tensor<fp16, [1, 188, 1024]> linear_102_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_q_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = string("linear_102_cast_fp16")];
+            tensor<int32, [4]> var_2439 = const()[name = string("op_2439"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_67_cast_fp16 = reshape(shape = var_2439, x = linear_102_cast_fp16)[name = string("q_67_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284581376))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285632128))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285630016))))[name = string("encoder_module_layers_11_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285633216)))];
+            tensor<fp16, [1, 188, 1024]> linear_103_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_k_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = string("linear_103_cast_fp16")];
+            tensor<int32, [4]> var_2444 = const()[name = string("op_2444"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_45_cast_fp16 = reshape(shape = var_2444, x = linear_103_cast_fp16)[name = string("k_45_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(285635328))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286686080))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286683968))))[name = string("encoder_module_layers_11_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286687168)))];
+            tensor<fp16, [1, 188, 1024]> linear_104_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_v_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = string("linear_104_cast_fp16")];
+            tensor<int32, [4]> var_2449 = const()[name = string("op_2449"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_23_cast_fp16 = reshape(shape = var_2449, x = linear_104_cast_fp16)[name = string("v_23_cast_fp16")];
+            tensor<int32, [4]> value_27_perm_0 = const()[name = string("value_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_11_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286689280)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2461_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_module_layers_11_self_attn_pos_bias_u_to_fp16)[name = string("op_2461_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_11_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286691392)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2463_cast_fp16 = add(x = q_67_cast_fp16, y = encoder_module_layers_11_self_attn_pos_bias_v_to_fp16)[name = string("op_2463_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_23_perm_0 = const()[name = string("q_with_bias_v_23_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_269_transpose_x_0 = const()[name = string("x_269_transpose_x_0"), val = bool(false)];
+            bool x_269_transpose_y_0 = const()[name = string("x_269_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2465_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(286693504))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287078400))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287077568))))[name = string("op_2465_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_23_cast_fp16 = transpose(perm = q_with_bias_v_23_perm_0, x = var_2463_cast_fp16)[name = string("transpose_235")];
+            tensor<fp16, [1, 8, 188, 375]> x_269_cast_fp16 = matmul(transpose_x = x_269_transpose_x_0, transpose_y = x_269_transpose_y_0, x = q_with_bias_v_23_cast_fp16, y = op_2465_to_fp16_quantized)[name = string("x_269_cast_fp16")];
+            tensor<int32, [8]> x_271_pad_0 = const()[name = string("x_271_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_271_mode_0 = const()[name = string("x_271_mode_0"), val = string("constant")];
+            fp16 const_198_to_fp16 = const()[name = string("const_198_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_271_cast_fp16 = pad(constant_val = const_198_to_fp16, mode = x_271_mode_0, pad = x_271_pad_0, x = x_269_cast_fp16)[name = string("x_271_cast_fp16")];
+            tensor<int32, [4]> var_2473 = const()[name = string("op_2473"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_273_cast_fp16 = reshape(shape = var_2473, x = x_271_cast_fp16)[name = string("x_273_cast_fp16")];
+            tensor<int32, [4]> var_2477_begin_0 = const()[name = string("op_2477_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2477_end_0 = const()[name = string("op_2477_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2477_end_mask_0 = const()[name = string("op_2477_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2477_cast_fp16 = slice_by_index(begin = var_2477_begin_0, end = var_2477_end_0, end_mask = var_2477_end_mask_0, x = x_273_cast_fp16)[name = string("op_2477_cast_fp16")];
+            tensor<int32, [4]> var_2478 = const()[name = string("op_2478"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_45_cast_fp16 = reshape(shape = var_2478, x = var_2477_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<int32, [4]> transpose_118_perm_0 = const()[name = string("transpose_118_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_119_perm_0 = const()[name = string("transpose_119_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_119 = transpose(perm = transpose_119_perm_0, x = k_45_cast_fp16)[name = string("transpose_233")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_118 = transpose(perm = transpose_118_perm_0, x = var_2461_cast_fp16)[name = string("transpose_234")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_47_begin_0 = const()[name = string("matrix_bd_47_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_47_end_0 = const()[name = string("matrix_bd_47_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_47_end_mask_0 = const()[name = string("matrix_bd_47_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2487_cast_fp16 = add(x = matrix_ac_23_cast_fp16, y = matrix_bd_47_cast_fp16)[name = string("op_2487_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<fp16, [1, 8, 188, 188]> _inversed_scores_45_cast_fp16 = mul(x = var_2487_cast_fp16, y = _inversed_scores_45_y_0_to_fp16)[name = string("_inversed_scores_45_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_47_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_45_cast_fp16, cond = mask_15)[name = string("scores_47_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2493_cast_fp16 = softmax(axis = var_152, x = scores_47_cast_fp16)[name = string("op_2493_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_613_cast_fp16 = select(a = var_164_to_fp16, b = var_2493_cast_fp16, cond = mask_15)[name = string("input_613_cast_fp16")];
+            bool x_275_transpose_x_0 = const()[name = string("x_275_transpose_x_0"), val = bool(false)];
+            bool x_275_transpose_y_0 = const()[name = string("x_275_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_27_cast_fp16 = transpose(perm = value_27_perm_0, x = v_23_cast_fp16)[name = string("transpose_232")];
+            tensor<fp16, [1, 8, 188, 128]> x_275_cast_fp16 = matmul(transpose_x = x_275_transpose_x_0, transpose_y = x_275_transpose_y_0, x = input_613_cast_fp16, y = value_27_cast_fp16)[name = string("x_275_cast_fp16")];
+            tensor<int32, [4]> var_2497_perm_0 = const()[name = string("op_2497_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2498 = const()[name = string("op_2498"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2497_cast_fp16 = transpose(perm = var_2497_perm_0, x = x_275_cast_fp16)[name = string("transpose_231")];
+            tensor<fp16, [1, 188, 1024]> input_615_cast_fp16 = reshape(shape = var_2498, x = var_2497_cast_fp16)[name = string("input_615_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_11_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(287078848))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288129600))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288127488))))[name = string("encoder_module_layers_11_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_11_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288130688)))];
+            tensor<fp16, [1, 188, 1024]> linear_106_cast_fp16 = linear(bias = encoder_module_layers_11_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_11_self_attn_linear_out_weight_to_fp16_quantized, x = input_615_cast_fp16)[name = string("linear_106_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_619_cast_fp16 = add(x = input_611_cast_fp16, y = linear_106_cast_fp16)[name = string("input_619_cast_fp16")];
+            tensor<int32, [1]> x_279_axes_0 = const()[name = string("x_279_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288132800)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288134912)))];
+            tensor<fp16, [1, 188, 1024]> x_279_cast_fp16 = layer_norm(axes = x_279_axes_0, beta = encoder_module_layers_11_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_conv_weight_to_fp16, x = input_619_cast_fp16)[name = string("x_279_cast_fp16")];
+            tensor<int32, [3]> input_621_perm_0 = const()[name = string("input_621_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_623_pad_type_0 = const()[name = string("input_623_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_623_strides_0 = const()[name = string("input_623_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_623_pad_0 = const()[name = string("input_623_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_623_dilations_0 = const()[name = string("input_623_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_623_groups_0 = const()[name = string("input_623_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(288137024))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290238400))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290234240))))[name = string("encoder_module_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290240512)))];
+            tensor<fp16, [1, 1024, 188]> input_621_cast_fp16 = transpose(perm = input_621_perm_0, x = x_279_cast_fp16)[name = string("transpose_230")];
+            tensor<fp16, [1, 2048, 188]> input_623_cast_fp16 = conv(bias = encoder_module_layers_11_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_11_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_621_cast_fp16)[name = string("input_623_cast_fp16")];
+            int32 x_281_split_num_splits_0 = const()[name = string("x_281_split_num_splits_0"), val = int32(2)];
+            int32 x_281_split_axis_0 = const()[name = string("x_281_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_281_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_281_split_cast_fp16_1 = split(axis = x_281_split_axis_0, num_splits = x_281_split_num_splits_0, x = input_623_cast_fp16)[name = string("x_281_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_281_split_1_sigmoid_cast_fp16 = sigmoid(x = x_281_split_cast_fp16_1)[name = string("x_281_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_281_cast_fp16 = mul(x = x_281_split_cast_fp16_0, y = x_281_split_1_sigmoid_cast_fp16)[name = string("x_281_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_625_cast_fp16 = select(a = var_164_to_fp16, b = x_281_cast_fp16, cond = var_608)[name = string("input_625_cast_fp16")];
+            tensor<int32, [6]> input_627_pad_0 = const()[name = string("input_627_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_627_mode_0 = const()[name = string("input_627_mode_0"), val = string("constant")];
+            fp16 const_201_to_fp16 = const()[name = string("const_201_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_627_cast_fp16 = pad(constant_val = const_201_to_fp16, mode = input_627_mode_0, pad = input_627_pad_0, x = input_625_cast_fp16)[name = string("input_627_cast_fp16")];
+            string input_629_pad_type_0 = const()[name = string("input_629_pad_type_0"), val = string("valid")];
+            int32 input_629_groups_0 = const()[name = string("input_629_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_629_strides_0 = const()[name = string("input_629_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_629_pad_0 = const()[name = string("input_629_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_629_dilations_0 = const()[name = string("input_629_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_344_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290244672))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290256064))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290253952))))[name = string("const_344_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_345_to_fp16 = const()[name = string("const_345_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290257152)))];
+            tensor<fp16, [1, 1024, 188]> input_631_cast_fp16 = conv(bias = const_345_to_fp16, dilations = input_629_dilations_0, groups = input_629_groups_0, pad = input_629_pad_0, pad_type = input_629_pad_type_0, strides = input_629_strides_0, weight = const_344_to_fp16_quantized, x = input_627_cast_fp16)[name = string("input_631_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_633_cast_fp16 = silu(x = input_631_cast_fp16)[name = string("input_633_cast_fp16")];
+            string x_283_pad_type_0 = const()[name = string("x_283_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_283_strides_0 = const()[name = string("x_283_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_283_pad_0 = const()[name = string("x_283_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_283_dilations_0 = const()[name = string("x_283_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_283_groups_0 = const()[name = string("x_283_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290259264))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291310016))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291307904))))[name = string("encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291311104)))];
+            tensor<fp16, [1, 1024, 188]> x_283_cast_fp16 = conv(bias = encoder_module_layers_11_conv_pointwise_conv2_bias_to_fp16, dilations = x_283_dilations_0, groups = x_283_groups_0, pad = x_283_pad_0, pad_type = x_283_pad_type_0, strides = x_283_strides_0, weight = encoder_module_layers_11_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_633_cast_fp16)[name = string("x_283_cast_fp16")];
+            tensor<int32, [3]> input_635_perm_0 = const()[name = string("input_635_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_635_cast_fp16 = transpose(perm = input_635_perm_0, x = x_283_cast_fp16)[name = string("transpose_229")];
+            tensor<fp16, [1, 188, 1024]> input_637_cast_fp16 = add(x = input_619_cast_fp16, y = input_635_cast_fp16)[name = string("input_637_cast_fp16")];
+            tensor<int32, [1]> input_639_axes_0 = const()[name = string("input_639_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291313216)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291315328)))];
+            tensor<fp16, [1, 188, 1024]> input_639_cast_fp16 = layer_norm(axes = input_639_axes_0, beta = encoder_module_layers_11_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_feed_forward2_weight_to_fp16, x = input_637_cast_fp16)[name = string("input_639_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(291317440))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295520064))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295511808))))[name = string("encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295524224)))];
+            tensor<fp16, [1, 188, 4096]> linear_107_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_11_feed_forward2_linear1_weight_to_fp16_quantized, x = input_639_cast_fp16)[name = string("linear_107_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_643_cast_fp16 = silu(x = linear_107_cast_fp16)[name = string("input_643_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(295532480))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299728960))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299726848))))[name = string("encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299730048)))];
+            tensor<fp16, [1, 188, 1024]> linear_108_cast_fp16 = linear(bias = encoder_module_layers_11_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_11_feed_forward2_linear2_weight_to_fp16_quantized, x = input_643_cast_fp16)[name = string("linear_108_cast_fp16")];
+            fp16 var_2564_to_fp16 = const()[name = string("op_2564_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2565_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_2564_to_fp16)[name = string("op_2565_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_649_cast_fp16 = add(x = input_637_cast_fp16, y = var_2565_cast_fp16)[name = string("input_649_cast_fp16")];
+            tensor<int32, [1]> input_651_axes_0 = const()[name = string("input_651_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_11_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299732160)))];
+            tensor<fp16, [1024]> encoder_module_layers_11_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_11_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299734272)))];
+            tensor<fp16, [1, 188, 1024]> input_651_cast_fp16 = layer_norm(axes = input_651_axes_0, beta = encoder_module_layers_11_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_11_norm_out_weight_to_fp16, x = input_649_cast_fp16)[name = string("input_651_cast_fp16")];
+            tensor<int32, [1]> input_653_axes_0 = const()[name = string("input_653_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299736384)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299738496)))];
+            tensor<fp16, [1, 188, 1024]> input_653_cast_fp16 = layer_norm(axes = input_653_axes_0, beta = encoder_module_layers_12_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_feed_forward1_weight_to_fp16, x = input_651_cast_fp16)[name = string("input_653_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299740608))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303943232))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303934976))))[name = string("encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303947392)))];
+            tensor<fp16, [1, 188, 4096]> linear_109_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_12_feed_forward1_linear1_weight_to_fp16_quantized, x = input_653_cast_fp16)[name = string("linear_109_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_657_cast_fp16 = silu(x = linear_109_cast_fp16)[name = string("input_657_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303955648))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308152128))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308150016))))[name = string("encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308153216)))];
+            tensor<fp16, [1, 188, 1024]> linear_110_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_12_feed_forward1_linear2_weight_to_fp16_quantized, x = input_657_cast_fp16)[name = string("linear_110_cast_fp16")];
+            fp16 var_2595_to_fp16 = const()[name = string("op_2595_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2596_cast_fp16 = mul(x = linear_110_cast_fp16, y = var_2595_to_fp16)[name = string("op_2596_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_663_cast_fp16 = add(x = input_651_cast_fp16, y = var_2596_cast_fp16)[name = string("input_663_cast_fp16")];
+            tensor<int32, [1]> query_25_axes_0 = const()[name = string("query_25_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308155328)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308157440)))];
+            tensor<fp16, [1, 188, 1024]> query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = encoder_module_layers_12_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_self_att_weight_to_fp16, x = input_663_cast_fp16)[name = string("query_25_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308159552))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309210304))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309208192))))[name = string("encoder_module_layers_12_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309211392)))];
+            tensor<fp16, [1, 188, 1024]> linear_111_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_q_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = string("linear_111_cast_fp16")];
+            tensor<int32, [4]> var_2613 = const()[name = string("op_2613"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_73_cast_fp16 = reshape(shape = var_2613, x = linear_111_cast_fp16)[name = string("q_73_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(309213504))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310264256))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310262144))))[name = string("encoder_module_layers_12_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310265344)))];
+            tensor<fp16, [1, 188, 1024]> linear_112_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_k_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = string("linear_112_cast_fp16")];
+            tensor<int32, [4]> var_2618 = const()[name = string("op_2618"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_49_cast_fp16 = reshape(shape = var_2618, x = linear_112_cast_fp16)[name = string("k_49_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(310267456))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311318208))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311316096))))[name = string("encoder_module_layers_12_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311319296)))];
+            tensor<fp16, [1, 188, 1024]> linear_113_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_v_weight_to_fp16_quantized, x = query_25_cast_fp16)[name = string("linear_113_cast_fp16")];
+            tensor<int32, [4]> var_2623 = const()[name = string("op_2623"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_25_cast_fp16 = reshape(shape = var_2623, x = linear_113_cast_fp16)[name = string("v_25_cast_fp16")];
+            tensor<int32, [4]> value_29_perm_0 = const()[name = string("value_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_12_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311321408)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2635_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_module_layers_12_self_attn_pos_bias_u_to_fp16)[name = string("op_2635_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_12_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311323520)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2637_cast_fp16 = add(x = q_73_cast_fp16, y = encoder_module_layers_12_self_attn_pos_bias_v_to_fp16)[name = string("op_2637_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_25_perm_0 = const()[name = string("q_with_bias_v_25_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_291_transpose_x_0 = const()[name = string("x_291_transpose_x_0"), val = bool(false)];
+            bool x_291_transpose_y_0 = const()[name = string("x_291_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2639_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311325632))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311710528))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311709696))))[name = string("op_2639_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_25_cast_fp16 = transpose(perm = q_with_bias_v_25_perm_0, x = var_2637_cast_fp16)[name = string("transpose_228")];
+            tensor<fp16, [1, 8, 188, 375]> x_291_cast_fp16 = matmul(transpose_x = x_291_transpose_x_0, transpose_y = x_291_transpose_y_0, x = q_with_bias_v_25_cast_fp16, y = op_2639_to_fp16_quantized)[name = string("x_291_cast_fp16")];
+            tensor<int32, [8]> x_293_pad_0 = const()[name = string("x_293_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_293_mode_0 = const()[name = string("x_293_mode_0"), val = string("constant")];
+            fp16 const_208_to_fp16 = const()[name = string("const_208_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_293_cast_fp16 = pad(constant_val = const_208_to_fp16, mode = x_293_mode_0, pad = x_293_pad_0, x = x_291_cast_fp16)[name = string("x_293_cast_fp16")];
+            tensor<int32, [4]> var_2647 = const()[name = string("op_2647"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_295_cast_fp16 = reshape(shape = var_2647, x = x_293_cast_fp16)[name = string("x_295_cast_fp16")];
+            tensor<int32, [4]> var_2651_begin_0 = const()[name = string("op_2651_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2651_end_0 = const()[name = string("op_2651_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2651_end_mask_0 = const()[name = string("op_2651_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2651_cast_fp16 = slice_by_index(begin = var_2651_begin_0, end = var_2651_end_0, end_mask = var_2651_end_mask_0, x = x_295_cast_fp16)[name = string("op_2651_cast_fp16")];
+            tensor<int32, [4]> var_2652 = const()[name = string("op_2652"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_49_cast_fp16 = reshape(shape = var_2652, x = var_2651_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<int32, [4]> transpose_120_perm_0 = const()[name = string("transpose_120_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_121_perm_0 = const()[name = string("transpose_121_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_121 = transpose(perm = transpose_121_perm_0, x = k_49_cast_fp16)[name = string("transpose_226")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_120 = transpose(perm = transpose_120_perm_0, x = var_2635_cast_fp16)[name = string("transpose_227")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_51_begin_0 = const()[name = string("matrix_bd_51_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_51_end_0 = const()[name = string("matrix_bd_51_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_51_end_mask_0 = const()[name = string("matrix_bd_51_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2661_cast_fp16 = add(x = matrix_ac_25_cast_fp16, y = matrix_bd_51_cast_fp16)[name = string("op_2661_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<fp16, [1, 8, 188, 188]> _inversed_scores_49_cast_fp16 = mul(x = var_2661_cast_fp16, y = _inversed_scores_49_y_0_to_fp16)[name = string("_inversed_scores_49_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_51_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_49_cast_fp16, cond = mask_15)[name = string("scores_51_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2667_cast_fp16 = softmax(axis = var_152, x = scores_51_cast_fp16)[name = string("op_2667_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_665_cast_fp16 = select(a = var_164_to_fp16, b = var_2667_cast_fp16, cond = mask_15)[name = string("input_665_cast_fp16")];
+            bool x_297_transpose_x_0 = const()[name = string("x_297_transpose_x_0"), val = bool(false)];
+            bool x_297_transpose_y_0 = const()[name = string("x_297_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_29_cast_fp16 = transpose(perm = value_29_perm_0, x = v_25_cast_fp16)[name = string("transpose_225")];
+            tensor<fp16, [1, 8, 188, 128]> x_297_cast_fp16 = matmul(transpose_x = x_297_transpose_x_0, transpose_y = x_297_transpose_y_0, x = input_665_cast_fp16, y = value_29_cast_fp16)[name = string("x_297_cast_fp16")];
+            tensor<int32, [4]> var_2671_perm_0 = const()[name = string("op_2671_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2672 = const()[name = string("op_2672"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2671_cast_fp16 = transpose(perm = var_2671_perm_0, x = x_297_cast_fp16)[name = string("transpose_224")];
+            tensor<fp16, [1, 188, 1024]> input_667_cast_fp16 = reshape(shape = var_2672, x = var_2671_cast_fp16)[name = string("input_667_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_12_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(311710976))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312761728))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312759616))))[name = string("encoder_module_layers_12_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_12_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312762816)))];
+            tensor<fp16, [1, 188, 1024]> linear_115_cast_fp16 = linear(bias = encoder_module_layers_12_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_12_self_attn_linear_out_weight_to_fp16_quantized, x = input_667_cast_fp16)[name = string("linear_115_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_671_cast_fp16 = add(x = input_663_cast_fp16, y = linear_115_cast_fp16)[name = string("input_671_cast_fp16")];
+            tensor<int32, [1]> x_301_axes_0 = const()[name = string("x_301_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312764928)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312767040)))];
+            tensor<fp16, [1, 188, 1024]> x_301_cast_fp16 = layer_norm(axes = x_301_axes_0, beta = encoder_module_layers_12_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_conv_weight_to_fp16, x = input_671_cast_fp16)[name = string("x_301_cast_fp16")];
+            tensor<int32, [3]> input_673_perm_0 = const()[name = string("input_673_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_675_pad_type_0 = const()[name = string("input_675_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_675_strides_0 = const()[name = string("input_675_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_675_pad_0 = const()[name = string("input_675_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_675_dilations_0 = const()[name = string("input_675_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_675_groups_0 = const()[name = string("input_675_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312769152))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314870528))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314866368))))[name = string("encoder_module_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314872640)))];
+            tensor<fp16, [1, 1024, 188]> input_673_cast_fp16 = transpose(perm = input_673_perm_0, x = x_301_cast_fp16)[name = string("transpose_223")];
+            tensor<fp16, [1, 2048, 188]> input_675_cast_fp16 = conv(bias = encoder_module_layers_12_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_12_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_673_cast_fp16)[name = string("input_675_cast_fp16")];
+            int32 x_303_split_num_splits_0 = const()[name = string("x_303_split_num_splits_0"), val = int32(2)];
+            int32 x_303_split_axis_0 = const()[name = string("x_303_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_303_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_303_split_cast_fp16_1 = split(axis = x_303_split_axis_0, num_splits = x_303_split_num_splits_0, x = input_675_cast_fp16)[name = string("x_303_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_303_split_1_sigmoid_cast_fp16 = sigmoid(x = x_303_split_cast_fp16_1)[name = string("x_303_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_303_cast_fp16 = mul(x = x_303_split_cast_fp16_0, y = x_303_split_1_sigmoid_cast_fp16)[name = string("x_303_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_677_cast_fp16 = select(a = var_164_to_fp16, b = x_303_cast_fp16, cond = var_608)[name = string("input_677_cast_fp16")];
+            tensor<int32, [6]> input_679_pad_0 = const()[name = string("input_679_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_679_mode_0 = const()[name = string("input_679_mode_0"), val = string("constant")];
+            fp16 const_211_to_fp16 = const()[name = string("const_211_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_679_cast_fp16 = pad(constant_val = const_211_to_fp16, mode = input_679_mode_0, pad = input_679_pad_0, x = input_677_cast_fp16)[name = string("input_679_cast_fp16")];
+            string input_681_pad_type_0 = const()[name = string("input_681_pad_type_0"), val = string("valid")];
+            int32 input_681_groups_0 = const()[name = string("input_681_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_681_strides_0 = const()[name = string("input_681_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_681_pad_0 = const()[name = string("input_681_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_681_dilations_0 = const()[name = string("input_681_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_346_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314876800))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314888192))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314886080))))[name = string("const_346_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_347_to_fp16 = const()[name = string("const_347_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314889280)))];
+            tensor<fp16, [1, 1024, 188]> input_683_cast_fp16 = conv(bias = const_347_to_fp16, dilations = input_681_dilations_0, groups = input_681_groups_0, pad = input_681_pad_0, pad_type = input_681_pad_type_0, strides = input_681_strides_0, weight = const_346_to_fp16_quantized, x = input_679_cast_fp16)[name = string("input_683_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_685_cast_fp16 = silu(x = input_683_cast_fp16)[name = string("input_685_cast_fp16")];
+            string x_305_pad_type_0 = const()[name = string("x_305_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_305_strides_0 = const()[name = string("x_305_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_305_pad_0 = const()[name = string("x_305_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_305_dilations_0 = const()[name = string("x_305_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_305_groups_0 = const()[name = string("x_305_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314891392))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315942144))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315940032))))[name = string("encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315943232)))];
+            tensor<fp16, [1, 1024, 188]> x_305_cast_fp16 = conv(bias = encoder_module_layers_12_conv_pointwise_conv2_bias_to_fp16, dilations = x_305_dilations_0, groups = x_305_groups_0, pad = x_305_pad_0, pad_type = x_305_pad_type_0, strides = x_305_strides_0, weight = encoder_module_layers_12_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_685_cast_fp16)[name = string("x_305_cast_fp16")];
+            tensor<int32, [3]> input_687_perm_0 = const()[name = string("input_687_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_687_cast_fp16 = transpose(perm = input_687_perm_0, x = x_305_cast_fp16)[name = string("transpose_222")];
+            tensor<fp16, [1, 188, 1024]> input_689_cast_fp16 = add(x = input_671_cast_fp16, y = input_687_cast_fp16)[name = string("input_689_cast_fp16")];
+            tensor<int32, [1]> input_691_axes_0 = const()[name = string("input_691_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315945344)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315947456)))];
+            tensor<fp16, [1, 188, 1024]> input_691_cast_fp16 = layer_norm(axes = input_691_axes_0, beta = encoder_module_layers_12_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_feed_forward2_weight_to_fp16, x = input_689_cast_fp16)[name = string("input_691_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315949568))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320152192))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320143936))))[name = string("encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320156352)))];
+            tensor<fp16, [1, 188, 4096]> linear_116_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_12_feed_forward2_linear1_weight_to_fp16_quantized, x = input_691_cast_fp16)[name = string("linear_116_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_695_cast_fp16 = silu(x = linear_116_cast_fp16)[name = string("input_695_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320164608))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324361088))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324358976))))[name = string("encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324362176)))];
+            tensor<fp16, [1, 188, 1024]> linear_117_cast_fp16 = linear(bias = encoder_module_layers_12_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_12_feed_forward2_linear2_weight_to_fp16_quantized, x = input_695_cast_fp16)[name = string("linear_117_cast_fp16")];
+            fp16 var_2738_to_fp16 = const()[name = string("op_2738_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2739_cast_fp16 = mul(x = linear_117_cast_fp16, y = var_2738_to_fp16)[name = string("op_2739_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_701_cast_fp16 = add(x = input_689_cast_fp16, y = var_2739_cast_fp16)[name = string("input_701_cast_fp16")];
+            tensor<int32, [1]> input_703_axes_0 = const()[name = string("input_703_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_12_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324364288)))];
+            tensor<fp16, [1024]> encoder_module_layers_12_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_12_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324366400)))];
+            tensor<fp16, [1, 188, 1024]> input_703_cast_fp16 = layer_norm(axes = input_703_axes_0, beta = encoder_module_layers_12_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_12_norm_out_weight_to_fp16, x = input_701_cast_fp16)[name = string("input_703_cast_fp16")];
+            tensor<int32, [1]> input_705_axes_0 = const()[name = string("input_705_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324368512)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324370624)))];
+            tensor<fp16, [1, 188, 1024]> input_705_cast_fp16 = layer_norm(axes = input_705_axes_0, beta = encoder_module_layers_13_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_feed_forward1_weight_to_fp16, x = input_703_cast_fp16)[name = string("input_705_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324372736))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328575360))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328567104))))[name = string("encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328579520)))];
+            tensor<fp16, [1, 188, 4096]> linear_118_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_13_feed_forward1_linear1_weight_to_fp16_quantized, x = input_705_cast_fp16)[name = string("linear_118_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_709_cast_fp16 = silu(x = linear_118_cast_fp16)[name = string("input_709_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328587776))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332784256))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332782144))))[name = string("encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332785344)))];
+            tensor<fp16, [1, 188, 1024]> linear_119_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_13_feed_forward1_linear2_weight_to_fp16_quantized, x = input_709_cast_fp16)[name = string("linear_119_cast_fp16")];
+            fp16 var_2769_to_fp16 = const()[name = string("op_2769_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2770_cast_fp16 = mul(x = linear_119_cast_fp16, y = var_2769_to_fp16)[name = string("op_2770_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_715_cast_fp16 = add(x = input_703_cast_fp16, y = var_2770_cast_fp16)[name = string("input_715_cast_fp16")];
+            tensor<int32, [1]> query_27_axes_0 = const()[name = string("query_27_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332787456)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332789568)))];
+            tensor<fp16, [1, 188, 1024]> query_27_cast_fp16 = layer_norm(axes = query_27_axes_0, beta = encoder_module_layers_13_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_self_att_weight_to_fp16, x = input_715_cast_fp16)[name = string("query_27_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332791680))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333842432))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333840320))))[name = string("encoder_module_layers_13_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333843520)))];
+            tensor<fp16, [1, 188, 1024]> linear_120_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_q_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = string("linear_120_cast_fp16")];
+            tensor<int32, [4]> var_2787 = const()[name = string("op_2787"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_79_cast_fp16 = reshape(shape = var_2787, x = linear_120_cast_fp16)[name = string("q_79_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333845632))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334896384))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334894272))))[name = string("encoder_module_layers_13_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334897472)))];
+            tensor<fp16, [1, 188, 1024]> linear_121_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_k_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = string("linear_121_cast_fp16")];
+            tensor<int32, [4]> var_2792 = const()[name = string("op_2792"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_53_cast_fp16 = reshape(shape = var_2792, x = linear_121_cast_fp16)[name = string("k_53_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334899584))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335950336))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335948224))))[name = string("encoder_module_layers_13_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335951424)))];
+            tensor<fp16, [1, 188, 1024]> linear_122_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_v_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = string("linear_122_cast_fp16")];
+            tensor<int32, [4]> var_2797 = const()[name = string("op_2797"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_27_cast_fp16 = reshape(shape = var_2797, x = linear_122_cast_fp16)[name = string("v_27_cast_fp16")];
+            tensor<int32, [4]> value_31_perm_0 = const()[name = string("value_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_13_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335953536)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2809_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_module_layers_13_self_attn_pos_bias_u_to_fp16)[name = string("op_2809_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_13_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335955648)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2811_cast_fp16 = add(x = q_79_cast_fp16, y = encoder_module_layers_13_self_attn_pos_bias_v_to_fp16)[name = string("op_2811_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_27_perm_0 = const()[name = string("q_with_bias_v_27_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_313_transpose_x_0 = const()[name = string("x_313_transpose_x_0"), val = bool(false)];
+            bool x_313_transpose_y_0 = const()[name = string("x_313_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2813_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335957760))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336342656))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336341824))))[name = string("op_2813_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_27_cast_fp16 = transpose(perm = q_with_bias_v_27_perm_0, x = var_2811_cast_fp16)[name = string("transpose_221")];
+            tensor<fp16, [1, 8, 188, 375]> x_313_cast_fp16 = matmul(transpose_x = x_313_transpose_x_0, transpose_y = x_313_transpose_y_0, x = q_with_bias_v_27_cast_fp16, y = op_2813_to_fp16_quantized)[name = string("x_313_cast_fp16")];
+            tensor<int32, [8]> x_315_pad_0 = const()[name = string("x_315_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_315_mode_0 = const()[name = string("x_315_mode_0"), val = string("constant")];
+            fp16 const_218_to_fp16 = const()[name = string("const_218_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_315_cast_fp16 = pad(constant_val = const_218_to_fp16, mode = x_315_mode_0, pad = x_315_pad_0, x = x_313_cast_fp16)[name = string("x_315_cast_fp16")];
+            tensor<int32, [4]> var_2821 = const()[name = string("op_2821"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_317_cast_fp16 = reshape(shape = var_2821, x = x_315_cast_fp16)[name = string("x_317_cast_fp16")];
+            tensor<int32, [4]> var_2825_begin_0 = const()[name = string("op_2825_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2825_end_0 = const()[name = string("op_2825_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2825_end_mask_0 = const()[name = string("op_2825_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2825_cast_fp16 = slice_by_index(begin = var_2825_begin_0, end = var_2825_end_0, end_mask = var_2825_end_mask_0, x = x_317_cast_fp16)[name = string("op_2825_cast_fp16")];
+            tensor<int32, [4]> var_2826 = const()[name = string("op_2826"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_53_cast_fp16 = reshape(shape = var_2826, x = var_2825_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<int32, [4]> transpose_122_perm_0 = const()[name = string("transpose_122_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_123_perm_0 = const()[name = string("transpose_123_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_123 = transpose(perm = transpose_123_perm_0, x = k_53_cast_fp16)[name = string("transpose_219")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_122 = transpose(perm = transpose_122_perm_0, x = var_2809_cast_fp16)[name = string("transpose_220")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_55_begin_0 = const()[name = string("matrix_bd_55_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_55_end_0 = const()[name = string("matrix_bd_55_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_55_end_mask_0 = const()[name = string("matrix_bd_55_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_2835_cast_fp16 = add(x = matrix_ac_27_cast_fp16, y = matrix_bd_55_cast_fp16)[name = string("op_2835_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<fp16, [1, 8, 188, 188]> _inversed_scores_53_cast_fp16 = mul(x = var_2835_cast_fp16, y = _inversed_scores_53_y_0_to_fp16)[name = string("_inversed_scores_53_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_55_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_53_cast_fp16, cond = mask_15)[name = string("scores_55_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_2841_cast_fp16 = softmax(axis = var_152, x = scores_55_cast_fp16)[name = string("op_2841_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_717_cast_fp16 = select(a = var_164_to_fp16, b = var_2841_cast_fp16, cond = mask_15)[name = string("input_717_cast_fp16")];
+            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<fp16, [1, 8, 188, 128]> value_31_cast_fp16 = transpose(perm = value_31_perm_0, x = v_27_cast_fp16)[name = string("transpose_218")];
+            tensor<fp16, [1, 8, 188, 128]> x_319_cast_fp16 = matmul(transpose_x = x_319_transpose_x_0, transpose_y = x_319_transpose_y_0, x = input_717_cast_fp16, y = value_31_cast_fp16)[name = string("x_319_cast_fp16")];
+            tensor<int32, [4]> var_2845_perm_0 = const()[name = string("op_2845_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_2846 = const()[name = string("op_2846"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_2845_cast_fp16 = transpose(perm = var_2845_perm_0, x = x_319_cast_fp16)[name = string("transpose_217")];
+            tensor<fp16, [1, 188, 1024]> input_719_cast_fp16 = reshape(shape = var_2846, x = var_2845_cast_fp16)[name = string("input_719_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_13_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(336343104))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337393856))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337391744))))[name = string("encoder_module_layers_13_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_13_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337394944)))];
+            tensor<fp16, [1, 188, 1024]> linear_124_cast_fp16 = linear(bias = encoder_module_layers_13_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_13_self_attn_linear_out_weight_to_fp16_quantized, x = input_719_cast_fp16)[name = string("linear_124_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_723_cast_fp16 = add(x = input_715_cast_fp16, y = linear_124_cast_fp16)[name = string("input_723_cast_fp16")];
+            tensor<int32, [1]> x_323_axes_0 = const()[name = string("x_323_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337397056)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337399168)))];
+            tensor<fp16, [1, 188, 1024]> x_323_cast_fp16 = layer_norm(axes = x_323_axes_0, beta = encoder_module_layers_13_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_conv_weight_to_fp16, x = input_723_cast_fp16)[name = string("x_323_cast_fp16")];
+            tensor<int32, [3]> input_725_perm_0 = const()[name = string("input_725_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_727_pad_type_0 = const()[name = string("input_727_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_727_strides_0 = const()[name = string("input_727_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_727_pad_0 = const()[name = string("input_727_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_727_dilations_0 = const()[name = string("input_727_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_727_groups_0 = const()[name = string("input_727_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337401280))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339502656))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339498496))))[name = string("encoder_module_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339504768)))];
+            tensor<fp16, [1, 1024, 188]> input_725_cast_fp16 = transpose(perm = input_725_perm_0, x = x_323_cast_fp16)[name = string("transpose_216")];
+            tensor<fp16, [1, 2048, 188]> input_727_cast_fp16 = conv(bias = encoder_module_layers_13_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_13_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_725_cast_fp16)[name = string("input_727_cast_fp16")];
+            int32 x_325_split_num_splits_0 = const()[name = string("x_325_split_num_splits_0"), val = int32(2)];
+            int32 x_325_split_axis_0 = const()[name = string("x_325_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_325_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_325_split_cast_fp16_1 = split(axis = x_325_split_axis_0, num_splits = x_325_split_num_splits_0, x = input_727_cast_fp16)[name = string("x_325_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_325_split_1_sigmoid_cast_fp16 = sigmoid(x = x_325_split_cast_fp16_1)[name = string("x_325_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_325_cast_fp16 = mul(x = x_325_split_cast_fp16_0, y = x_325_split_1_sigmoid_cast_fp16)[name = string("x_325_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_729_cast_fp16 = select(a = var_164_to_fp16, b = x_325_cast_fp16, cond = var_608)[name = string("input_729_cast_fp16")];
+            tensor<int32, [6]> input_731_pad_0 = const()[name = string("input_731_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_731_mode_0 = const()[name = string("input_731_mode_0"), val = string("constant")];
+            fp16 const_221_to_fp16 = const()[name = string("const_221_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_731_cast_fp16 = pad(constant_val = const_221_to_fp16, mode = input_731_mode_0, pad = input_731_pad_0, x = input_729_cast_fp16)[name = string("input_731_cast_fp16")];
+            string input_733_pad_type_0 = const()[name = string("input_733_pad_type_0"), val = string("valid")];
+            int32 input_733_groups_0 = const()[name = string("input_733_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_733_strides_0 = const()[name = string("input_733_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_733_pad_0 = const()[name = string("input_733_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_733_dilations_0 = const()[name = string("input_733_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_348_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339508928))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339520320))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339518208))))[name = string("const_348_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_349_to_fp16 = const()[name = string("const_349_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339521408)))];
+            tensor<fp16, [1, 1024, 188]> input_735_cast_fp16 = conv(bias = const_349_to_fp16, dilations = input_733_dilations_0, groups = input_733_groups_0, pad = input_733_pad_0, pad_type = input_733_pad_type_0, strides = input_733_strides_0, weight = const_348_to_fp16_quantized, x = input_731_cast_fp16)[name = string("input_735_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_737_cast_fp16 = silu(x = input_735_cast_fp16)[name = string("input_737_cast_fp16")];
+            string x_327_pad_type_0 = const()[name = string("x_327_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_327_strides_0 = const()[name = string("x_327_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_327_pad_0 = const()[name = string("x_327_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_327_dilations_0 = const()[name = string("x_327_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_327_groups_0 = const()[name = string("x_327_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(339523520))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340574272))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340572160))))[name = string("encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340575360)))];
+            tensor<fp16, [1, 1024, 188]> x_327_cast_fp16 = conv(bias = encoder_module_layers_13_conv_pointwise_conv2_bias_to_fp16, dilations = x_327_dilations_0, groups = x_327_groups_0, pad = x_327_pad_0, pad_type = x_327_pad_type_0, strides = x_327_strides_0, weight = encoder_module_layers_13_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_737_cast_fp16)[name = string("x_327_cast_fp16")];
+            tensor<int32, [3]> input_739_perm_0 = const()[name = string("input_739_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_739_cast_fp16 = transpose(perm = input_739_perm_0, x = x_327_cast_fp16)[name = string("transpose_215")];
+            tensor<fp16, [1, 188, 1024]> input_741_cast_fp16 = add(x = input_723_cast_fp16, y = input_739_cast_fp16)[name = string("input_741_cast_fp16")];
+            tensor<int32, [1]> input_743_axes_0 = const()[name = string("input_743_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340577472)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340579584)))];
+            tensor<fp16, [1, 188, 1024]> input_743_cast_fp16 = layer_norm(axes = input_743_axes_0, beta = encoder_module_layers_13_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_feed_forward2_weight_to_fp16, x = input_741_cast_fp16)[name = string("input_743_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(340581696))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344784320))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344776064))))[name = string("encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344788480)))];
+            tensor<fp16, [1, 188, 4096]> linear_125_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_13_feed_forward2_linear1_weight_to_fp16_quantized, x = input_743_cast_fp16)[name = string("linear_125_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_747_cast_fp16 = silu(x = linear_125_cast_fp16)[name = string("input_747_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344796736))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348993216))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348991104))))[name = string("encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348994304)))];
+            tensor<fp16, [1, 188, 1024]> linear_126_cast_fp16 = linear(bias = encoder_module_layers_13_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_13_feed_forward2_linear2_weight_to_fp16_quantized, x = input_747_cast_fp16)[name = string("linear_126_cast_fp16")];
+            fp16 var_2912_to_fp16 = const()[name = string("op_2912_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2913_cast_fp16 = mul(x = linear_126_cast_fp16, y = var_2912_to_fp16)[name = string("op_2913_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_753_cast_fp16 = add(x = input_741_cast_fp16, y = var_2913_cast_fp16)[name = string("input_753_cast_fp16")];
+            tensor<int32, [1]> input_755_axes_0 = const()[name = string("input_755_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_13_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348996416)))];
+            tensor<fp16, [1024]> encoder_module_layers_13_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_13_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(348998528)))];
+            tensor<fp16, [1, 188, 1024]> input_755_cast_fp16 = layer_norm(axes = input_755_axes_0, beta = encoder_module_layers_13_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_13_norm_out_weight_to_fp16, x = input_753_cast_fp16)[name = string("input_755_cast_fp16")];
+            tensor<int32, [1]> input_757_axes_0 = const()[name = string("input_757_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349000640)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349002752)))];
+            tensor<fp16, [1, 188, 1024]> input_757_cast_fp16 = layer_norm(axes = input_757_axes_0, beta = encoder_module_layers_14_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_feed_forward1_weight_to_fp16, x = input_755_cast_fp16)[name = string("input_757_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(349004864))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353207488))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353199232))))[name = string("encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353211648)))];
+            tensor<fp16, [1, 188, 4096]> linear_127_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_14_feed_forward1_linear1_weight_to_fp16_quantized, x = input_757_cast_fp16)[name = string("linear_127_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_761_cast_fp16 = silu(x = linear_127_cast_fp16)[name = string("input_761_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(353219904))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357416384))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357414272))))[name = string("encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357417472)))];
+            tensor<fp16, [1, 188, 1024]> linear_128_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_14_feed_forward1_linear2_weight_to_fp16_quantized, x = input_761_cast_fp16)[name = string("linear_128_cast_fp16")];
+            fp16 var_2943_to_fp16 = const()[name = string("op_2943_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_2944_cast_fp16 = mul(x = linear_128_cast_fp16, y = var_2943_to_fp16)[name = string("op_2944_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_767_cast_fp16 = add(x = input_755_cast_fp16, y = var_2944_cast_fp16)[name = string("input_767_cast_fp16")];
+            tensor<int32, [1]> query_29_axes_0 = const()[name = string("query_29_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357419584)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357421696)))];
+            tensor<fp16, [1, 188, 1024]> query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = encoder_module_layers_14_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_self_att_weight_to_fp16, x = input_767_cast_fp16)[name = string("query_29_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357423808))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358474560))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358472448))))[name = string("encoder_module_layers_14_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358475648)))];
+            tensor<fp16, [1, 188, 1024]> linear_129_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_q_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = string("linear_129_cast_fp16")];
+            tensor<int32, [4]> var_2961 = const()[name = string("op_2961"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_85_cast_fp16 = reshape(shape = var_2961, x = linear_129_cast_fp16)[name = string("q_85_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358477760))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(359528512))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(359526400))))[name = string("encoder_module_layers_14_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(359529600)))];
+            tensor<fp16, [1, 188, 1024]> linear_130_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_k_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = string("linear_130_cast_fp16")];
+            tensor<int32, [4]> var_2966 = const()[name = string("op_2966"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_57_cast_fp16 = reshape(shape = var_2966, x = linear_130_cast_fp16)[name = string("k_57_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(359531712))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360582464))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360580352))))[name = string("encoder_module_layers_14_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360583552)))];
+            tensor<fp16, [1, 188, 1024]> linear_131_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_v_weight_to_fp16_quantized, x = query_29_cast_fp16)[name = string("linear_131_cast_fp16")];
+            tensor<int32, [4]> var_2971 = const()[name = string("op_2971"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_29_cast_fp16 = reshape(shape = var_2971, x = linear_131_cast_fp16)[name = string("v_29_cast_fp16")];
+            tensor<int32, [4]> value_33_perm_0 = const()[name = string("value_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_14_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360585664)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2983_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_module_layers_14_self_attn_pos_bias_u_to_fp16)[name = string("op_2983_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_14_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360587776)))];
+            tensor<fp16, [1, 188, 8, 128]> var_2985_cast_fp16 = add(x = q_85_cast_fp16, y = encoder_module_layers_14_self_attn_pos_bias_v_to_fp16)[name = string("op_2985_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_29_perm_0 = const()[name = string("q_with_bias_v_29_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_335_transpose_x_0 = const()[name = string("x_335_transpose_x_0"), val = bool(false)];
+            bool x_335_transpose_y_0 = const()[name = string("x_335_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_2987_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360589888))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360974784))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360973952))))[name = string("op_2987_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_29_cast_fp16 = transpose(perm = q_with_bias_v_29_perm_0, x = var_2985_cast_fp16)[name = string("transpose_214")];
+            tensor<fp16, [1, 8, 188, 375]> x_335_cast_fp16 = matmul(transpose_x = x_335_transpose_x_0, transpose_y = x_335_transpose_y_0, x = q_with_bias_v_29_cast_fp16, y = op_2987_to_fp16_quantized)[name = string("x_335_cast_fp16")];
+            tensor<int32, [8]> x_337_pad_0 = const()[name = string("x_337_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_337_mode_0 = const()[name = string("x_337_mode_0"), val = string("constant")];
+            fp16 const_228_to_fp16 = const()[name = string("const_228_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_337_cast_fp16 = pad(constant_val = const_228_to_fp16, mode = x_337_mode_0, pad = x_337_pad_0, x = x_335_cast_fp16)[name = string("x_337_cast_fp16")];
+            tensor<int32, [4]> var_2995 = const()[name = string("op_2995"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_339_cast_fp16 = reshape(shape = var_2995, x = x_337_cast_fp16)[name = string("x_339_cast_fp16")];
+            tensor<int32, [4]> var_2999_begin_0 = const()[name = string("op_2999_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_2999_end_0 = const()[name = string("op_2999_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_2999_end_mask_0 = const()[name = string("op_2999_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_2999_cast_fp16 = slice_by_index(begin = var_2999_begin_0, end = var_2999_end_0, end_mask = var_2999_end_mask_0, x = x_339_cast_fp16)[name = string("op_2999_cast_fp16")];
+            tensor<int32, [4]> var_3000 = const()[name = string("op_3000"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_57_cast_fp16 = reshape(shape = var_3000, x = var_2999_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<int32, [4]> transpose_124_perm_0 = const()[name = string("transpose_124_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_125_perm_0 = const()[name = string("transpose_125_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_125 = transpose(perm = transpose_125_perm_0, x = k_57_cast_fp16)[name = string("transpose_212")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_124 = transpose(perm = transpose_124_perm_0, x = var_2983_cast_fp16)[name = string("transpose_213")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_59_begin_0 = const()[name = string("matrix_bd_59_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_59_end_0 = const()[name = string("matrix_bd_59_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_59_end_mask_0 = const()[name = string("matrix_bd_59_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3009_cast_fp16 = add(x = matrix_ac_29_cast_fp16, y = matrix_bd_59_cast_fp16)[name = string("op_3009_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<fp16, [1, 8, 188, 188]> _inversed_scores_57_cast_fp16 = mul(x = var_3009_cast_fp16, y = _inversed_scores_57_y_0_to_fp16)[name = string("_inversed_scores_57_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_59_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_57_cast_fp16, cond = mask_15)[name = string("scores_59_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3015_cast_fp16 = softmax(axis = var_152, x = scores_59_cast_fp16)[name = string("op_3015_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_769_cast_fp16 = select(a = var_164_to_fp16, b = var_3015_cast_fp16, cond = mask_15)[name = string("input_769_cast_fp16")];
+            bool x_341_transpose_x_0 = const()[name = string("x_341_transpose_x_0"), val = bool(false)];
+            bool x_341_transpose_y_0 = const()[name = string("x_341_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_33_cast_fp16 = transpose(perm = value_33_perm_0, x = v_29_cast_fp16)[name = string("transpose_211")];
+            tensor<fp16, [1, 8, 188, 128]> x_341_cast_fp16 = matmul(transpose_x = x_341_transpose_x_0, transpose_y = x_341_transpose_y_0, x = input_769_cast_fp16, y = value_33_cast_fp16)[name = string("x_341_cast_fp16")];
+            tensor<int32, [4]> var_3019_perm_0 = const()[name = string("op_3019_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3020 = const()[name = string("op_3020"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3019_cast_fp16 = transpose(perm = var_3019_perm_0, x = x_341_cast_fp16)[name = string("transpose_210")];
+            tensor<fp16, [1, 188, 1024]> input_771_cast_fp16 = reshape(shape = var_3020, x = var_3019_cast_fp16)[name = string("input_771_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_14_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360975232))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362025984))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362023872))))[name = string("encoder_module_layers_14_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_14_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362027072)))];
+            tensor<fp16, [1, 188, 1024]> linear_133_cast_fp16 = linear(bias = encoder_module_layers_14_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_14_self_attn_linear_out_weight_to_fp16_quantized, x = input_771_cast_fp16)[name = string("linear_133_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_775_cast_fp16 = add(x = input_767_cast_fp16, y = linear_133_cast_fp16)[name = string("input_775_cast_fp16")];
+            tensor<int32, [1]> x_345_axes_0 = const()[name = string("x_345_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362029184)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362031296)))];
+            tensor<fp16, [1, 188, 1024]> x_345_cast_fp16 = layer_norm(axes = x_345_axes_0, beta = encoder_module_layers_14_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_conv_weight_to_fp16, x = input_775_cast_fp16)[name = string("x_345_cast_fp16")];
+            tensor<int32, [3]> input_777_perm_0 = const()[name = string("input_777_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_779_pad_type_0 = const()[name = string("input_779_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_779_strides_0 = const()[name = string("input_779_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_779_pad_0 = const()[name = string("input_779_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_779_dilations_0 = const()[name = string("input_779_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_779_groups_0 = const()[name = string("input_779_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(362033408))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364134784))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364130624))))[name = string("encoder_module_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364136896)))];
+            tensor<fp16, [1, 1024, 188]> input_777_cast_fp16 = transpose(perm = input_777_perm_0, x = x_345_cast_fp16)[name = string("transpose_209")];
+            tensor<fp16, [1, 2048, 188]> input_779_cast_fp16 = conv(bias = encoder_module_layers_14_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_14_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_777_cast_fp16)[name = string("input_779_cast_fp16")];
+            int32 x_347_split_num_splits_0 = const()[name = string("x_347_split_num_splits_0"), val = int32(2)];
+            int32 x_347_split_axis_0 = const()[name = string("x_347_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_347_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_347_split_cast_fp16_1 = split(axis = x_347_split_axis_0, num_splits = x_347_split_num_splits_0, x = input_779_cast_fp16)[name = string("x_347_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_347_split_1_sigmoid_cast_fp16 = sigmoid(x = x_347_split_cast_fp16_1)[name = string("x_347_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_347_cast_fp16 = mul(x = x_347_split_cast_fp16_0, y = x_347_split_1_sigmoid_cast_fp16)[name = string("x_347_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_781_cast_fp16 = select(a = var_164_to_fp16, b = x_347_cast_fp16, cond = var_608)[name = string("input_781_cast_fp16")];
+            tensor<int32, [6]> input_783_pad_0 = const()[name = string("input_783_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_783_mode_0 = const()[name = string("input_783_mode_0"), val = string("constant")];
+            fp16 const_231_to_fp16 = const()[name = string("const_231_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_783_cast_fp16 = pad(constant_val = const_231_to_fp16, mode = input_783_mode_0, pad = input_783_pad_0, x = input_781_cast_fp16)[name = string("input_783_cast_fp16")];
+            string input_785_pad_type_0 = const()[name = string("input_785_pad_type_0"), val = string("valid")];
+            int32 input_785_groups_0 = const()[name = string("input_785_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_785_strides_0 = const()[name = string("input_785_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_785_pad_0 = const()[name = string("input_785_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_785_dilations_0 = const()[name = string("input_785_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_350_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364141056))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364152448))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364150336))))[name = string("const_350_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_351_to_fp16 = const()[name = string("const_351_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364153536)))];
+            tensor<fp16, [1, 1024, 188]> input_787_cast_fp16 = conv(bias = const_351_to_fp16, dilations = input_785_dilations_0, groups = input_785_groups_0, pad = input_785_pad_0, pad_type = input_785_pad_type_0, strides = input_785_strides_0, weight = const_350_to_fp16_quantized, x = input_783_cast_fp16)[name = string("input_787_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_789_cast_fp16 = silu(x = input_787_cast_fp16)[name = string("input_789_cast_fp16")];
+            string x_349_pad_type_0 = const()[name = string("x_349_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_349_strides_0 = const()[name = string("x_349_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_349_pad_0 = const()[name = string("x_349_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_349_dilations_0 = const()[name = string("x_349_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_349_groups_0 = const()[name = string("x_349_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(364155648))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365206400))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365204288))))[name = string("encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365207488)))];
+            tensor<fp16, [1, 1024, 188]> x_349_cast_fp16 = conv(bias = encoder_module_layers_14_conv_pointwise_conv2_bias_to_fp16, dilations = x_349_dilations_0, groups = x_349_groups_0, pad = x_349_pad_0, pad_type = x_349_pad_type_0, strides = x_349_strides_0, weight = encoder_module_layers_14_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_789_cast_fp16)[name = string("x_349_cast_fp16")];
+            tensor<int32, [3]> input_791_perm_0 = const()[name = string("input_791_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_791_cast_fp16 = transpose(perm = input_791_perm_0, x = x_349_cast_fp16)[name = string("transpose_208")];
+            tensor<fp16, [1, 188, 1024]> input_793_cast_fp16 = add(x = input_775_cast_fp16, y = input_791_cast_fp16)[name = string("input_793_cast_fp16")];
+            tensor<int32, [1]> input_795_axes_0 = const()[name = string("input_795_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365209600)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365211712)))];
+            tensor<fp16, [1, 188, 1024]> input_795_cast_fp16 = layer_norm(axes = input_795_axes_0, beta = encoder_module_layers_14_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_feed_forward2_weight_to_fp16, x = input_793_cast_fp16)[name = string("input_795_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365213824))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369416448))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369408192))))[name = string("encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369420608)))];
+            tensor<fp16, [1, 188, 4096]> linear_134_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_14_feed_forward2_linear1_weight_to_fp16_quantized, x = input_795_cast_fp16)[name = string("linear_134_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_799_cast_fp16 = silu(x = linear_134_cast_fp16)[name = string("input_799_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(369428864))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373625344))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373623232))))[name = string("encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373626432)))];
+            tensor<fp16, [1, 188, 1024]> linear_135_cast_fp16 = linear(bias = encoder_module_layers_14_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_14_feed_forward2_linear2_weight_to_fp16_quantized, x = input_799_cast_fp16)[name = string("linear_135_cast_fp16")];
+            fp16 var_3086_to_fp16 = const()[name = string("op_3086_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3087_cast_fp16 = mul(x = linear_135_cast_fp16, y = var_3086_to_fp16)[name = string("op_3087_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_805_cast_fp16 = add(x = input_793_cast_fp16, y = var_3087_cast_fp16)[name = string("input_805_cast_fp16")];
+            tensor<int32, [1]> input_807_axes_0 = const()[name = string("input_807_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_14_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373628544)))];
+            tensor<fp16, [1024]> encoder_module_layers_14_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_14_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373630656)))];
+            tensor<fp16, [1, 188, 1024]> input_807_cast_fp16 = layer_norm(axes = input_807_axes_0, beta = encoder_module_layers_14_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_14_norm_out_weight_to_fp16, x = input_805_cast_fp16)[name = string("input_807_cast_fp16")];
+            tensor<int32, [1]> input_809_axes_0 = const()[name = string("input_809_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373632768)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373634880)))];
+            tensor<fp16, [1, 188, 1024]> input_809_cast_fp16 = layer_norm(axes = input_809_axes_0, beta = encoder_module_layers_15_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_feed_forward1_weight_to_fp16, x = input_807_cast_fp16)[name = string("input_809_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(373636992))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(377839616))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(377831360))))[name = string("encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(377843776)))];
+            tensor<fp16, [1, 188, 4096]> linear_136_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_15_feed_forward1_linear1_weight_to_fp16_quantized, x = input_809_cast_fp16)[name = string("linear_136_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_813_cast_fp16 = silu(x = linear_136_cast_fp16)[name = string("input_813_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(377852032))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382048512))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382046400))))[name = string("encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382049600)))];
+            tensor<fp16, [1, 188, 1024]> linear_137_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_15_feed_forward1_linear2_weight_to_fp16_quantized, x = input_813_cast_fp16)[name = string("linear_137_cast_fp16")];
+            fp16 var_3117_to_fp16 = const()[name = string("op_3117_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3118_cast_fp16 = mul(x = linear_137_cast_fp16, y = var_3117_to_fp16)[name = string("op_3118_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_819_cast_fp16 = add(x = input_807_cast_fp16, y = var_3118_cast_fp16)[name = string("input_819_cast_fp16")];
+            tensor<int32, [1]> query_31_axes_0 = const()[name = string("query_31_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382051712)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382053824)))];
+            tensor<fp16, [1, 188, 1024]> query_31_cast_fp16 = layer_norm(axes = query_31_axes_0, beta = encoder_module_layers_15_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_self_att_weight_to_fp16, x = input_819_cast_fp16)[name = string("query_31_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(382055936))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383106688))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383104576))))[name = string("encoder_module_layers_15_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383107776)))];
+            tensor<fp16, [1, 188, 1024]> linear_138_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_q_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = string("linear_138_cast_fp16")];
+            tensor<int32, [4]> var_3135 = const()[name = string("op_3135"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_91_cast_fp16 = reshape(shape = var_3135, x = linear_138_cast_fp16)[name = string("q_91_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(383109888))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384160640))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384158528))))[name = string("encoder_module_layers_15_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384161728)))];
+            tensor<fp16, [1, 188, 1024]> linear_139_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_k_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = string("linear_139_cast_fp16")];
+            tensor<int32, [4]> var_3140 = const()[name = string("op_3140"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_61_cast_fp16 = reshape(shape = var_3140, x = linear_139_cast_fp16)[name = string("k_61_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(384163840))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385214592))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385212480))))[name = string("encoder_module_layers_15_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385215680)))];
+            tensor<fp16, [1, 188, 1024]> linear_140_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_v_weight_to_fp16_quantized, x = query_31_cast_fp16)[name = string("linear_140_cast_fp16")];
+            tensor<int32, [4]> var_3145 = const()[name = string("op_3145"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_31_cast_fp16 = reshape(shape = var_3145, x = linear_140_cast_fp16)[name = string("v_31_cast_fp16")];
+            tensor<int32, [4]> value_35_perm_0 = const()[name = string("value_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_15_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385217792)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3157_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_module_layers_15_self_attn_pos_bias_u_to_fp16)[name = string("op_3157_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_15_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385219904)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3159_cast_fp16 = add(x = q_91_cast_fp16, y = encoder_module_layers_15_self_attn_pos_bias_v_to_fp16)[name = string("op_3159_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_31_perm_0 = const()[name = string("q_with_bias_v_31_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_357_transpose_x_0 = const()[name = string("x_357_transpose_x_0"), val = bool(false)];
+            bool x_357_transpose_y_0 = const()[name = string("x_357_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3161_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385222016))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385606912))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385606080))))[name = string("op_3161_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_31_cast_fp16 = transpose(perm = q_with_bias_v_31_perm_0, x = var_3159_cast_fp16)[name = string("transpose_207")];
+            tensor<fp16, [1, 8, 188, 375]> x_357_cast_fp16 = matmul(transpose_x = x_357_transpose_x_0, transpose_y = x_357_transpose_y_0, x = q_with_bias_v_31_cast_fp16, y = op_3161_to_fp16_quantized)[name = string("x_357_cast_fp16")];
+            tensor<int32, [8]> x_359_pad_0 = const()[name = string("x_359_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_359_mode_0 = const()[name = string("x_359_mode_0"), val = string("constant")];
+            fp16 const_238_to_fp16 = const()[name = string("const_238_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_359_cast_fp16 = pad(constant_val = const_238_to_fp16, mode = x_359_mode_0, pad = x_359_pad_0, x = x_357_cast_fp16)[name = string("x_359_cast_fp16")];
+            tensor<int32, [4]> var_3169 = const()[name = string("op_3169"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_361_cast_fp16 = reshape(shape = var_3169, x = x_359_cast_fp16)[name = string("x_361_cast_fp16")];
+            tensor<int32, [4]> var_3173_begin_0 = const()[name = string("op_3173_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3173_end_0 = const()[name = string("op_3173_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3173_end_mask_0 = const()[name = string("op_3173_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3173_cast_fp16 = slice_by_index(begin = var_3173_begin_0, end = var_3173_end_0, end_mask = var_3173_end_mask_0, x = x_361_cast_fp16)[name = string("op_3173_cast_fp16")];
+            tensor<int32, [4]> var_3174 = const()[name = string("op_3174"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_61_cast_fp16 = reshape(shape = var_3174, x = var_3173_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<int32, [4]> transpose_126_perm_0 = const()[name = string("transpose_126_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_127_perm_0 = const()[name = string("transpose_127_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_127 = transpose(perm = transpose_127_perm_0, x = k_61_cast_fp16)[name = string("transpose_205")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_126 = transpose(perm = transpose_126_perm_0, x = var_3157_cast_fp16)[name = string("transpose_206")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_63_begin_0 = const()[name = string("matrix_bd_63_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_63_end_0 = const()[name = string("matrix_bd_63_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_63_end_mask_0 = const()[name = string("matrix_bd_63_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3183_cast_fp16 = add(x = matrix_ac_31_cast_fp16, y = matrix_bd_63_cast_fp16)[name = string("op_3183_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<fp16, [1, 8, 188, 188]> _inversed_scores_61_cast_fp16 = mul(x = var_3183_cast_fp16, y = _inversed_scores_61_y_0_to_fp16)[name = string("_inversed_scores_61_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_63_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_61_cast_fp16, cond = mask_15)[name = string("scores_63_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3189_cast_fp16 = softmax(axis = var_152, x = scores_63_cast_fp16)[name = string("op_3189_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_821_cast_fp16 = select(a = var_164_to_fp16, b = var_3189_cast_fp16, cond = mask_15)[name = string("input_821_cast_fp16")];
+            bool x_363_transpose_x_0 = const()[name = string("x_363_transpose_x_0"), val = bool(false)];
+            bool x_363_transpose_y_0 = const()[name = string("x_363_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_35_cast_fp16 = transpose(perm = value_35_perm_0, x = v_31_cast_fp16)[name = string("transpose_204")];
+            tensor<fp16, [1, 8, 188, 128]> x_363_cast_fp16 = matmul(transpose_x = x_363_transpose_x_0, transpose_y = x_363_transpose_y_0, x = input_821_cast_fp16, y = value_35_cast_fp16)[name = string("x_363_cast_fp16")];
+            tensor<int32, [4]> var_3193_perm_0 = const()[name = string("op_3193_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3194 = const()[name = string("op_3194"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3193_cast_fp16 = transpose(perm = var_3193_perm_0, x = x_363_cast_fp16)[name = string("transpose_203")];
+            tensor<fp16, [1, 188, 1024]> input_823_cast_fp16 = reshape(shape = var_3194, x = var_3193_cast_fp16)[name = string("input_823_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_15_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385607360))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386658112))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386656000))))[name = string("encoder_module_layers_15_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_15_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386659200)))];
+            tensor<fp16, [1, 188, 1024]> linear_142_cast_fp16 = linear(bias = encoder_module_layers_15_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_15_self_attn_linear_out_weight_to_fp16_quantized, x = input_823_cast_fp16)[name = string("linear_142_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_827_cast_fp16 = add(x = input_819_cast_fp16, y = linear_142_cast_fp16)[name = string("input_827_cast_fp16")];
+            tensor<int32, [1]> x_367_axes_0 = const()[name = string("x_367_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386661312)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386663424)))];
+            tensor<fp16, [1, 188, 1024]> x_367_cast_fp16 = layer_norm(axes = x_367_axes_0, beta = encoder_module_layers_15_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_conv_weight_to_fp16, x = input_827_cast_fp16)[name = string("x_367_cast_fp16")];
+            tensor<int32, [3]> input_829_perm_0 = const()[name = string("input_829_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_831_pad_type_0 = const()[name = string("input_831_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_831_strides_0 = const()[name = string("input_831_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_831_pad_0 = const()[name = string("input_831_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_831_dilations_0 = const()[name = string("input_831_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_831_groups_0 = const()[name = string("input_831_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(386665536))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388766912))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388762752))))[name = string("encoder_module_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388769024)))];
+            tensor<fp16, [1, 1024, 188]> input_829_cast_fp16 = transpose(perm = input_829_perm_0, x = x_367_cast_fp16)[name = string("transpose_202")];
+            tensor<fp16, [1, 2048, 188]> input_831_cast_fp16 = conv(bias = encoder_module_layers_15_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_15_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_829_cast_fp16)[name = string("input_831_cast_fp16")];
+            int32 x_369_split_num_splits_0 = const()[name = string("x_369_split_num_splits_0"), val = int32(2)];
+            int32 x_369_split_axis_0 = const()[name = string("x_369_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_369_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_369_split_cast_fp16_1 = split(axis = x_369_split_axis_0, num_splits = x_369_split_num_splits_0, x = input_831_cast_fp16)[name = string("x_369_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_369_split_1_sigmoid_cast_fp16 = sigmoid(x = x_369_split_cast_fp16_1)[name = string("x_369_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_369_cast_fp16 = mul(x = x_369_split_cast_fp16_0, y = x_369_split_1_sigmoid_cast_fp16)[name = string("x_369_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_833_cast_fp16 = select(a = var_164_to_fp16, b = x_369_cast_fp16, cond = var_608)[name = string("input_833_cast_fp16")];
+            tensor<int32, [6]> input_835_pad_0 = const()[name = string("input_835_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_835_mode_0 = const()[name = string("input_835_mode_0"), val = string("constant")];
+            fp16 const_241_to_fp16 = const()[name = string("const_241_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_835_cast_fp16 = pad(constant_val = const_241_to_fp16, mode = input_835_mode_0, pad = input_835_pad_0, x = input_833_cast_fp16)[name = string("input_835_cast_fp16")];
+            string input_837_pad_type_0 = const()[name = string("input_837_pad_type_0"), val = string("valid")];
+            int32 input_837_groups_0 = const()[name = string("input_837_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_837_strides_0 = const()[name = string("input_837_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_837_pad_0 = const()[name = string("input_837_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_837_dilations_0 = const()[name = string("input_837_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_352_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388773184))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388784576))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388782464))))[name = string("const_352_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_353_to_fp16 = const()[name = string("const_353_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388785664)))];
+            tensor<fp16, [1, 1024, 188]> input_839_cast_fp16 = conv(bias = const_353_to_fp16, dilations = input_837_dilations_0, groups = input_837_groups_0, pad = input_837_pad_0, pad_type = input_837_pad_type_0, strides = input_837_strides_0, weight = const_352_to_fp16_quantized, x = input_835_cast_fp16)[name = string("input_839_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_841_cast_fp16 = silu(x = input_839_cast_fp16)[name = string("input_841_cast_fp16")];
+            string x_371_pad_type_0 = const()[name = string("x_371_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_371_strides_0 = const()[name = string("x_371_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_371_pad_0 = const()[name = string("x_371_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_371_dilations_0 = const()[name = string("x_371_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_371_groups_0 = const()[name = string("x_371_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388787776))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389838528))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389836416))))[name = string("encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389839616)))];
+            tensor<fp16, [1, 1024, 188]> x_371_cast_fp16 = conv(bias = encoder_module_layers_15_conv_pointwise_conv2_bias_to_fp16, dilations = x_371_dilations_0, groups = x_371_groups_0, pad = x_371_pad_0, pad_type = x_371_pad_type_0, strides = x_371_strides_0, weight = encoder_module_layers_15_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_841_cast_fp16)[name = string("x_371_cast_fp16")];
+            tensor<int32, [3]> input_843_perm_0 = const()[name = string("input_843_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_843_cast_fp16 = transpose(perm = input_843_perm_0, x = x_371_cast_fp16)[name = string("transpose_201")];
+            tensor<fp16, [1, 188, 1024]> input_845_cast_fp16 = add(x = input_827_cast_fp16, y = input_843_cast_fp16)[name = string("input_845_cast_fp16")];
+            tensor<int32, [1]> input_847_axes_0 = const()[name = string("input_847_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389841728)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389843840)))];
+            tensor<fp16, [1, 188, 1024]> input_847_cast_fp16 = layer_norm(axes = input_847_axes_0, beta = encoder_module_layers_15_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_feed_forward2_weight_to_fp16, x = input_845_cast_fp16)[name = string("input_847_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389845952))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394048576))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394040320))))[name = string("encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394052736)))];
+            tensor<fp16, [1, 188, 4096]> linear_143_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_15_feed_forward2_linear1_weight_to_fp16_quantized, x = input_847_cast_fp16)[name = string("linear_143_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_851_cast_fp16 = silu(x = linear_143_cast_fp16)[name = string("input_851_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394060992))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398257472))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398255360))))[name = string("encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398258560)))];
+            tensor<fp16, [1, 188, 1024]> linear_144_cast_fp16 = linear(bias = encoder_module_layers_15_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_15_feed_forward2_linear2_weight_to_fp16_quantized, x = input_851_cast_fp16)[name = string("linear_144_cast_fp16")];
+            fp16 var_3260_to_fp16 = const()[name = string("op_3260_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3261_cast_fp16 = mul(x = linear_144_cast_fp16, y = var_3260_to_fp16)[name = string("op_3261_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_857_cast_fp16 = add(x = input_845_cast_fp16, y = var_3261_cast_fp16)[name = string("input_857_cast_fp16")];
+            tensor<int32, [1]> input_859_axes_0 = const()[name = string("input_859_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_15_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398260672)))];
+            tensor<fp16, [1024]> encoder_module_layers_15_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_15_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398262784)))];
+            tensor<fp16, [1, 188, 1024]> input_859_cast_fp16 = layer_norm(axes = input_859_axes_0, beta = encoder_module_layers_15_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_15_norm_out_weight_to_fp16, x = input_857_cast_fp16)[name = string("input_859_cast_fp16")];
+            tensor<int32, [1]> input_861_axes_0 = const()[name = string("input_861_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398264896)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398267008)))];
+            tensor<fp16, [1, 188, 1024]> input_861_cast_fp16 = layer_norm(axes = input_861_axes_0, beta = encoder_module_layers_16_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_feed_forward1_weight_to_fp16, x = input_859_cast_fp16)[name = string("input_861_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(398269120))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402471744))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402463488))))[name = string("encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402475904)))];
+            tensor<fp16, [1, 188, 4096]> linear_145_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_16_feed_forward1_linear1_weight_to_fp16_quantized, x = input_861_cast_fp16)[name = string("linear_145_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_865_cast_fp16 = silu(x = linear_145_cast_fp16)[name = string("input_865_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402484160))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406680640))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406678528))))[name = string("encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406681728)))];
+            tensor<fp16, [1, 188, 1024]> linear_146_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_16_feed_forward1_linear2_weight_to_fp16_quantized, x = input_865_cast_fp16)[name = string("linear_146_cast_fp16")];
+            fp16 var_3291_to_fp16 = const()[name = string("op_3291_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3292_cast_fp16 = mul(x = linear_146_cast_fp16, y = var_3291_to_fp16)[name = string("op_3292_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_871_cast_fp16 = add(x = input_859_cast_fp16, y = var_3292_cast_fp16)[name = string("input_871_cast_fp16")];
+            tensor<int32, [1]> query_33_axes_0 = const()[name = string("query_33_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406683840)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406685952)))];
+            tensor<fp16, [1, 188, 1024]> query_33_cast_fp16 = layer_norm(axes = query_33_axes_0, beta = encoder_module_layers_16_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_self_att_weight_to_fp16, x = input_871_cast_fp16)[name = string("query_33_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(406688064))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407738816))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407736704))))[name = string("encoder_module_layers_16_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407739904)))];
+            tensor<fp16, [1, 188, 1024]> linear_147_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_q_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = string("linear_147_cast_fp16")];
+            tensor<int32, [4]> var_3309 = const()[name = string("op_3309"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_97_cast_fp16 = reshape(shape = var_3309, x = linear_147_cast_fp16)[name = string("q_97_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407742016))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408792768))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408790656))))[name = string("encoder_module_layers_16_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408793856)))];
+            tensor<fp16, [1, 188, 1024]> linear_148_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_k_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = string("linear_148_cast_fp16")];
+            tensor<int32, [4]> var_3314 = const()[name = string("op_3314"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_65_cast_fp16 = reshape(shape = var_3314, x = linear_148_cast_fp16)[name = string("k_65_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(408795968))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409846720))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409844608))))[name = string("encoder_module_layers_16_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409847808)))];
+            tensor<fp16, [1, 188, 1024]> linear_149_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_v_weight_to_fp16_quantized, x = query_33_cast_fp16)[name = string("linear_149_cast_fp16")];
+            tensor<int32, [4]> var_3319 = const()[name = string("op_3319"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_33_cast_fp16 = reshape(shape = var_3319, x = linear_149_cast_fp16)[name = string("v_33_cast_fp16")];
+            tensor<int32, [4]> value_37_perm_0 = const()[name = string("value_37_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_16_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409849920)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3331_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_module_layers_16_self_attn_pos_bias_u_to_fp16)[name = string("op_3331_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_16_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409852032)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3333_cast_fp16 = add(x = q_97_cast_fp16, y = encoder_module_layers_16_self_attn_pos_bias_v_to_fp16)[name = string("op_3333_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_33_perm_0 = const()[name = string("q_with_bias_v_33_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_379_transpose_x_0 = const()[name = string("x_379_transpose_x_0"), val = bool(false)];
+            bool x_379_transpose_y_0 = const()[name = string("x_379_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3335_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(409854144))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410239040))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410238208))))[name = string("op_3335_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_33_cast_fp16 = transpose(perm = q_with_bias_v_33_perm_0, x = var_3333_cast_fp16)[name = string("transpose_200")];
+            tensor<fp16, [1, 8, 188, 375]> x_379_cast_fp16 = matmul(transpose_x = x_379_transpose_x_0, transpose_y = x_379_transpose_y_0, x = q_with_bias_v_33_cast_fp16, y = op_3335_to_fp16_quantized)[name = string("x_379_cast_fp16")];
+            tensor<int32, [8]> x_381_pad_0 = const()[name = string("x_381_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_381_mode_0 = const()[name = string("x_381_mode_0"), val = string("constant")];
+            fp16 const_248_to_fp16 = const()[name = string("const_248_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_381_cast_fp16 = pad(constant_val = const_248_to_fp16, mode = x_381_mode_0, pad = x_381_pad_0, x = x_379_cast_fp16)[name = string("x_381_cast_fp16")];
+            tensor<int32, [4]> var_3343 = const()[name = string("op_3343"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_383_cast_fp16 = reshape(shape = var_3343, x = x_381_cast_fp16)[name = string("x_383_cast_fp16")];
+            tensor<int32, [4]> var_3347_begin_0 = const()[name = string("op_3347_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3347_end_0 = const()[name = string("op_3347_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3347_end_mask_0 = const()[name = string("op_3347_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3347_cast_fp16 = slice_by_index(begin = var_3347_begin_0, end = var_3347_end_0, end_mask = var_3347_end_mask_0, x = x_383_cast_fp16)[name = string("op_3347_cast_fp16")];
+            tensor<int32, [4]> var_3348 = const()[name = string("op_3348"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_65_cast_fp16 = reshape(shape = var_3348, x = var_3347_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<int32, [4]> transpose_128_perm_0 = const()[name = string("transpose_128_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_129_perm_0 = const()[name = string("transpose_129_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_129 = transpose(perm = transpose_129_perm_0, x = k_65_cast_fp16)[name = string("transpose_198")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_128 = transpose(perm = transpose_128_perm_0, x = var_3331_cast_fp16)[name = string("transpose_199")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_67_begin_0 = const()[name = string("matrix_bd_67_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_67_end_0 = const()[name = string("matrix_bd_67_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_67_end_mask_0 = const()[name = string("matrix_bd_67_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3357_cast_fp16 = add(x = matrix_ac_33_cast_fp16, y = matrix_bd_67_cast_fp16)[name = string("op_3357_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<fp16, [1, 8, 188, 188]> _inversed_scores_65_cast_fp16 = mul(x = var_3357_cast_fp16, y = _inversed_scores_65_y_0_to_fp16)[name = string("_inversed_scores_65_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_67_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_65_cast_fp16, cond = mask_15)[name = string("scores_67_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3363_cast_fp16 = softmax(axis = var_152, x = scores_67_cast_fp16)[name = string("op_3363_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_873_cast_fp16 = select(a = var_164_to_fp16, b = var_3363_cast_fp16, cond = mask_15)[name = string("input_873_cast_fp16")];
+            bool x_385_transpose_x_0 = const()[name = string("x_385_transpose_x_0"), val = bool(false)];
+            bool x_385_transpose_y_0 = const()[name = string("x_385_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_37_cast_fp16 = transpose(perm = value_37_perm_0, x = v_33_cast_fp16)[name = string("transpose_197")];
+            tensor<fp16, [1, 8, 188, 128]> x_385_cast_fp16 = matmul(transpose_x = x_385_transpose_x_0, transpose_y = x_385_transpose_y_0, x = input_873_cast_fp16, y = value_37_cast_fp16)[name = string("x_385_cast_fp16")];
+            tensor<int32, [4]> var_3367_perm_0 = const()[name = string("op_3367_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3368 = const()[name = string("op_3368"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3367_cast_fp16 = transpose(perm = var_3367_perm_0, x = x_385_cast_fp16)[name = string("transpose_196")];
+            tensor<fp16, [1, 188, 1024]> input_875_cast_fp16 = reshape(shape = var_3368, x = var_3367_cast_fp16)[name = string("input_875_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_16_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410239488))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411290240))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411288128))))[name = string("encoder_module_layers_16_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_16_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411291328)))];
+            tensor<fp16, [1, 188, 1024]> linear_151_cast_fp16 = linear(bias = encoder_module_layers_16_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_16_self_attn_linear_out_weight_to_fp16_quantized, x = input_875_cast_fp16)[name = string("linear_151_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_879_cast_fp16 = add(x = input_871_cast_fp16, y = linear_151_cast_fp16)[name = string("input_879_cast_fp16")];
+            tensor<int32, [1]> x_389_axes_0 = const()[name = string("x_389_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411293440)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411295552)))];
+            tensor<fp16, [1, 188, 1024]> x_389_cast_fp16 = layer_norm(axes = x_389_axes_0, beta = encoder_module_layers_16_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_conv_weight_to_fp16, x = input_879_cast_fp16)[name = string("x_389_cast_fp16")];
+            tensor<int32, [3]> input_881_perm_0 = const()[name = string("input_881_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_883_pad_type_0 = const()[name = string("input_883_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_883_strides_0 = const()[name = string("input_883_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_883_pad_0 = const()[name = string("input_883_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_883_dilations_0 = const()[name = string("input_883_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_883_groups_0 = const()[name = string("input_883_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(411297664))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(413399040))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(413394880))))[name = string("encoder_module_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(413401152)))];
+            tensor<fp16, [1, 1024, 188]> input_881_cast_fp16 = transpose(perm = input_881_perm_0, x = x_389_cast_fp16)[name = string("transpose_195")];
+            tensor<fp16, [1, 2048, 188]> input_883_cast_fp16 = conv(bias = encoder_module_layers_16_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_16_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_881_cast_fp16)[name = string("input_883_cast_fp16")];
+            int32 x_391_split_num_splits_0 = const()[name = string("x_391_split_num_splits_0"), val = int32(2)];
+            int32 x_391_split_axis_0 = const()[name = string("x_391_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_391_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_391_split_cast_fp16_1 = split(axis = x_391_split_axis_0, num_splits = x_391_split_num_splits_0, x = input_883_cast_fp16)[name = string("x_391_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_391_split_1_sigmoid_cast_fp16 = sigmoid(x = x_391_split_cast_fp16_1)[name = string("x_391_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_391_cast_fp16 = mul(x = x_391_split_cast_fp16_0, y = x_391_split_1_sigmoid_cast_fp16)[name = string("x_391_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_885_cast_fp16 = select(a = var_164_to_fp16, b = x_391_cast_fp16, cond = var_608)[name = string("input_885_cast_fp16")];
+            tensor<int32, [6]> input_887_pad_0 = const()[name = string("input_887_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_887_mode_0 = const()[name = string("input_887_mode_0"), val = string("constant")];
+            fp16 const_251_to_fp16 = const()[name = string("const_251_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_887_cast_fp16 = pad(constant_val = const_251_to_fp16, mode = input_887_mode_0, pad = input_887_pad_0, x = input_885_cast_fp16)[name = string("input_887_cast_fp16")];
+            string input_889_pad_type_0 = const()[name = string("input_889_pad_type_0"), val = string("valid")];
+            int32 input_889_groups_0 = const()[name = string("input_889_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_889_strides_0 = const()[name = string("input_889_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_889_pad_0 = const()[name = string("input_889_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_889_dilations_0 = const()[name = string("input_889_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_354_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(413405312))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(413416704))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(413414592))))[name = string("const_354_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_355_to_fp16 = const()[name = string("const_355_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(413417792)))];
+            tensor<fp16, [1, 1024, 188]> input_891_cast_fp16 = conv(bias = const_355_to_fp16, dilations = input_889_dilations_0, groups = input_889_groups_0, pad = input_889_pad_0, pad_type = input_889_pad_type_0, strides = input_889_strides_0, weight = const_354_to_fp16_quantized, x = input_887_cast_fp16)[name = string("input_891_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_893_cast_fp16 = silu(x = input_891_cast_fp16)[name = string("input_893_cast_fp16")];
+            string x_393_pad_type_0 = const()[name = string("x_393_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_393_strides_0 = const()[name = string("x_393_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_393_pad_0 = const()[name = string("x_393_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_393_dilations_0 = const()[name = string("x_393_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_393_groups_0 = const()[name = string("x_393_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(413419904))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(414470656))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(414468544))))[name = string("encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(414471744)))];
+            tensor<fp16, [1, 1024, 188]> x_393_cast_fp16 = conv(bias = encoder_module_layers_16_conv_pointwise_conv2_bias_to_fp16, dilations = x_393_dilations_0, groups = x_393_groups_0, pad = x_393_pad_0, pad_type = x_393_pad_type_0, strides = x_393_strides_0, weight = encoder_module_layers_16_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_893_cast_fp16)[name = string("x_393_cast_fp16")];
+            tensor<int32, [3]> input_895_perm_0 = const()[name = string("input_895_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_895_cast_fp16 = transpose(perm = input_895_perm_0, x = x_393_cast_fp16)[name = string("transpose_194")];
+            tensor<fp16, [1, 188, 1024]> input_897_cast_fp16 = add(x = input_879_cast_fp16, y = input_895_cast_fp16)[name = string("input_897_cast_fp16")];
+            tensor<int32, [1]> input_899_axes_0 = const()[name = string("input_899_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(414473856)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(414475968)))];
+            tensor<fp16, [1, 188, 1024]> input_899_cast_fp16 = layer_norm(axes = input_899_axes_0, beta = encoder_module_layers_16_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_feed_forward2_weight_to_fp16, x = input_897_cast_fp16)[name = string("input_899_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(414478080))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418680704))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418672448))))[name = string("encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418684864)))];
+            tensor<fp16, [1, 188, 4096]> linear_152_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_16_feed_forward2_linear1_weight_to_fp16_quantized, x = input_899_cast_fp16)[name = string("linear_152_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_903_cast_fp16 = silu(x = linear_152_cast_fp16)[name = string("input_903_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(418693120))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422889600))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422887488))))[name = string("encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422890688)))];
+            tensor<fp16, [1, 188, 1024]> linear_153_cast_fp16 = linear(bias = encoder_module_layers_16_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_16_feed_forward2_linear2_weight_to_fp16_quantized, x = input_903_cast_fp16)[name = string("linear_153_cast_fp16")];
+            fp16 var_3434_to_fp16 = const()[name = string("op_3434_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3435_cast_fp16 = mul(x = linear_153_cast_fp16, y = var_3434_to_fp16)[name = string("op_3435_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_909_cast_fp16 = add(x = input_897_cast_fp16, y = var_3435_cast_fp16)[name = string("input_909_cast_fp16")];
+            tensor<int32, [1]> input_911_axes_0 = const()[name = string("input_911_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_16_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422892800)))];
+            tensor<fp16, [1024]> encoder_module_layers_16_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_16_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422894912)))];
+            tensor<fp16, [1, 188, 1024]> input_911_cast_fp16 = layer_norm(axes = input_911_axes_0, beta = encoder_module_layers_16_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_16_norm_out_weight_to_fp16, x = input_909_cast_fp16)[name = string("input_911_cast_fp16")];
+            tensor<int32, [1]> input_913_axes_0 = const()[name = string("input_913_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422897024)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422899136)))];
+            tensor<fp16, [1, 188, 1024]> input_913_cast_fp16 = layer_norm(axes = input_913_axes_0, beta = encoder_module_layers_17_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_feed_forward1_weight_to_fp16, x = input_911_cast_fp16)[name = string("input_913_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(422901248))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427103872))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427095616))))[name = string("encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427108032)))];
+            tensor<fp16, [1, 188, 4096]> linear_154_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_17_feed_forward1_linear1_weight_to_fp16_quantized, x = input_913_cast_fp16)[name = string("linear_154_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_917_cast_fp16 = silu(x = linear_154_cast_fp16)[name = string("input_917_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(427116288))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431312768))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431310656))))[name = string("encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431313856)))];
+            tensor<fp16, [1, 188, 1024]> linear_155_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_17_feed_forward1_linear2_weight_to_fp16_quantized, x = input_917_cast_fp16)[name = string("linear_155_cast_fp16")];
+            fp16 var_3465_to_fp16 = const()[name = string("op_3465_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3466_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_3465_to_fp16)[name = string("op_3466_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_923_cast_fp16 = add(x = input_911_cast_fp16, y = var_3466_cast_fp16)[name = string("input_923_cast_fp16")];
+            tensor<int32, [1]> query_35_axes_0 = const()[name = string("query_35_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431315968)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431318080)))];
+            tensor<fp16, [1, 188, 1024]> query_35_cast_fp16 = layer_norm(axes = query_35_axes_0, beta = encoder_module_layers_17_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_self_att_weight_to_fp16, x = input_923_cast_fp16)[name = string("query_35_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(431320192))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432370944))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432368832))))[name = string("encoder_module_layers_17_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432372032)))];
+            tensor<fp16, [1, 188, 1024]> linear_156_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_q_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = string("linear_156_cast_fp16")];
+            tensor<int32, [4]> var_3483 = const()[name = string("op_3483"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_103_cast_fp16 = reshape(shape = var_3483, x = linear_156_cast_fp16)[name = string("q_103_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432374144))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433424896))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433422784))))[name = string("encoder_module_layers_17_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433425984)))];
+            tensor<fp16, [1, 188, 1024]> linear_157_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_k_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = string("linear_157_cast_fp16")];
+            tensor<int32, [4]> var_3488 = const()[name = string("op_3488"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_69_cast_fp16 = reshape(shape = var_3488, x = linear_157_cast_fp16)[name = string("k_69_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(433428096))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434478848))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434476736))))[name = string("encoder_module_layers_17_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434479936)))];
+            tensor<fp16, [1, 188, 1024]> linear_158_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_v_weight_to_fp16_quantized, x = query_35_cast_fp16)[name = string("linear_158_cast_fp16")];
+            tensor<int32, [4]> var_3493 = const()[name = string("op_3493"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_35_cast_fp16 = reshape(shape = var_3493, x = linear_158_cast_fp16)[name = string("v_35_cast_fp16")];
+            tensor<int32, [4]> value_39_perm_0 = const()[name = string("value_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_17_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434482048)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3505_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_module_layers_17_self_attn_pos_bias_u_to_fp16)[name = string("op_3505_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_17_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434484160)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3507_cast_fp16 = add(x = q_103_cast_fp16, y = encoder_module_layers_17_self_attn_pos_bias_v_to_fp16)[name = string("op_3507_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_35_perm_0 = const()[name = string("q_with_bias_v_35_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_401_transpose_x_0 = const()[name = string("x_401_transpose_x_0"), val = bool(false)];
+            bool x_401_transpose_y_0 = const()[name = string("x_401_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3509_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434486272))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434871168))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434870336))))[name = string("op_3509_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_35_cast_fp16 = transpose(perm = q_with_bias_v_35_perm_0, x = var_3507_cast_fp16)[name = string("transpose_193")];
+            tensor<fp16, [1, 8, 188, 375]> x_401_cast_fp16 = matmul(transpose_x = x_401_transpose_x_0, transpose_y = x_401_transpose_y_0, x = q_with_bias_v_35_cast_fp16, y = op_3509_to_fp16_quantized)[name = string("x_401_cast_fp16")];
+            tensor<int32, [8]> x_403_pad_0 = const()[name = string("x_403_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_403_mode_0 = const()[name = string("x_403_mode_0"), val = string("constant")];
+            fp16 const_258_to_fp16 = const()[name = string("const_258_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_403_cast_fp16 = pad(constant_val = const_258_to_fp16, mode = x_403_mode_0, pad = x_403_pad_0, x = x_401_cast_fp16)[name = string("x_403_cast_fp16")];
+            tensor<int32, [4]> var_3517 = const()[name = string("op_3517"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_405_cast_fp16 = reshape(shape = var_3517, x = x_403_cast_fp16)[name = string("x_405_cast_fp16")];
+            tensor<int32, [4]> var_3521_begin_0 = const()[name = string("op_3521_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3521_end_0 = const()[name = string("op_3521_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3521_end_mask_0 = const()[name = string("op_3521_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3521_cast_fp16 = slice_by_index(begin = var_3521_begin_0, end = var_3521_end_0, end_mask = var_3521_end_mask_0, x = x_405_cast_fp16)[name = string("op_3521_cast_fp16")];
+            tensor<int32, [4]> var_3522 = const()[name = string("op_3522"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_69_cast_fp16 = reshape(shape = var_3522, x = var_3521_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<int32, [4]> transpose_130_perm_0 = const()[name = string("transpose_130_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_131_perm_0 = const()[name = string("transpose_131_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_131 = transpose(perm = transpose_131_perm_0, x = k_69_cast_fp16)[name = string("transpose_191")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_130 = transpose(perm = transpose_130_perm_0, x = var_3505_cast_fp16)[name = string("transpose_192")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_71_begin_0 = const()[name = string("matrix_bd_71_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_71_end_0 = const()[name = string("matrix_bd_71_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_71_end_mask_0 = const()[name = string("matrix_bd_71_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3531_cast_fp16 = add(x = matrix_ac_35_cast_fp16, y = matrix_bd_71_cast_fp16)[name = string("op_3531_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<fp16, [1, 8, 188, 188]> _inversed_scores_69_cast_fp16 = mul(x = var_3531_cast_fp16, y = _inversed_scores_69_y_0_to_fp16)[name = string("_inversed_scores_69_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_71_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_69_cast_fp16, cond = mask_15)[name = string("scores_71_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3537_cast_fp16 = softmax(axis = var_152, x = scores_71_cast_fp16)[name = string("op_3537_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_925_cast_fp16 = select(a = var_164_to_fp16, b = var_3537_cast_fp16, cond = mask_15)[name = string("input_925_cast_fp16")];
+            bool x_407_transpose_x_0 = const()[name = string("x_407_transpose_x_0"), val = bool(false)];
+            bool x_407_transpose_y_0 = const()[name = string("x_407_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_39_cast_fp16 = transpose(perm = value_39_perm_0, x = v_35_cast_fp16)[name = string("transpose_190")];
+            tensor<fp16, [1, 8, 188, 128]> x_407_cast_fp16 = matmul(transpose_x = x_407_transpose_x_0, transpose_y = x_407_transpose_y_0, x = input_925_cast_fp16, y = value_39_cast_fp16)[name = string("x_407_cast_fp16")];
+            tensor<int32, [4]> var_3541_perm_0 = const()[name = string("op_3541_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3542 = const()[name = string("op_3542"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3541_cast_fp16 = transpose(perm = var_3541_perm_0, x = x_407_cast_fp16)[name = string("transpose_189")];
+            tensor<fp16, [1, 188, 1024]> input_927_cast_fp16 = reshape(shape = var_3542, x = var_3541_cast_fp16)[name = string("input_927_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_17_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(434871616))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435922368))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435920256))))[name = string("encoder_module_layers_17_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_17_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435923456)))];
+            tensor<fp16, [1, 188, 1024]> linear_160_cast_fp16 = linear(bias = encoder_module_layers_17_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_17_self_attn_linear_out_weight_to_fp16_quantized, x = input_927_cast_fp16)[name = string("linear_160_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_931_cast_fp16 = add(x = input_923_cast_fp16, y = linear_160_cast_fp16)[name = string("input_931_cast_fp16")];
+            tensor<int32, [1]> x_411_axes_0 = const()[name = string("x_411_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435925568)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435927680)))];
+            tensor<fp16, [1, 188, 1024]> x_411_cast_fp16 = layer_norm(axes = x_411_axes_0, beta = encoder_module_layers_17_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_conv_weight_to_fp16, x = input_931_cast_fp16)[name = string("x_411_cast_fp16")];
+            tensor<int32, [3]> input_933_perm_0 = const()[name = string("input_933_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_935_pad_type_0 = const()[name = string("input_935_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_935_strides_0 = const()[name = string("input_935_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_935_pad_0 = const()[name = string("input_935_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_935_dilations_0 = const()[name = string("input_935_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_935_groups_0 = const()[name = string("input_935_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(435929792))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(438031168))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(438027008))))[name = string("encoder_module_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(438033280)))];
+            tensor<fp16, [1, 1024, 188]> input_933_cast_fp16 = transpose(perm = input_933_perm_0, x = x_411_cast_fp16)[name = string("transpose_188")];
+            tensor<fp16, [1, 2048, 188]> input_935_cast_fp16 = conv(bias = encoder_module_layers_17_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_17_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_933_cast_fp16)[name = string("input_935_cast_fp16")];
+            int32 x_413_split_num_splits_0 = const()[name = string("x_413_split_num_splits_0"), val = int32(2)];
+            int32 x_413_split_axis_0 = const()[name = string("x_413_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_413_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_413_split_cast_fp16_1 = split(axis = x_413_split_axis_0, num_splits = x_413_split_num_splits_0, x = input_935_cast_fp16)[name = string("x_413_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_413_split_1_sigmoid_cast_fp16 = sigmoid(x = x_413_split_cast_fp16_1)[name = string("x_413_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_413_cast_fp16 = mul(x = x_413_split_cast_fp16_0, y = x_413_split_1_sigmoid_cast_fp16)[name = string("x_413_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_937_cast_fp16 = select(a = var_164_to_fp16, b = x_413_cast_fp16, cond = var_608)[name = string("input_937_cast_fp16")];
+            tensor<int32, [6]> input_939_pad_0 = const()[name = string("input_939_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_939_mode_0 = const()[name = string("input_939_mode_0"), val = string("constant")];
+            fp16 const_261_to_fp16 = const()[name = string("const_261_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_939_cast_fp16 = pad(constant_val = const_261_to_fp16, mode = input_939_mode_0, pad = input_939_pad_0, x = input_937_cast_fp16)[name = string("input_939_cast_fp16")];
+            string input_941_pad_type_0 = const()[name = string("input_941_pad_type_0"), val = string("valid")];
+            int32 input_941_groups_0 = const()[name = string("input_941_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_941_strides_0 = const()[name = string("input_941_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_941_pad_0 = const()[name = string("input_941_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_941_dilations_0 = const()[name = string("input_941_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_356_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(438037440))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(438048832))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(438046720))))[name = string("const_356_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_357_to_fp16 = const()[name = string("const_357_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(438049920)))];
+            tensor<fp16, [1, 1024, 188]> input_943_cast_fp16 = conv(bias = const_357_to_fp16, dilations = input_941_dilations_0, groups = input_941_groups_0, pad = input_941_pad_0, pad_type = input_941_pad_type_0, strides = input_941_strides_0, weight = const_356_to_fp16_quantized, x = input_939_cast_fp16)[name = string("input_943_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_945_cast_fp16 = silu(x = input_943_cast_fp16)[name = string("input_945_cast_fp16")];
+            string x_415_pad_type_0 = const()[name = string("x_415_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_415_strides_0 = const()[name = string("x_415_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_415_pad_0 = const()[name = string("x_415_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_415_dilations_0 = const()[name = string("x_415_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_415_groups_0 = const()[name = string("x_415_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(438052032))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439102784))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439100672))))[name = string("encoder_module_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439103872)))];
+            tensor<fp16, [1, 1024, 188]> x_415_cast_fp16 = conv(bias = encoder_module_layers_17_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_17_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_945_cast_fp16)[name = string("x_415_cast_fp16")];
+            tensor<int32, [3]> input_947_perm_0 = const()[name = string("input_947_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_947_cast_fp16 = transpose(perm = input_947_perm_0, x = x_415_cast_fp16)[name = string("transpose_187")];
+            tensor<fp16, [1, 188, 1024]> input_949_cast_fp16 = add(x = input_931_cast_fp16, y = input_947_cast_fp16)[name = string("input_949_cast_fp16")];
+            tensor<int32, [1]> input_951_axes_0 = const()[name = string("input_951_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439105984)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439108096)))];
+            tensor<fp16, [1, 188, 1024]> input_951_cast_fp16 = layer_norm(axes = input_951_axes_0, beta = encoder_module_layers_17_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_feed_forward2_weight_to_fp16, x = input_949_cast_fp16)[name = string("input_951_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(439110208))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443312832))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443304576))))[name = string("encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443316992)))];
+            tensor<fp16, [1, 188, 4096]> linear_161_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_17_feed_forward2_linear1_weight_to_fp16_quantized, x = input_951_cast_fp16)[name = string("linear_161_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_955_cast_fp16 = silu(x = linear_161_cast_fp16)[name = string("input_955_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(443325248))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447521728))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447519616))))[name = string("encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447522816)))];
+            tensor<fp16, [1, 188, 1024]> linear_162_cast_fp16 = linear(bias = encoder_module_layers_17_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_17_feed_forward2_linear2_weight_to_fp16_quantized, x = input_955_cast_fp16)[name = string("linear_162_cast_fp16")];
+            fp16 var_3608_to_fp16 = const()[name = string("op_3608_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3609_cast_fp16 = mul(x = linear_162_cast_fp16, y = var_3608_to_fp16)[name = string("op_3609_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_961_cast_fp16 = add(x = input_949_cast_fp16, y = var_3609_cast_fp16)[name = string("input_961_cast_fp16")];
+            tensor<int32, [1]> input_963_axes_0 = const()[name = string("input_963_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_17_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447524928)))];
+            tensor<fp16, [1024]> encoder_module_layers_17_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_17_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447527040)))];
+            tensor<fp16, [1, 188, 1024]> input_963_cast_fp16 = layer_norm(axes = input_963_axes_0, beta = encoder_module_layers_17_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_17_norm_out_weight_to_fp16, x = input_961_cast_fp16)[name = string("input_963_cast_fp16")];
+            tensor<int32, [1]> input_965_axes_0 = const()[name = string("input_965_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447529152)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447531264)))];
+            tensor<fp16, [1, 188, 1024]> input_965_cast_fp16 = layer_norm(axes = input_965_axes_0, beta = encoder_module_layers_18_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_feed_forward1_weight_to_fp16, x = input_963_cast_fp16)[name = string("input_965_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(447533376))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(451736000))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(451727744))))[name = string("encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(451740160)))];
+            tensor<fp16, [1, 188, 4096]> linear_163_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_18_feed_forward1_linear1_weight_to_fp16_quantized, x = input_965_cast_fp16)[name = string("linear_163_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_969_cast_fp16 = silu(x = linear_163_cast_fp16)[name = string("input_969_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(451748416))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(455944896))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(455942784))))[name = string("encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(455945984)))];
+            tensor<fp16, [1, 188, 1024]> linear_164_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_18_feed_forward1_linear2_weight_to_fp16_quantized, x = input_969_cast_fp16)[name = string("linear_164_cast_fp16")];
+            fp16 var_3639_to_fp16 = const()[name = string("op_3639_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3640_cast_fp16 = mul(x = linear_164_cast_fp16, y = var_3639_to_fp16)[name = string("op_3640_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_975_cast_fp16 = add(x = input_963_cast_fp16, y = var_3640_cast_fp16)[name = string("input_975_cast_fp16")];
+            tensor<int32, [1]> query_37_axes_0 = const()[name = string("query_37_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(455948096)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(455950208)))];
+            tensor<fp16, [1, 188, 1024]> query_37_cast_fp16 = layer_norm(axes = query_37_axes_0, beta = encoder_module_layers_18_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_self_att_weight_to_fp16, x = input_975_cast_fp16)[name = string("query_37_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(455952320))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457003072))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457000960))))[name = string("encoder_module_layers_18_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457004160)))];
+            tensor<fp16, [1, 188, 1024]> linear_165_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_q_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = string("linear_165_cast_fp16")];
+            tensor<int32, [4]> var_3657 = const()[name = string("op_3657"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_109_cast_fp16 = reshape(shape = var_3657, x = linear_165_cast_fp16)[name = string("q_109_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(457006272))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(458057024))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(458054912))))[name = string("encoder_module_layers_18_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(458058112)))];
+            tensor<fp16, [1, 188, 1024]> linear_166_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_k_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = string("linear_166_cast_fp16")];
+            tensor<int32, [4]> var_3662 = const()[name = string("op_3662"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_73_cast_fp16 = reshape(shape = var_3662, x = linear_166_cast_fp16)[name = string("k_73_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(458060224))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(459110976))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(459108864))))[name = string("encoder_module_layers_18_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(459112064)))];
+            tensor<fp16, [1, 188, 1024]> linear_167_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_v_weight_to_fp16_quantized, x = query_37_cast_fp16)[name = string("linear_167_cast_fp16")];
+            tensor<int32, [4]> var_3667 = const()[name = string("op_3667"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_37_cast_fp16 = reshape(shape = var_3667, x = linear_167_cast_fp16)[name = string("v_37_cast_fp16")];
+            tensor<int32, [4]> value_41_perm_0 = const()[name = string("value_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_18_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(459114176)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3679_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_module_layers_18_self_attn_pos_bias_u_to_fp16)[name = string("op_3679_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_18_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(459116288)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3681_cast_fp16 = add(x = q_109_cast_fp16, y = encoder_module_layers_18_self_attn_pos_bias_v_to_fp16)[name = string("op_3681_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_37_perm_0 = const()[name = string("q_with_bias_v_37_perm_0"), val = tensor<int32, [4]>([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<fp16, [1, 8, 128, 375]> op_3683_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(459118400))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(459503296))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(459502464))))[name = string("op_3683_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_37_cast_fp16 = transpose(perm = q_with_bias_v_37_perm_0, x = var_3681_cast_fp16)[name = string("transpose_186")];
+            tensor<fp16, [1, 8, 188, 375]> x_423_cast_fp16 = matmul(transpose_x = x_423_transpose_x_0, transpose_y = x_423_transpose_y_0, x = q_with_bias_v_37_cast_fp16, y = op_3683_to_fp16_quantized)[name = string("x_423_cast_fp16")];
+            tensor<int32, [8]> x_425_pad_0 = const()[name = string("x_425_pad_0"), val = tensor<int32, [8]>([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_268_to_fp16 = const()[name = string("const_268_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_425_cast_fp16 = pad(constant_val = const_268_to_fp16, mode = x_425_mode_0, pad = x_425_pad_0, x = x_423_cast_fp16)[name = string("x_425_cast_fp16")];
+            tensor<int32, [4]> var_3691 = const()[name = string("op_3691"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_427_cast_fp16 = reshape(shape = var_3691, x = x_425_cast_fp16)[name = string("x_427_cast_fp16")];
+            tensor<int32, [4]> var_3695_begin_0 = const()[name = string("op_3695_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3695_end_0 = const()[name = string("op_3695_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3695_end_mask_0 = const()[name = string("op_3695_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3695_cast_fp16 = slice_by_index(begin = var_3695_begin_0, end = var_3695_end_0, end_mask = var_3695_end_mask_0, x = x_427_cast_fp16)[name = string("op_3695_cast_fp16")];
+            tensor<int32, [4]> var_3696 = const()[name = string("op_3696"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_73_cast_fp16 = reshape(shape = var_3696, x = var_3695_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<int32, [4]> transpose_132_perm_0 = const()[name = string("transpose_132_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_133_perm_0 = const()[name = string("transpose_133_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_133 = transpose(perm = transpose_133_perm_0, x = k_73_cast_fp16)[name = string("transpose_184")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_132 = transpose(perm = transpose_132_perm_0, x = var_3679_cast_fp16)[name = string("transpose_185")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_75_begin_0 = const()[name = string("matrix_bd_75_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_75_end_0 = const()[name = string("matrix_bd_75_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_75_end_mask_0 = const()[name = string("matrix_bd_75_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3705_cast_fp16 = add(x = matrix_ac_37_cast_fp16, y = matrix_bd_75_cast_fp16)[name = string("op_3705_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<fp16, [1, 8, 188, 188]> _inversed_scores_73_cast_fp16 = mul(x = var_3705_cast_fp16, y = _inversed_scores_73_y_0_to_fp16)[name = string("_inversed_scores_73_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_75_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_73_cast_fp16, cond = mask_15)[name = string("scores_75_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3711_cast_fp16 = softmax(axis = var_152, x = scores_75_cast_fp16)[name = string("op_3711_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_977_cast_fp16 = select(a = var_164_to_fp16, b = var_3711_cast_fp16, cond = mask_15)[name = string("input_977_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<fp16, [1, 8, 188, 128]> value_41_cast_fp16 = transpose(perm = value_41_perm_0, x = v_37_cast_fp16)[name = string("transpose_183")];
+            tensor<fp16, [1, 8, 188, 128]> x_429_cast_fp16 = matmul(transpose_x = x_429_transpose_x_0, transpose_y = x_429_transpose_y_0, x = input_977_cast_fp16, y = value_41_cast_fp16)[name = string("x_429_cast_fp16")];
+            tensor<int32, [4]> var_3715_perm_0 = const()[name = string("op_3715_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3716 = const()[name = string("op_3716"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3715_cast_fp16 = transpose(perm = var_3715_perm_0, x = x_429_cast_fp16)[name = string("transpose_182")];
+            tensor<fp16, [1, 188, 1024]> input_979_cast_fp16 = reshape(shape = var_3716, x = var_3715_cast_fp16)[name = string("input_979_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_18_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(459503744))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460554496))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460552384))))[name = string("encoder_module_layers_18_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_18_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460555584)))];
+            tensor<fp16, [1, 188, 1024]> linear_169_cast_fp16 = linear(bias = encoder_module_layers_18_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_18_self_attn_linear_out_weight_to_fp16_quantized, x = input_979_cast_fp16)[name = string("linear_169_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_983_cast_fp16 = add(x = input_975_cast_fp16, y = linear_169_cast_fp16)[name = string("input_983_cast_fp16")];
+            tensor<int32, [1]> x_433_axes_0 = const()[name = string("x_433_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460557696)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460559808)))];
+            tensor<fp16, [1, 188, 1024]> x_433_cast_fp16 = layer_norm(axes = x_433_axes_0, beta = encoder_module_layers_18_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_conv_weight_to_fp16, x = input_983_cast_fp16)[name = string("x_433_cast_fp16")];
+            tensor<int32, [3]> input_985_perm_0 = const()[name = string("input_985_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_987_pad_type_0 = const()[name = string("input_987_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_987_strides_0 = const()[name = string("input_987_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_987_pad_0 = const()[name = string("input_987_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_987_dilations_0 = const()[name = string("input_987_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_987_groups_0 = const()[name = string("input_987_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(460561920))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(462663296))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(462659136))))[name = string("encoder_module_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(462665408)))];
+            tensor<fp16, [1, 1024, 188]> input_985_cast_fp16 = transpose(perm = input_985_perm_0, x = x_433_cast_fp16)[name = string("transpose_181")];
+            tensor<fp16, [1, 2048, 188]> input_987_cast_fp16 = conv(bias = encoder_module_layers_18_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_18_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_985_cast_fp16)[name = string("input_987_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<fp16, [1, 1024, 188]> x_435_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_435_split_cast_fp16_1 = split(axis = x_435_split_axis_0, num_splits = x_435_split_num_splits_0, x = input_987_cast_fp16)[name = string("x_435_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> 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<fp16, [1, 1024, 188]> input_989_cast_fp16 = select(a = var_164_to_fp16, b = x_435_cast_fp16, cond = var_608)[name = string("input_989_cast_fp16")];
+            tensor<int32, [6]> input_991_pad_0 = const()[name = string("input_991_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_991_mode_0 = const()[name = string("input_991_mode_0"), val = string("constant")];
+            fp16 const_271_to_fp16 = const()[name = string("const_271_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_991_cast_fp16 = pad(constant_val = const_271_to_fp16, mode = input_991_mode_0, pad = input_991_pad_0, x = input_989_cast_fp16)[name = string("input_991_cast_fp16")];
+            string input_993_pad_type_0 = const()[name = string("input_993_pad_type_0"), val = string("valid")];
+            int32 input_993_groups_0 = const()[name = string("input_993_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_993_strides_0 = const()[name = string("input_993_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_993_pad_0 = const()[name = string("input_993_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_993_dilations_0 = const()[name = string("input_993_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_358_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(462669568))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(462680960))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(462678848))))[name = string("const_358_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_359_to_fp16 = const()[name = string("const_359_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(462682048)))];
+            tensor<fp16, [1, 1024, 188]> input_995_cast_fp16 = conv(bias = const_359_to_fp16, dilations = input_993_dilations_0, groups = input_993_groups_0, pad = input_993_pad_0, pad_type = input_993_pad_type_0, strides = input_993_strides_0, weight = const_358_to_fp16_quantized, x = input_991_cast_fp16)[name = string("input_995_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_997_cast_fp16 = silu(x = input_995_cast_fp16)[name = string("input_997_cast_fp16")];
+            string x_437_pad_type_0 = const()[name = string("x_437_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_437_strides_0 = const()[name = string("x_437_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_437_pad_0 = const()[name = string("x_437_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_437_dilations_0 = const()[name = string("x_437_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_437_groups_0 = const()[name = string("x_437_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(462684160))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(463734912))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(463732800))))[name = string("encoder_module_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(463736000)))];
+            tensor<fp16, [1, 1024, 188]> x_437_cast_fp16 = conv(bias = encoder_module_layers_18_conv_pointwise_conv2_bias_to_fp16, 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_module_layers_18_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_997_cast_fp16)[name = string("x_437_cast_fp16")];
+            tensor<int32, [3]> input_999_perm_0 = const()[name = string("input_999_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_999_cast_fp16 = transpose(perm = input_999_perm_0, x = x_437_cast_fp16)[name = string("transpose_180")];
+            tensor<fp16, [1, 188, 1024]> input_1001_cast_fp16 = add(x = input_983_cast_fp16, y = input_999_cast_fp16)[name = string("input_1001_cast_fp16")];
+            tensor<int32, [1]> input_1003_axes_0 = const()[name = string("input_1003_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(463738112)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(463740224)))];
+            tensor<fp16, [1, 188, 1024]> input_1003_cast_fp16 = layer_norm(axes = input_1003_axes_0, beta = encoder_module_layers_18_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_feed_forward2_weight_to_fp16, x = input_1001_cast_fp16)[name = string("input_1003_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(463742336))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467944960))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467936704))))[name = string("encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467949120)))];
+            tensor<fp16, [1, 188, 4096]> linear_170_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_18_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1003_cast_fp16)[name = string("linear_170_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1007_cast_fp16 = silu(x = linear_170_cast_fp16)[name = string("input_1007_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467957376))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472153856))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472151744))))[name = string("encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472154944)))];
+            tensor<fp16, [1, 188, 1024]> linear_171_cast_fp16 = linear(bias = encoder_module_layers_18_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_18_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1007_cast_fp16)[name = string("linear_171_cast_fp16")];
+            fp16 var_3782_to_fp16 = const()[name = string("op_3782_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3783_cast_fp16 = mul(x = linear_171_cast_fp16, y = var_3782_to_fp16)[name = string("op_3783_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1013_cast_fp16 = add(x = input_1001_cast_fp16, y = var_3783_cast_fp16)[name = string("input_1013_cast_fp16")];
+            tensor<int32, [1]> input_1015_axes_0 = const()[name = string("input_1015_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_18_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472157056)))];
+            tensor<fp16, [1024]> encoder_module_layers_18_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_18_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472159168)))];
+            tensor<fp16, [1, 188, 1024]> input_1015_cast_fp16 = layer_norm(axes = input_1015_axes_0, beta = encoder_module_layers_18_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_18_norm_out_weight_to_fp16, x = input_1013_cast_fp16)[name = string("input_1015_cast_fp16")];
+            tensor<int32, [1]> input_1017_axes_0 = const()[name = string("input_1017_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472161280)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472163392)))];
+            tensor<fp16, [1, 188, 1024]> input_1017_cast_fp16 = layer_norm(axes = input_1017_axes_0, beta = encoder_module_layers_19_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_feed_forward1_weight_to_fp16, x = input_1015_cast_fp16)[name = string("input_1017_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472165504))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(476368128))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(476359872))))[name = string("encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(476372288)))];
+            tensor<fp16, [1, 188, 4096]> linear_172_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_19_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1017_cast_fp16)[name = string("linear_172_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1021_cast_fp16 = silu(x = linear_172_cast_fp16)[name = string("input_1021_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(476380544))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(480577024))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(480574912))))[name = string("encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(480578112)))];
+            tensor<fp16, [1, 188, 1024]> linear_173_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_19_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1021_cast_fp16)[name = string("linear_173_cast_fp16")];
+            fp16 var_3813_to_fp16 = const()[name = string("op_3813_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3814_cast_fp16 = mul(x = linear_173_cast_fp16, y = var_3813_to_fp16)[name = string("op_3814_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1027_cast_fp16 = add(x = input_1015_cast_fp16, y = var_3814_cast_fp16)[name = string("input_1027_cast_fp16")];
+            tensor<int32, [1]> query_39_axes_0 = const()[name = string("query_39_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(480580224)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(480582336)))];
+            tensor<fp16, [1, 188, 1024]> query_39_cast_fp16 = layer_norm(axes = query_39_axes_0, beta = encoder_module_layers_19_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_self_att_weight_to_fp16, x = input_1027_cast_fp16)[name = string("query_39_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(480584448))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481635200))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481633088))))[name = string("encoder_module_layers_19_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481636288)))];
+            tensor<fp16, [1, 188, 1024]> linear_174_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_q_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = string("linear_174_cast_fp16")];
+            tensor<int32, [4]> var_3831 = const()[name = string("op_3831"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_115_cast_fp16 = reshape(shape = var_3831, x = linear_174_cast_fp16)[name = string("q_115_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(481638400))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(482689152))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(482687040))))[name = string("encoder_module_layers_19_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(482690240)))];
+            tensor<fp16, [1, 188, 1024]> linear_175_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_k_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = string("linear_175_cast_fp16")];
+            tensor<int32, [4]> var_3836 = const()[name = string("op_3836"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_77_cast_fp16 = reshape(shape = var_3836, x = linear_175_cast_fp16)[name = string("k_77_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(482692352))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483743104))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483740992))))[name = string("encoder_module_layers_19_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483744192)))];
+            tensor<fp16, [1, 188, 1024]> linear_176_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_v_weight_to_fp16_quantized, x = query_39_cast_fp16)[name = string("linear_176_cast_fp16")];
+            tensor<int32, [4]> var_3841 = const()[name = string("op_3841"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_39_cast_fp16 = reshape(shape = var_3841, x = linear_176_cast_fp16)[name = string("v_39_cast_fp16")];
+            tensor<int32, [4]> value_43_perm_0 = const()[name = string("value_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_19_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483746304)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3853_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_module_layers_19_self_attn_pos_bias_u_to_fp16)[name = string("op_3853_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_19_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483748416)))];
+            tensor<fp16, [1, 188, 8, 128]> var_3855_cast_fp16 = add(x = q_115_cast_fp16, y = encoder_module_layers_19_self_attn_pos_bias_v_to_fp16)[name = string("op_3855_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_39_perm_0 = const()[name = string("q_with_bias_v_39_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_445_transpose_x_0 = const()[name = string("x_445_transpose_x_0"), val = bool(false)];
+            bool x_445_transpose_y_0 = const()[name = string("x_445_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_3857_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483750528))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484135424))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484134592))))[name = string("op_3857_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_39_cast_fp16 = transpose(perm = q_with_bias_v_39_perm_0, x = var_3855_cast_fp16)[name = string("transpose_179")];
+            tensor<fp16, [1, 8, 188, 375]> x_445_cast_fp16 = matmul(transpose_x = x_445_transpose_x_0, transpose_y = x_445_transpose_y_0, x = q_with_bias_v_39_cast_fp16, y = op_3857_to_fp16_quantized)[name = string("x_445_cast_fp16")];
+            tensor<int32, [8]> x_447_pad_0 = const()[name = string("x_447_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_447_mode_0 = const()[name = string("x_447_mode_0"), val = string("constant")];
+            fp16 const_278_to_fp16 = const()[name = string("const_278_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_447_cast_fp16 = pad(constant_val = const_278_to_fp16, mode = x_447_mode_0, pad = x_447_pad_0, x = x_445_cast_fp16)[name = string("x_447_cast_fp16")];
+            tensor<int32, [4]> var_3865 = const()[name = string("op_3865"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_449_cast_fp16 = reshape(shape = var_3865, x = x_447_cast_fp16)[name = string("x_449_cast_fp16")];
+            tensor<int32, [4]> var_3869_begin_0 = const()[name = string("op_3869_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_3869_end_0 = const()[name = string("op_3869_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_3869_end_mask_0 = const()[name = string("op_3869_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_3869_cast_fp16 = slice_by_index(begin = var_3869_begin_0, end = var_3869_end_0, end_mask = var_3869_end_mask_0, x = x_449_cast_fp16)[name = string("op_3869_cast_fp16")];
+            tensor<int32, [4]> var_3870 = const()[name = string("op_3870"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_77_cast_fp16 = reshape(shape = var_3870, x = var_3869_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<int32, [4]> transpose_134_perm_0 = const()[name = string("transpose_134_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_135_perm_0 = const()[name = string("transpose_135_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_135 = transpose(perm = transpose_135_perm_0, x = k_77_cast_fp16)[name = string("transpose_177")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_134 = transpose(perm = transpose_134_perm_0, x = var_3853_cast_fp16)[name = string("transpose_178")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_79_begin_0 = const()[name = string("matrix_bd_79_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_79_end_0 = const()[name = string("matrix_bd_79_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_79_end_mask_0 = const()[name = string("matrix_bd_79_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_3879_cast_fp16 = add(x = matrix_ac_39_cast_fp16, y = matrix_bd_79_cast_fp16)[name = string("op_3879_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<fp16, [1, 8, 188, 188]> _inversed_scores_77_cast_fp16 = mul(x = var_3879_cast_fp16, y = _inversed_scores_77_y_0_to_fp16)[name = string("_inversed_scores_77_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_79_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_77_cast_fp16, cond = mask_15)[name = string("scores_79_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_3885_cast_fp16 = softmax(axis = var_152, x = scores_79_cast_fp16)[name = string("op_3885_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1029_cast_fp16 = select(a = var_164_to_fp16, b = var_3885_cast_fp16, cond = mask_15)[name = string("input_1029_cast_fp16")];
+            bool x_451_transpose_x_0 = const()[name = string("x_451_transpose_x_0"), val = bool(false)];
+            bool x_451_transpose_y_0 = const()[name = string("x_451_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_43_cast_fp16 = transpose(perm = value_43_perm_0, x = v_39_cast_fp16)[name = string("transpose_176")];
+            tensor<fp16, [1, 8, 188, 128]> x_451_cast_fp16 = matmul(transpose_x = x_451_transpose_x_0, transpose_y = x_451_transpose_y_0, x = input_1029_cast_fp16, y = value_43_cast_fp16)[name = string("x_451_cast_fp16")];
+            tensor<int32, [4]> var_3889_perm_0 = const()[name = string("op_3889_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_3890 = const()[name = string("op_3890"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_3889_cast_fp16 = transpose(perm = var_3889_perm_0, x = x_451_cast_fp16)[name = string("transpose_175")];
+            tensor<fp16, [1, 188, 1024]> input_1031_cast_fp16 = reshape(shape = var_3890, x = var_3889_cast_fp16)[name = string("input_1031_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_19_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484135872))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485186624))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485184512))))[name = string("encoder_module_layers_19_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_19_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485187712)))];
+            tensor<fp16, [1, 188, 1024]> linear_178_cast_fp16 = linear(bias = encoder_module_layers_19_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_19_self_attn_linear_out_weight_to_fp16_quantized, x = input_1031_cast_fp16)[name = string("linear_178_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1035_cast_fp16 = add(x = input_1027_cast_fp16, y = linear_178_cast_fp16)[name = string("input_1035_cast_fp16")];
+            tensor<int32, [1]> x_455_axes_0 = const()[name = string("x_455_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485189824)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485191936)))];
+            tensor<fp16, [1, 188, 1024]> x_455_cast_fp16 = layer_norm(axes = x_455_axes_0, beta = encoder_module_layers_19_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_conv_weight_to_fp16, x = input_1035_cast_fp16)[name = string("x_455_cast_fp16")];
+            tensor<int32, [3]> input_1037_perm_0 = const()[name = string("input_1037_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1039_pad_type_0 = const()[name = string("input_1039_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1039_strides_0 = const()[name = string("input_1039_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1039_pad_0 = const()[name = string("input_1039_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1039_dilations_0 = const()[name = string("input_1039_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1039_groups_0 = const()[name = string("input_1039_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(485194048))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(487295424))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(487291264))))[name = string("encoder_module_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(487297536)))];
+            tensor<fp16, [1, 1024, 188]> input_1037_cast_fp16 = transpose(perm = input_1037_perm_0, x = x_455_cast_fp16)[name = string("transpose_174")];
+            tensor<fp16, [1, 2048, 188]> input_1039_cast_fp16 = conv(bias = encoder_module_layers_19_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_19_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1037_cast_fp16)[name = string("input_1039_cast_fp16")];
+            int32 x_457_split_num_splits_0 = const()[name = string("x_457_split_num_splits_0"), val = int32(2)];
+            int32 x_457_split_axis_0 = const()[name = string("x_457_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_457_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_457_split_cast_fp16_1 = split(axis = x_457_split_axis_0, num_splits = x_457_split_num_splits_0, x = input_1039_cast_fp16)[name = string("x_457_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_457_split_1_sigmoid_cast_fp16 = sigmoid(x = x_457_split_cast_fp16_1)[name = string("x_457_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_457_cast_fp16 = mul(x = x_457_split_cast_fp16_0, y = x_457_split_1_sigmoid_cast_fp16)[name = string("x_457_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1041_cast_fp16 = select(a = var_164_to_fp16, b = x_457_cast_fp16, cond = var_608)[name = string("input_1041_cast_fp16")];
+            tensor<int32, [6]> input_1043_pad_0 = const()[name = string("input_1043_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1043_mode_0 = const()[name = string("input_1043_mode_0"), val = string("constant")];
+            fp16 const_281_to_fp16 = const()[name = string("const_281_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1043_cast_fp16 = pad(constant_val = const_281_to_fp16, mode = input_1043_mode_0, pad = input_1043_pad_0, x = input_1041_cast_fp16)[name = string("input_1043_cast_fp16")];
+            string input_1045_pad_type_0 = const()[name = string("input_1045_pad_type_0"), val = string("valid")];
+            int32 input_1045_groups_0 = const()[name = string("input_1045_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1045_strides_0 = const()[name = string("input_1045_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1045_pad_0 = const()[name = string("input_1045_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1045_dilations_0 = const()[name = string("input_1045_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_360_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(487301696))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(487313088))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(487310976))))[name = string("const_360_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_361_to_fp16 = const()[name = string("const_361_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(487314176)))];
+            tensor<fp16, [1, 1024, 188]> input_1047_cast_fp16 = conv(bias = const_361_to_fp16, dilations = input_1045_dilations_0, groups = input_1045_groups_0, pad = input_1045_pad_0, pad_type = input_1045_pad_type_0, strides = input_1045_strides_0, weight = const_360_to_fp16_quantized, x = input_1043_cast_fp16)[name = string("input_1047_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1049_cast_fp16 = silu(x = input_1047_cast_fp16)[name = string("input_1049_cast_fp16")];
+            string x_459_pad_type_0 = const()[name = string("x_459_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_459_strides_0 = const()[name = string("x_459_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_459_pad_0 = const()[name = string("x_459_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_459_dilations_0 = const()[name = string("x_459_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_459_groups_0 = const()[name = string("x_459_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(487316288))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488367040))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488364928))))[name = string("encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488368128)))];
+            tensor<fp16, [1, 1024, 188]> x_459_cast_fp16 = conv(bias = encoder_module_layers_19_conv_pointwise_conv2_bias_to_fp16, dilations = x_459_dilations_0, groups = x_459_groups_0, pad = x_459_pad_0, pad_type = x_459_pad_type_0, strides = x_459_strides_0, weight = encoder_module_layers_19_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1049_cast_fp16)[name = string("x_459_cast_fp16")];
+            tensor<int32, [3]> input_1051_perm_0 = const()[name = string("input_1051_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1051_cast_fp16 = transpose(perm = input_1051_perm_0, x = x_459_cast_fp16)[name = string("transpose_173")];
+            tensor<fp16, [1, 188, 1024]> input_1053_cast_fp16 = add(x = input_1035_cast_fp16, y = input_1051_cast_fp16)[name = string("input_1053_cast_fp16")];
+            tensor<int32, [1]> input_1055_axes_0 = const()[name = string("input_1055_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488370240)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488372352)))];
+            tensor<fp16, [1, 188, 1024]> input_1055_cast_fp16 = layer_norm(axes = input_1055_axes_0, beta = encoder_module_layers_19_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_feed_forward2_weight_to_fp16, x = input_1053_cast_fp16)[name = string("input_1055_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488374464))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492577088))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492568832))))[name = string("encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492581248)))];
+            tensor<fp16, [1, 188, 4096]> linear_179_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_19_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1055_cast_fp16)[name = string("linear_179_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1059_cast_fp16 = silu(x = linear_179_cast_fp16)[name = string("input_1059_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(492589504))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(496785984))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(496783872))))[name = string("encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(496787072)))];
+            tensor<fp16, [1, 188, 1024]> linear_180_cast_fp16 = linear(bias = encoder_module_layers_19_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_19_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1059_cast_fp16)[name = string("linear_180_cast_fp16")];
+            fp16 var_3956_to_fp16 = const()[name = string("op_3956_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3957_cast_fp16 = mul(x = linear_180_cast_fp16, y = var_3956_to_fp16)[name = string("op_3957_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1065_cast_fp16 = add(x = input_1053_cast_fp16, y = var_3957_cast_fp16)[name = string("input_1065_cast_fp16")];
+            tensor<int32, [1]> input_1067_axes_0 = const()[name = string("input_1067_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_19_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(496789184)))];
+            tensor<fp16, [1024]> encoder_module_layers_19_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_19_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(496791296)))];
+            tensor<fp16, [1, 188, 1024]> input_1067_cast_fp16 = layer_norm(axes = input_1067_axes_0, beta = encoder_module_layers_19_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_19_norm_out_weight_to_fp16, x = input_1065_cast_fp16)[name = string("input_1067_cast_fp16")];
+            tensor<int32, [1]> input_1069_axes_0 = const()[name = string("input_1069_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(496793408)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(496795520)))];
+            tensor<fp16, [1, 188, 1024]> input_1069_cast_fp16 = layer_norm(axes = input_1069_axes_0, beta = encoder_module_layers_20_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_feed_forward1_weight_to_fp16, x = input_1067_cast_fp16)[name = string("input_1069_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(496797632))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501000256))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(500992000))))[name = string("encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501004416)))];
+            tensor<fp16, [1, 188, 4096]> linear_181_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_20_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1069_cast_fp16)[name = string("linear_181_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1073_cast_fp16 = silu(x = linear_181_cast_fp16)[name = string("input_1073_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501012672))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(505209152))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(505207040))))[name = string("encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(505210240)))];
+            tensor<fp16, [1, 188, 1024]> linear_182_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_20_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1073_cast_fp16)[name = string("linear_182_cast_fp16")];
+            fp16 var_3987_to_fp16 = const()[name = string("op_3987_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_3988_cast_fp16 = mul(x = linear_182_cast_fp16, y = var_3987_to_fp16)[name = string("op_3988_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1079_cast_fp16 = add(x = input_1067_cast_fp16, y = var_3988_cast_fp16)[name = string("input_1079_cast_fp16")];
+            tensor<int32, [1]> query_41_axes_0 = const()[name = string("query_41_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(505212352)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(505214464)))];
+            tensor<fp16, [1, 188, 1024]> query_41_cast_fp16 = layer_norm(axes = query_41_axes_0, beta = encoder_module_layers_20_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_self_att_weight_to_fp16, x = input_1079_cast_fp16)[name = string("query_41_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(505216576))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(506267328))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(506265216))))[name = string("encoder_module_layers_20_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(506268416)))];
+            tensor<fp16, [1, 188, 1024]> linear_183_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_q_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = string("linear_183_cast_fp16")];
+            tensor<int32, [4]> var_4005 = const()[name = string("op_4005"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_121_cast_fp16 = reshape(shape = var_4005, x = linear_183_cast_fp16)[name = string("q_121_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(506270528))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(507321280))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(507319168))))[name = string("encoder_module_layers_20_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(507322368)))];
+            tensor<fp16, [1, 188, 1024]> linear_184_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_k_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = string("linear_184_cast_fp16")];
+            tensor<int32, [4]> var_4010 = const()[name = string("op_4010"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_81_cast_fp16 = reshape(shape = var_4010, x = linear_184_cast_fp16)[name = string("k_81_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(507324480))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(508375232))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(508373120))))[name = string("encoder_module_layers_20_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(508376320)))];
+            tensor<fp16, [1, 188, 1024]> linear_185_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_v_weight_to_fp16_quantized, x = query_41_cast_fp16)[name = string("linear_185_cast_fp16")];
+            tensor<int32, [4]> var_4015 = const()[name = string("op_4015"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_41_cast_fp16 = reshape(shape = var_4015, x = linear_185_cast_fp16)[name = string("v_41_cast_fp16")];
+            tensor<int32, [4]> value_45_perm_0 = const()[name = string("value_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_20_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(508378432)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4027_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_module_layers_20_self_attn_pos_bias_u_to_fp16)[name = string("op_4027_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_20_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(508380544)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4029_cast_fp16 = add(x = q_121_cast_fp16, y = encoder_module_layers_20_self_attn_pos_bias_v_to_fp16)[name = string("op_4029_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_41_perm_0 = const()[name = string("q_with_bias_v_41_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_467_transpose_x_0 = const()[name = string("x_467_transpose_x_0"), val = bool(false)];
+            bool x_467_transpose_y_0 = const()[name = string("x_467_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_4031_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(508382656))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(508767552))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(508766720))))[name = string("op_4031_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_41_cast_fp16 = transpose(perm = q_with_bias_v_41_perm_0, x = var_4029_cast_fp16)[name = string("transpose_172")];
+            tensor<fp16, [1, 8, 188, 375]> x_467_cast_fp16 = matmul(transpose_x = x_467_transpose_x_0, transpose_y = x_467_transpose_y_0, x = q_with_bias_v_41_cast_fp16, y = op_4031_to_fp16_quantized)[name = string("x_467_cast_fp16")];
+            tensor<int32, [8]> x_469_pad_0 = const()[name = string("x_469_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_469_mode_0 = const()[name = string("x_469_mode_0"), val = string("constant")];
+            fp16 const_288_to_fp16 = const()[name = string("const_288_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_469_cast_fp16 = pad(constant_val = const_288_to_fp16, mode = x_469_mode_0, pad = x_469_pad_0, x = x_467_cast_fp16)[name = string("x_469_cast_fp16")];
+            tensor<int32, [4]> var_4039 = const()[name = string("op_4039"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_471_cast_fp16 = reshape(shape = var_4039, x = x_469_cast_fp16)[name = string("x_471_cast_fp16")];
+            tensor<int32, [4]> var_4043_begin_0 = const()[name = string("op_4043_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4043_end_0 = const()[name = string("op_4043_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4043_end_mask_0 = const()[name = string("op_4043_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4043_cast_fp16 = slice_by_index(begin = var_4043_begin_0, end = var_4043_end_0, end_mask = var_4043_end_mask_0, x = x_471_cast_fp16)[name = string("op_4043_cast_fp16")];
+            tensor<int32, [4]> var_4044 = const()[name = string("op_4044"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_81_cast_fp16 = reshape(shape = var_4044, x = var_4043_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<int32, [4]> transpose_136_perm_0 = const()[name = string("transpose_136_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_137_perm_0 = const()[name = string("transpose_137_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_137 = transpose(perm = transpose_137_perm_0, x = k_81_cast_fp16)[name = string("transpose_170")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_136 = transpose(perm = transpose_136_perm_0, x = var_4027_cast_fp16)[name = string("transpose_171")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_83_begin_0 = const()[name = string("matrix_bd_83_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_83_end_0 = const()[name = string("matrix_bd_83_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_83_end_mask_0 = const()[name = string("matrix_bd_83_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4053_cast_fp16 = add(x = matrix_ac_41_cast_fp16, y = matrix_bd_83_cast_fp16)[name = string("op_4053_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<fp16, [1, 8, 188, 188]> _inversed_scores_81_cast_fp16 = mul(x = var_4053_cast_fp16, y = _inversed_scores_81_y_0_to_fp16)[name = string("_inversed_scores_81_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_83_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_81_cast_fp16, cond = mask_15)[name = string("scores_83_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4059_cast_fp16 = softmax(axis = var_152, x = scores_83_cast_fp16)[name = string("op_4059_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1081_cast_fp16 = select(a = var_164_to_fp16, b = var_4059_cast_fp16, cond = mask_15)[name = string("input_1081_cast_fp16")];
+            bool x_473_transpose_x_0 = const()[name = string("x_473_transpose_x_0"), val = bool(false)];
+            bool x_473_transpose_y_0 = const()[name = string("x_473_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_45_cast_fp16 = transpose(perm = value_45_perm_0, x = v_41_cast_fp16)[name = string("transpose_169")];
+            tensor<fp16, [1, 8, 188, 128]> x_473_cast_fp16 = matmul(transpose_x = x_473_transpose_x_0, transpose_y = x_473_transpose_y_0, x = input_1081_cast_fp16, y = value_45_cast_fp16)[name = string("x_473_cast_fp16")];
+            tensor<int32, [4]> var_4063_perm_0 = const()[name = string("op_4063_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4064 = const()[name = string("op_4064"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4063_cast_fp16 = transpose(perm = var_4063_perm_0, x = x_473_cast_fp16)[name = string("transpose_168")];
+            tensor<fp16, [1, 188, 1024]> input_1083_cast_fp16 = reshape(shape = var_4064, x = var_4063_cast_fp16)[name = string("input_1083_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_20_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(508768000))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(509818752))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(509816640))))[name = string("encoder_module_layers_20_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_20_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(509819840)))];
+            tensor<fp16, [1, 188, 1024]> linear_187_cast_fp16 = linear(bias = encoder_module_layers_20_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_20_self_attn_linear_out_weight_to_fp16_quantized, x = input_1083_cast_fp16)[name = string("linear_187_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1087_cast_fp16 = add(x = input_1079_cast_fp16, y = linear_187_cast_fp16)[name = string("input_1087_cast_fp16")];
+            tensor<int32, [1]> x_477_axes_0 = const()[name = string("x_477_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(509821952)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(509824064)))];
+            tensor<fp16, [1, 188, 1024]> x_477_cast_fp16 = layer_norm(axes = x_477_axes_0, beta = encoder_module_layers_20_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_conv_weight_to_fp16, x = input_1087_cast_fp16)[name = string("x_477_cast_fp16")];
+            tensor<int32, [3]> input_1089_perm_0 = const()[name = string("input_1089_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1091_pad_type_0 = const()[name = string("input_1091_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1091_strides_0 = const()[name = string("input_1091_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1091_pad_0 = const()[name = string("input_1091_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1091_dilations_0 = const()[name = string("input_1091_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1091_groups_0 = const()[name = string("input_1091_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(509826176))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511927552))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511923392))))[name = string("encoder_module_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511929664)))];
+            tensor<fp16, [1, 1024, 188]> input_1089_cast_fp16 = transpose(perm = input_1089_perm_0, x = x_477_cast_fp16)[name = string("transpose_167")];
+            tensor<fp16, [1, 2048, 188]> input_1091_cast_fp16 = conv(bias = encoder_module_layers_20_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_20_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1089_cast_fp16)[name = string("input_1091_cast_fp16")];
+            int32 x_479_split_num_splits_0 = const()[name = string("x_479_split_num_splits_0"), val = int32(2)];
+            int32 x_479_split_axis_0 = const()[name = string("x_479_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_479_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_479_split_cast_fp16_1 = split(axis = x_479_split_axis_0, num_splits = x_479_split_num_splits_0, x = input_1091_cast_fp16)[name = string("x_479_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_479_split_1_sigmoid_cast_fp16 = sigmoid(x = x_479_split_cast_fp16_1)[name = string("x_479_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_479_cast_fp16 = mul(x = x_479_split_cast_fp16_0, y = x_479_split_1_sigmoid_cast_fp16)[name = string("x_479_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1093_cast_fp16 = select(a = var_164_to_fp16, b = x_479_cast_fp16, cond = var_608)[name = string("input_1093_cast_fp16")];
+            tensor<int32, [6]> input_1095_pad_0 = const()[name = string("input_1095_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1095_mode_0 = const()[name = string("input_1095_mode_0"), val = string("constant")];
+            fp16 const_291_to_fp16 = const()[name = string("const_291_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1095_cast_fp16 = pad(constant_val = const_291_to_fp16, mode = input_1095_mode_0, pad = input_1095_pad_0, x = input_1093_cast_fp16)[name = string("input_1095_cast_fp16")];
+            string input_1097_pad_type_0 = const()[name = string("input_1097_pad_type_0"), val = string("valid")];
+            int32 input_1097_groups_0 = const()[name = string("input_1097_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1097_strides_0 = const()[name = string("input_1097_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1097_pad_0 = const()[name = string("input_1097_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1097_dilations_0 = const()[name = string("input_1097_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_362_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511933824))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511945216))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511943104))))[name = string("const_362_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_363_to_fp16 = const()[name = string("const_363_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511946304)))];
+            tensor<fp16, [1, 1024, 188]> input_1099_cast_fp16 = conv(bias = const_363_to_fp16, dilations = input_1097_dilations_0, groups = input_1097_groups_0, pad = input_1097_pad_0, pad_type = input_1097_pad_type_0, strides = input_1097_strides_0, weight = const_362_to_fp16_quantized, x = input_1095_cast_fp16)[name = string("input_1099_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1101_cast_fp16 = silu(x = input_1099_cast_fp16)[name = string("input_1101_cast_fp16")];
+            string x_481_pad_type_0 = const()[name = string("x_481_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_481_strides_0 = const()[name = string("x_481_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_481_pad_0 = const()[name = string("x_481_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_481_dilations_0 = const()[name = string("x_481_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_481_groups_0 = const()[name = string("x_481_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(511948416))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(512999168))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(512997056))))[name = string("encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513000256)))];
+            tensor<fp16, [1, 1024, 188]> x_481_cast_fp16 = conv(bias = encoder_module_layers_20_conv_pointwise_conv2_bias_to_fp16, dilations = x_481_dilations_0, groups = x_481_groups_0, pad = x_481_pad_0, pad_type = x_481_pad_type_0, strides = x_481_strides_0, weight = encoder_module_layers_20_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1101_cast_fp16)[name = string("x_481_cast_fp16")];
+            tensor<int32, [3]> input_1103_perm_0 = const()[name = string("input_1103_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1103_cast_fp16 = transpose(perm = input_1103_perm_0, x = x_481_cast_fp16)[name = string("transpose_166")];
+            tensor<fp16, [1, 188, 1024]> input_1105_cast_fp16 = add(x = input_1087_cast_fp16, y = input_1103_cast_fp16)[name = string("input_1105_cast_fp16")];
+            tensor<int32, [1]> input_1107_axes_0 = const()[name = string("input_1107_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513002368)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513004480)))];
+            tensor<fp16, [1, 188, 1024]> input_1107_cast_fp16 = layer_norm(axes = input_1107_axes_0, beta = encoder_module_layers_20_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_feed_forward2_weight_to_fp16, x = input_1105_cast_fp16)[name = string("input_1107_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513006592))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(517209216))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(517200960))))[name = string("encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(517213376)))];
+            tensor<fp16, [1, 188, 4096]> linear_188_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_20_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1107_cast_fp16)[name = string("linear_188_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1111_cast_fp16 = silu(x = linear_188_cast_fp16)[name = string("input_1111_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(517221632))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(521418112))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(521416000))))[name = string("encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(521419200)))];
+            tensor<fp16, [1, 188, 1024]> linear_189_cast_fp16 = linear(bias = encoder_module_layers_20_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_20_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1111_cast_fp16)[name = string("linear_189_cast_fp16")];
+            fp16 var_4130_to_fp16 = const()[name = string("op_4130_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4131_cast_fp16 = mul(x = linear_189_cast_fp16, y = var_4130_to_fp16)[name = string("op_4131_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1117_cast_fp16 = add(x = input_1105_cast_fp16, y = var_4131_cast_fp16)[name = string("input_1117_cast_fp16")];
+            tensor<int32, [1]> input_1119_axes_0 = const()[name = string("input_1119_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_20_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(521421312)))];
+            tensor<fp16, [1024]> encoder_module_layers_20_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_20_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(521423424)))];
+            tensor<fp16, [1, 188, 1024]> input_1119_cast_fp16 = layer_norm(axes = input_1119_axes_0, beta = encoder_module_layers_20_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_20_norm_out_weight_to_fp16, x = input_1117_cast_fp16)[name = string("input_1119_cast_fp16")];
+            tensor<int32, [1]> input_1121_axes_0 = const()[name = string("input_1121_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(521425536)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(521427648)))];
+            tensor<fp16, [1, 188, 1024]> input_1121_cast_fp16 = layer_norm(axes = input_1121_axes_0, beta = encoder_module_layers_21_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_feed_forward1_weight_to_fp16, x = input_1119_cast_fp16)[name = string("input_1121_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(521429760))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525632384))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525624128))))[name = string("encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525636544)))];
+            tensor<fp16, [1, 188, 4096]> linear_190_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_21_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1121_cast_fp16)[name = string("linear_190_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1125_cast_fp16 = silu(x = linear_190_cast_fp16)[name = string("input_1125_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525644800))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529841280))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529839168))))[name = string("encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529842368)))];
+            tensor<fp16, [1, 188, 1024]> linear_191_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_21_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1125_cast_fp16)[name = string("linear_191_cast_fp16")];
+            fp16 var_4161_to_fp16 = const()[name = string("op_4161_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4162_cast_fp16 = mul(x = linear_191_cast_fp16, y = var_4161_to_fp16)[name = string("op_4162_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1131_cast_fp16 = add(x = input_1119_cast_fp16, y = var_4162_cast_fp16)[name = string("input_1131_cast_fp16")];
+            tensor<int32, [1]> query_43_axes_0 = const()[name = string("query_43_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529844480)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529846592)))];
+            tensor<fp16, [1, 188, 1024]> query_43_cast_fp16 = layer_norm(axes = query_43_axes_0, beta = encoder_module_layers_21_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_self_att_weight_to_fp16, x = input_1131_cast_fp16)[name = string("query_43_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529848704))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530899456))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530897344))))[name = string("encoder_module_layers_21_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530900544)))];
+            tensor<fp16, [1, 188, 1024]> linear_192_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_q_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = string("linear_192_cast_fp16")];
+            tensor<int32, [4]> var_4179 = const()[name = string("op_4179"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_127_cast_fp16 = reshape(shape = var_4179, x = linear_192_cast_fp16)[name = string("q_127_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530902656))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531953408))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531951296))))[name = string("encoder_module_layers_21_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531954496)))];
+            tensor<fp16, [1, 188, 1024]> linear_193_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_k_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = string("linear_193_cast_fp16")];
+            tensor<int32, [4]> var_4184 = const()[name = string("op_4184"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_85_cast_fp16 = reshape(shape = var_4184, x = linear_193_cast_fp16)[name = string("k_85_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531956608))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533007360))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533005248))))[name = string("encoder_module_layers_21_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533008448)))];
+            tensor<fp16, [1, 188, 1024]> linear_194_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_v_weight_to_fp16_quantized, x = query_43_cast_fp16)[name = string("linear_194_cast_fp16")];
+            tensor<int32, [4]> var_4189 = const()[name = string("op_4189"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_43_cast_fp16 = reshape(shape = var_4189, x = linear_194_cast_fp16)[name = string("v_43_cast_fp16")];
+            tensor<int32, [4]> value_47_perm_0 = const()[name = string("value_47_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_21_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533010560)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4201_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_module_layers_21_self_attn_pos_bias_u_to_fp16)[name = string("op_4201_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_21_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533012672)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4203_cast_fp16 = add(x = q_127_cast_fp16, y = encoder_module_layers_21_self_attn_pos_bias_v_to_fp16)[name = string("op_4203_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_43_perm_0 = const()[name = string("q_with_bias_v_43_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_489_transpose_x_0 = const()[name = string("x_489_transpose_x_0"), val = bool(false)];
+            bool x_489_transpose_y_0 = const()[name = string("x_489_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_4205_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533014784))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533399680))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533398848))))[name = string("op_4205_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_43_cast_fp16 = transpose(perm = q_with_bias_v_43_perm_0, x = var_4203_cast_fp16)[name = string("transpose_165")];
+            tensor<fp16, [1, 8, 188, 375]> x_489_cast_fp16 = matmul(transpose_x = x_489_transpose_x_0, transpose_y = x_489_transpose_y_0, x = q_with_bias_v_43_cast_fp16, y = op_4205_to_fp16_quantized)[name = string("x_489_cast_fp16")];
+            tensor<int32, [8]> x_491_pad_0 = const()[name = string("x_491_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_491_mode_0 = const()[name = string("x_491_mode_0"), val = string("constant")];
+            fp16 const_298_to_fp16 = const()[name = string("const_298_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_491_cast_fp16 = pad(constant_val = const_298_to_fp16, mode = x_491_mode_0, pad = x_491_pad_0, x = x_489_cast_fp16)[name = string("x_491_cast_fp16")];
+            tensor<int32, [4]> var_4213 = const()[name = string("op_4213"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_493_cast_fp16 = reshape(shape = var_4213, x = x_491_cast_fp16)[name = string("x_493_cast_fp16")];
+            tensor<int32, [4]> var_4217_begin_0 = const()[name = string("op_4217_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4217_end_0 = const()[name = string("op_4217_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4217_end_mask_0 = const()[name = string("op_4217_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4217_cast_fp16 = slice_by_index(begin = var_4217_begin_0, end = var_4217_end_0, end_mask = var_4217_end_mask_0, x = x_493_cast_fp16)[name = string("op_4217_cast_fp16")];
+            tensor<int32, [4]> var_4218 = const()[name = string("op_4218"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_85_cast_fp16 = reshape(shape = var_4218, x = var_4217_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<int32, [4]> transpose_138_perm_0 = const()[name = string("transpose_138_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_139_perm_0 = const()[name = string("transpose_139_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_139 = transpose(perm = transpose_139_perm_0, x = k_85_cast_fp16)[name = string("transpose_163")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_138 = transpose(perm = transpose_138_perm_0, x = var_4201_cast_fp16)[name = string("transpose_164")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_87_begin_0 = const()[name = string("matrix_bd_87_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_87_end_0 = const()[name = string("matrix_bd_87_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_87_end_mask_0 = const()[name = string("matrix_bd_87_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4227_cast_fp16 = add(x = matrix_ac_43_cast_fp16, y = matrix_bd_87_cast_fp16)[name = string("op_4227_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<fp16, [1, 8, 188, 188]> _inversed_scores_85_cast_fp16 = mul(x = var_4227_cast_fp16, y = _inversed_scores_85_y_0_to_fp16)[name = string("_inversed_scores_85_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_87_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_85_cast_fp16, cond = mask_15)[name = string("scores_87_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4233_cast_fp16 = softmax(axis = var_152, x = scores_87_cast_fp16)[name = string("op_4233_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1133_cast_fp16 = select(a = var_164_to_fp16, b = var_4233_cast_fp16, cond = mask_15)[name = string("input_1133_cast_fp16")];
+            bool x_495_transpose_x_0 = const()[name = string("x_495_transpose_x_0"), val = bool(false)];
+            bool x_495_transpose_y_0 = const()[name = string("x_495_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_47_cast_fp16 = transpose(perm = value_47_perm_0, x = v_43_cast_fp16)[name = string("transpose_162")];
+            tensor<fp16, [1, 8, 188, 128]> x_495_cast_fp16 = matmul(transpose_x = x_495_transpose_x_0, transpose_y = x_495_transpose_y_0, x = input_1133_cast_fp16, y = value_47_cast_fp16)[name = string("x_495_cast_fp16")];
+            tensor<int32, [4]> var_4237_perm_0 = const()[name = string("op_4237_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4238 = const()[name = string("op_4238"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4237_cast_fp16 = transpose(perm = var_4237_perm_0, x = x_495_cast_fp16)[name = string("transpose_161")];
+            tensor<fp16, [1, 188, 1024]> input_1135_cast_fp16 = reshape(shape = var_4238, x = var_4237_cast_fp16)[name = string("input_1135_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_21_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(533400128))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534450880))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534448768))))[name = string("encoder_module_layers_21_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_21_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534451968)))];
+            tensor<fp16, [1, 188, 1024]> linear_196_cast_fp16 = linear(bias = encoder_module_layers_21_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_21_self_attn_linear_out_weight_to_fp16_quantized, x = input_1135_cast_fp16)[name = string("linear_196_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1139_cast_fp16 = add(x = input_1131_cast_fp16, y = linear_196_cast_fp16)[name = string("input_1139_cast_fp16")];
+            tensor<int32, [1]> x_499_axes_0 = const()[name = string("x_499_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534454080)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534456192)))];
+            tensor<fp16, [1, 188, 1024]> x_499_cast_fp16 = layer_norm(axes = x_499_axes_0, beta = encoder_module_layers_21_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_conv_weight_to_fp16, x = input_1139_cast_fp16)[name = string("x_499_cast_fp16")];
+            tensor<int32, [3]> input_1141_perm_0 = const()[name = string("input_1141_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1143_pad_type_0 = const()[name = string("input_1143_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1143_strides_0 = const()[name = string("input_1143_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1143_pad_0 = const()[name = string("input_1143_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1143_dilations_0 = const()[name = string("input_1143_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1143_groups_0 = const()[name = string("input_1143_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534458304))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(536559680))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(536555520))))[name = string("encoder_module_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(536561792)))];
+            tensor<fp16, [1, 1024, 188]> input_1141_cast_fp16 = transpose(perm = input_1141_perm_0, x = x_499_cast_fp16)[name = string("transpose_160")];
+            tensor<fp16, [1, 2048, 188]> input_1143_cast_fp16 = conv(bias = encoder_module_layers_21_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_21_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1141_cast_fp16)[name = string("input_1143_cast_fp16")];
+            int32 x_501_split_num_splits_0 = const()[name = string("x_501_split_num_splits_0"), val = int32(2)];
+            int32 x_501_split_axis_0 = const()[name = string("x_501_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_501_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_501_split_cast_fp16_1 = split(axis = x_501_split_axis_0, num_splits = x_501_split_num_splits_0, x = input_1143_cast_fp16)[name = string("x_501_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_501_split_1_sigmoid_cast_fp16 = sigmoid(x = x_501_split_cast_fp16_1)[name = string("x_501_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_501_cast_fp16 = mul(x = x_501_split_cast_fp16_0, y = x_501_split_1_sigmoid_cast_fp16)[name = string("x_501_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1145_cast_fp16 = select(a = var_164_to_fp16, b = x_501_cast_fp16, cond = var_608)[name = string("input_1145_cast_fp16")];
+            tensor<int32, [6]> input_1147_pad_0 = const()[name = string("input_1147_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1147_mode_0 = const()[name = string("input_1147_mode_0"), val = string("constant")];
+            fp16 const_301_to_fp16 = const()[name = string("const_301_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1147_cast_fp16 = pad(constant_val = const_301_to_fp16, mode = input_1147_mode_0, pad = input_1147_pad_0, x = input_1145_cast_fp16)[name = string("input_1147_cast_fp16")];
+            string input_1149_pad_type_0 = const()[name = string("input_1149_pad_type_0"), val = string("valid")];
+            int32 input_1149_groups_0 = const()[name = string("input_1149_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1149_strides_0 = const()[name = string("input_1149_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1149_pad_0 = const()[name = string("input_1149_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1149_dilations_0 = const()[name = string("input_1149_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_364_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(536565952))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(536577344))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(536575232))))[name = string("const_364_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_365_to_fp16 = const()[name = string("const_365_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(536578432)))];
+            tensor<fp16, [1, 1024, 188]> input_1151_cast_fp16 = conv(bias = const_365_to_fp16, dilations = input_1149_dilations_0, groups = input_1149_groups_0, pad = input_1149_pad_0, pad_type = input_1149_pad_type_0, strides = input_1149_strides_0, weight = const_364_to_fp16_quantized, x = input_1147_cast_fp16)[name = string("input_1151_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1153_cast_fp16 = silu(x = input_1151_cast_fp16)[name = string("input_1153_cast_fp16")];
+            string x_503_pad_type_0 = const()[name = string("x_503_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_503_strides_0 = const()[name = string("x_503_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_503_pad_0 = const()[name = string("x_503_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_503_dilations_0 = const()[name = string("x_503_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_503_groups_0 = const()[name = string("x_503_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(536580544))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537631296))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537629184))))[name = string("encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537632384)))];
+            tensor<fp16, [1, 1024, 188]> x_503_cast_fp16 = conv(bias = encoder_module_layers_21_conv_pointwise_conv2_bias_to_fp16, dilations = x_503_dilations_0, groups = x_503_groups_0, pad = x_503_pad_0, pad_type = x_503_pad_type_0, strides = x_503_strides_0, weight = encoder_module_layers_21_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1153_cast_fp16)[name = string("x_503_cast_fp16")];
+            tensor<int32, [3]> input_1155_perm_0 = const()[name = string("input_1155_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1155_cast_fp16 = transpose(perm = input_1155_perm_0, x = x_503_cast_fp16)[name = string("transpose_159")];
+            tensor<fp16, [1, 188, 1024]> input_1157_cast_fp16 = add(x = input_1139_cast_fp16, y = input_1155_cast_fp16)[name = string("input_1157_cast_fp16")];
+            tensor<int32, [1]> input_1159_axes_0 = const()[name = string("input_1159_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537634496)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537636608)))];
+            tensor<fp16, [1, 188, 1024]> input_1159_cast_fp16 = layer_norm(axes = input_1159_axes_0, beta = encoder_module_layers_21_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_feed_forward2_weight_to_fp16, x = input_1157_cast_fp16)[name = string("input_1159_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537638720))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(541841344))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(541833088))))[name = string("encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(541845504)))];
+            tensor<fp16, [1, 188, 4096]> linear_197_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_21_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1159_cast_fp16)[name = string("linear_197_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1163_cast_fp16 = silu(x = linear_197_cast_fp16)[name = string("input_1163_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(541853760))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(546050240))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(546048128))))[name = string("encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(546051328)))];
+            tensor<fp16, [1, 188, 1024]> linear_198_cast_fp16 = linear(bias = encoder_module_layers_21_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_21_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1163_cast_fp16)[name = string("linear_198_cast_fp16")];
+            fp16 var_4304_to_fp16 = const()[name = string("op_4304_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4305_cast_fp16 = mul(x = linear_198_cast_fp16, y = var_4304_to_fp16)[name = string("op_4305_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1169_cast_fp16 = add(x = input_1157_cast_fp16, y = var_4305_cast_fp16)[name = string("input_1169_cast_fp16")];
+            tensor<int32, [1]> input_1171_axes_0 = const()[name = string("input_1171_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_21_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(546053440)))];
+            tensor<fp16, [1024]> encoder_module_layers_21_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_21_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(546055552)))];
+            tensor<fp16, [1, 188, 1024]> input_1171_cast_fp16 = layer_norm(axes = input_1171_axes_0, beta = encoder_module_layers_21_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_21_norm_out_weight_to_fp16, x = input_1169_cast_fp16)[name = string("input_1171_cast_fp16")];
+            tensor<int32, [1]> input_1173_axes_0 = const()[name = string("input_1173_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(546057664)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(546059776)))];
+            tensor<fp16, [1, 188, 1024]> input_1173_cast_fp16 = layer_norm(axes = input_1173_axes_0, beta = encoder_module_layers_22_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_feed_forward1_weight_to_fp16, x = input_1171_cast_fp16)[name = string("input_1173_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(546061888))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(550264512))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(550256256))))[name = string("encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(550268672)))];
+            tensor<fp16, [1, 188, 4096]> linear_199_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_22_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1173_cast_fp16)[name = string("linear_199_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1177_cast_fp16 = silu(x = linear_199_cast_fp16)[name = string("input_1177_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(550276928))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(554473408))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(554471296))))[name = string("encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(554474496)))];
+            tensor<fp16, [1, 188, 1024]> linear_200_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_22_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1177_cast_fp16)[name = string("linear_200_cast_fp16")];
+            fp16 var_4335_to_fp16 = const()[name = string("op_4335_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4336_cast_fp16 = mul(x = linear_200_cast_fp16, y = var_4335_to_fp16)[name = string("op_4336_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1183_cast_fp16 = add(x = input_1171_cast_fp16, y = var_4336_cast_fp16)[name = string("input_1183_cast_fp16")];
+            tensor<int32, [1]> query_45_axes_0 = const()[name = string("query_45_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(554476608)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(554478720)))];
+            tensor<fp16, [1, 188, 1024]> query_45_cast_fp16 = layer_norm(axes = query_45_axes_0, beta = encoder_module_layers_22_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_self_att_weight_to_fp16, x = input_1183_cast_fp16)[name = string("query_45_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(554480832))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555531584))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555529472))))[name = string("encoder_module_layers_22_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555532672)))];
+            tensor<fp16, [1, 188, 1024]> linear_201_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_q_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = string("linear_201_cast_fp16")];
+            tensor<int32, [4]> var_4353 = const()[name = string("op_4353"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_133_cast_fp16 = reshape(shape = var_4353, x = linear_201_cast_fp16)[name = string("q_133_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(555534784))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(556585536))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(556583424))))[name = string("encoder_module_layers_22_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(556586624)))];
+            tensor<fp16, [1, 188, 1024]> linear_202_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_k_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = string("linear_202_cast_fp16")];
+            tensor<int32, [4]> var_4358 = const()[name = string("op_4358"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_89_cast_fp16 = reshape(shape = var_4358, x = linear_202_cast_fp16)[name = string("k_89_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(556588736))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557639488))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557637376))))[name = string("encoder_module_layers_22_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557640576)))];
+            tensor<fp16, [1, 188, 1024]> linear_203_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_v_weight_to_fp16_quantized, x = query_45_cast_fp16)[name = string("linear_203_cast_fp16")];
+            tensor<int32, [4]> var_4363 = const()[name = string("op_4363"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_45_cast_fp16 = reshape(shape = var_4363, x = linear_203_cast_fp16)[name = string("v_45_cast_fp16")];
+            tensor<int32, [4]> value_49_perm_0 = const()[name = string("value_49_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_22_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557642688)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4375_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_module_layers_22_self_attn_pos_bias_u_to_fp16)[name = string("op_4375_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_22_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557644800)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4377_cast_fp16 = add(x = q_133_cast_fp16, y = encoder_module_layers_22_self_attn_pos_bias_v_to_fp16)[name = string("op_4377_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_45_perm_0 = const()[name = string("q_with_bias_v_45_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            bool x_511_transpose_x_0 = const()[name = string("x_511_transpose_x_0"), val = bool(false)];
+            bool x_511_transpose_y_0 = const()[name = string("x_511_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 128, 375]> op_4379_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557646912))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(558031808))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(558030976))))[name = string("op_4379_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_45_cast_fp16 = transpose(perm = q_with_bias_v_45_perm_0, x = var_4377_cast_fp16)[name = string("transpose_158")];
+            tensor<fp16, [1, 8, 188, 375]> x_511_cast_fp16 = matmul(transpose_x = x_511_transpose_x_0, transpose_y = x_511_transpose_y_0, x = q_with_bias_v_45_cast_fp16, y = op_4379_to_fp16_quantized)[name = string("x_511_cast_fp16")];
+            tensor<int32, [8]> x_513_pad_0 = const()[name = string("x_513_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_513_mode_0 = const()[name = string("x_513_mode_0"), val = string("constant")];
+            fp16 const_308_to_fp16 = const()[name = string("const_308_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_513_cast_fp16 = pad(constant_val = const_308_to_fp16, mode = x_513_mode_0, pad = x_513_pad_0, x = x_511_cast_fp16)[name = string("x_513_cast_fp16")];
+            tensor<int32, [4]> var_4387 = const()[name = string("op_4387"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_515_cast_fp16 = reshape(shape = var_4387, x = x_513_cast_fp16)[name = string("x_515_cast_fp16")];
+            tensor<int32, [4]> var_4391_begin_0 = const()[name = string("op_4391_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4391_end_0 = const()[name = string("op_4391_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4391_end_mask_0 = const()[name = string("op_4391_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4391_cast_fp16 = slice_by_index(begin = var_4391_begin_0, end = var_4391_end_0, end_mask = var_4391_end_mask_0, x = x_515_cast_fp16)[name = string("op_4391_cast_fp16")];
+            tensor<int32, [4]> var_4392 = const()[name = string("op_4392"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_89_cast_fp16 = reshape(shape = var_4392, x = var_4391_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<int32, [4]> transpose_140_perm_0 = const()[name = string("transpose_140_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_141_perm_0 = const()[name = string("transpose_141_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_141 = transpose(perm = transpose_141_perm_0, x = k_89_cast_fp16)[name = string("transpose_156")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_140 = transpose(perm = transpose_140_perm_0, x = var_4375_cast_fp16)[name = string("transpose_157")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_91_begin_0 = const()[name = string("matrix_bd_91_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_91_end_0 = const()[name = string("matrix_bd_91_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_91_end_mask_0 = const()[name = string("matrix_bd_91_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4401_cast_fp16 = add(x = matrix_ac_45_cast_fp16, y = matrix_bd_91_cast_fp16)[name = string("op_4401_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<fp16, [1, 8, 188, 188]> _inversed_scores_89_cast_fp16 = mul(x = var_4401_cast_fp16, y = _inversed_scores_89_y_0_to_fp16)[name = string("_inversed_scores_89_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_91_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_89_cast_fp16, cond = mask_15)[name = string("scores_91_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4407_cast_fp16 = softmax(axis = var_152, x = scores_91_cast_fp16)[name = string("op_4407_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1185_cast_fp16 = select(a = var_164_to_fp16, b = var_4407_cast_fp16, cond = mask_15)[name = string("input_1185_cast_fp16")];
+            bool x_517_transpose_x_0 = const()[name = string("x_517_transpose_x_0"), val = bool(false)];
+            bool x_517_transpose_y_0 = const()[name = string("x_517_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_49_cast_fp16 = transpose(perm = value_49_perm_0, x = v_45_cast_fp16)[name = string("transpose_155")];
+            tensor<fp16, [1, 8, 188, 128]> x_517_cast_fp16 = matmul(transpose_x = x_517_transpose_x_0, transpose_y = x_517_transpose_y_0, x = input_1185_cast_fp16, y = value_49_cast_fp16)[name = string("x_517_cast_fp16")];
+            tensor<int32, [4]> var_4411_perm_0 = const()[name = string("op_4411_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4412 = const()[name = string("op_4412"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4411_cast_fp16 = transpose(perm = var_4411_perm_0, x = x_517_cast_fp16)[name = string("transpose_154")];
+            tensor<fp16, [1, 188, 1024]> input_1187_cast_fp16 = reshape(shape = var_4412, x = var_4411_cast_fp16)[name = string("input_1187_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_22_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(558032256))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(559083008))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(559080896))))[name = string("encoder_module_layers_22_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_22_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(559084096)))];
+            tensor<fp16, [1, 188, 1024]> linear_205_cast_fp16 = linear(bias = encoder_module_layers_22_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_22_self_attn_linear_out_weight_to_fp16_quantized, x = input_1187_cast_fp16)[name = string("linear_205_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1191_cast_fp16 = add(x = input_1183_cast_fp16, y = linear_205_cast_fp16)[name = string("input_1191_cast_fp16")];
+            tensor<int32, [1]> x_521_axes_0 = const()[name = string("x_521_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(559086208)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(559088320)))];
+            tensor<fp16, [1, 188, 1024]> x_521_cast_fp16 = layer_norm(axes = x_521_axes_0, beta = encoder_module_layers_22_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_conv_weight_to_fp16, x = input_1191_cast_fp16)[name = string("x_521_cast_fp16")];
+            tensor<int32, [3]> input_1193_perm_0 = const()[name = string("input_1193_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1195_pad_type_0 = const()[name = string("input_1195_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1195_strides_0 = const()[name = string("input_1195_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1195_pad_0 = const()[name = string("input_1195_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1195_dilations_0 = const()[name = string("input_1195_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1195_groups_0 = const()[name = string("input_1195_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(559090432))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(561191808))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(561187648))))[name = string("encoder_module_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(561193920)))];
+            tensor<fp16, [1, 1024, 188]> input_1193_cast_fp16 = transpose(perm = input_1193_perm_0, x = x_521_cast_fp16)[name = string("transpose_153")];
+            tensor<fp16, [1, 2048, 188]> input_1195_cast_fp16 = conv(bias = encoder_module_layers_22_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_22_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1193_cast_fp16)[name = string("input_1195_cast_fp16")];
+            int32 x_523_split_num_splits_0 = const()[name = string("x_523_split_num_splits_0"), val = int32(2)];
+            int32 x_523_split_axis_0 = const()[name = string("x_523_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_523_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_523_split_cast_fp16_1 = split(axis = x_523_split_axis_0, num_splits = x_523_split_num_splits_0, x = input_1195_cast_fp16)[name = string("x_523_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_523_split_1_sigmoid_cast_fp16 = sigmoid(x = x_523_split_cast_fp16_1)[name = string("x_523_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_523_cast_fp16 = mul(x = x_523_split_cast_fp16_0, y = x_523_split_1_sigmoid_cast_fp16)[name = string("x_523_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1197_cast_fp16 = select(a = var_164_to_fp16, b = x_523_cast_fp16, cond = var_608)[name = string("input_1197_cast_fp16")];
+            tensor<int32, [6]> input_1199_pad_0 = const()[name = string("input_1199_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1199_mode_0 = const()[name = string("input_1199_mode_0"), val = string("constant")];
+            fp16 const_311_to_fp16 = const()[name = string("const_311_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1199_cast_fp16 = pad(constant_val = const_311_to_fp16, mode = input_1199_mode_0, pad = input_1199_pad_0, x = input_1197_cast_fp16)[name = string("input_1199_cast_fp16")];
+            string input_1201_pad_type_0 = const()[name = string("input_1201_pad_type_0"), val = string("valid")];
+            int32 input_1201_groups_0 = const()[name = string("input_1201_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1201_strides_0 = const()[name = string("input_1201_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1201_pad_0 = const()[name = string("input_1201_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1201_dilations_0 = const()[name = string("input_1201_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_366_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(561198080))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(561209472))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(561207360))))[name = string("const_366_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_367_to_fp16 = const()[name = string("const_367_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(561210560)))];
+            tensor<fp16, [1, 1024, 188]> input_1203_cast_fp16 = conv(bias = const_367_to_fp16, dilations = input_1201_dilations_0, groups = input_1201_groups_0, pad = input_1201_pad_0, pad_type = input_1201_pad_type_0, strides = input_1201_strides_0, weight = const_366_to_fp16_quantized, x = input_1199_cast_fp16)[name = string("input_1203_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1205_cast_fp16 = silu(x = input_1203_cast_fp16)[name = string("input_1205_cast_fp16")];
+            string x_525_pad_type_0 = const()[name = string("x_525_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_525_strides_0 = const()[name = string("x_525_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_525_pad_0 = const()[name = string("x_525_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_525_dilations_0 = const()[name = string("x_525_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_525_groups_0 = const()[name = string("x_525_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(561212672))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562263424))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562261312))))[name = string("encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562264512)))];
+            tensor<fp16, [1, 1024, 188]> x_525_cast_fp16 = conv(bias = encoder_module_layers_22_conv_pointwise_conv2_bias_to_fp16, dilations = x_525_dilations_0, groups = x_525_groups_0, pad = x_525_pad_0, pad_type = x_525_pad_type_0, strides = x_525_strides_0, weight = encoder_module_layers_22_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1205_cast_fp16)[name = string("x_525_cast_fp16")];
+            tensor<int32, [3]> input_1207_perm_0 = const()[name = string("input_1207_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1207_cast_fp16 = transpose(perm = input_1207_perm_0, x = x_525_cast_fp16)[name = string("transpose_152")];
+            tensor<fp16, [1, 188, 1024]> input_1209_cast_fp16 = add(x = input_1191_cast_fp16, y = input_1207_cast_fp16)[name = string("input_1209_cast_fp16")];
+            tensor<int32, [1]> input_1211_axes_0 = const()[name = string("input_1211_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562266624)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562268736)))];
+            tensor<fp16, [1, 188, 1024]> input_1211_cast_fp16 = layer_norm(axes = input_1211_axes_0, beta = encoder_module_layers_22_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_feed_forward2_weight_to_fp16, x = input_1209_cast_fp16)[name = string("input_1211_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562270848))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566473472))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566465216))))[name = string("encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566477632)))];
+            tensor<fp16, [1, 188, 4096]> linear_206_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_22_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1211_cast_fp16)[name = string("linear_206_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1215_cast_fp16 = silu(x = linear_206_cast_fp16)[name = string("input_1215_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566485888))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570682368))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570680256))))[name = string("encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570683456)))];
+            tensor<fp16, [1, 188, 1024]> linear_207_cast_fp16 = linear(bias = encoder_module_layers_22_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_22_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1215_cast_fp16)[name = string("linear_207_cast_fp16")];
+            fp16 var_4478_to_fp16 = const()[name = string("op_4478_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4479_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_4478_to_fp16)[name = string("op_4479_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1221_cast_fp16 = add(x = input_1209_cast_fp16, y = var_4479_cast_fp16)[name = string("input_1221_cast_fp16")];
+            tensor<int32, [1]> input_1223_axes_0 = const()[name = string("input_1223_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_22_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570685568)))];
+            tensor<fp16, [1024]> encoder_module_layers_22_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_22_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570687680)))];
+            tensor<fp16, [1, 188, 1024]> input_1223_cast_fp16 = layer_norm(axes = input_1223_axes_0, beta = encoder_module_layers_22_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_22_norm_out_weight_to_fp16, x = input_1221_cast_fp16)[name = string("input_1223_cast_fp16")];
+            tensor<int32, [1]> input_1225_axes_0 = const()[name = string("input_1225_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward1_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward1_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570689792)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570691904)))];
+            tensor<fp16, [1, 188, 1024]> input_1225_cast_fp16 = layer_norm(axes = input_1225_axes_0, beta = encoder_module_layers_23_norm_feed_forward1_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_feed_forward1_weight_to_fp16, x = input_1223_cast_fp16)[name = string("input_1225_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570694016))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(574896640))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(574888384))))[name = string("encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(574900800)))];
+            tensor<fp16, [1, 188, 4096]> linear_208_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward1_linear1_bias_to_fp16, weight = encoder_module_layers_23_feed_forward1_linear1_weight_to_fp16_quantized, x = input_1225_cast_fp16)[name = string("linear_208_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1229_cast_fp16 = silu(x = linear_208_cast_fp16)[name = string("input_1229_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(574909056))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(579105536))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(579103424))))[name = string("encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(579106624)))];
+            tensor<fp16, [1, 188, 1024]> linear_209_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward1_linear2_bias_to_fp16, weight = encoder_module_layers_23_feed_forward1_linear2_weight_to_fp16_quantized, x = input_1229_cast_fp16)[name = string("linear_209_cast_fp16")];
+            fp16 var_4509_to_fp16 = const()[name = string("op_4509_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4510_cast_fp16 = mul(x = linear_209_cast_fp16, y = var_4509_to_fp16)[name = string("op_4510_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1235_cast_fp16 = add(x = input_1223_cast_fp16, y = var_4510_cast_fp16)[name = string("input_1235_cast_fp16")];
+            tensor<int32, [1]> query_axes_0 = const()[name = string("query_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_self_att_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_self_att_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(579108736)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_self_att_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_self_att_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(579110848)))];
+            tensor<fp16, [1, 188, 1024]> query_cast_fp16 = layer_norm(axes = query_axes_0, beta = encoder_module_layers_23_norm_self_att_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_self_att_weight_to_fp16, x = input_1235_cast_fp16)[name = string("query_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_q_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(579112960))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(580163712))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(580161600))))[name = string("encoder_module_layers_23_self_attn_linear_q_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_q_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_q_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(580164800)))];
+            tensor<fp16, [1, 188, 1024]> linear_210_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_q_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_q_weight_to_fp16_quantized, x = query_cast_fp16)[name = string("linear_210_cast_fp16")];
+            tensor<int32, [4]> var_4527 = const()[name = string("op_4527"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> q_139_cast_fp16 = reshape(shape = var_4527, x = linear_210_cast_fp16)[name = string("q_139_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_k_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(580166912))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(581217664))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(581215552))))[name = string("encoder_module_layers_23_self_attn_linear_k_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_k_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_k_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(581218752)))];
+            tensor<fp16, [1, 188, 1024]> linear_211_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_k_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_k_weight_to_fp16_quantized, x = query_cast_fp16)[name = string("linear_211_cast_fp16")];
+            tensor<int32, [4]> var_4532 = const()[name = string("op_4532"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> k_93_cast_fp16 = reshape(shape = var_4532, x = linear_211_cast_fp16)[name = string("k_93_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_v_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(581220864))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(582271616))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(582269504))))[name = string("encoder_module_layers_23_self_attn_linear_v_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_v_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_v_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(582272704)))];
+            tensor<fp16, [1, 188, 1024]> linear_212_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_v_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_v_weight_to_fp16_quantized, x = query_cast_fp16)[name = string("linear_212_cast_fp16")];
+            tensor<int32, [4]> var_4537 = const()[name = string("op_4537"), val = tensor<int32, [4]>([1, -1, 8, 128])];
+            tensor<fp16, [1, 188, 8, 128]> v_cast_fp16 = reshape(shape = var_4537, x = linear_212_cast_fp16)[name = string("v_cast_fp16")];
+            tensor<int32, [4]> value_perm_0 = const()[name = string("value_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<fp16, [8, 128]> encoder_module_layers_23_self_attn_pos_bias_u_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_pos_bias_u_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(582274816)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4549_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_module_layers_23_self_attn_pos_bias_u_to_fp16)[name = string("op_4549_cast_fp16")];
+            tensor<fp16, [8, 128]> encoder_module_layers_23_self_attn_pos_bias_v_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_pos_bias_v_to_fp16"), val = tensor<fp16, [8, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(582276928)))];
+            tensor<fp16, [1, 188, 8, 128]> var_4551_cast_fp16 = add(x = q_139_cast_fp16, y = encoder_module_layers_23_self_attn_pos_bias_v_to_fp16)[name = string("op_4551_cast_fp16")];
+            tensor<int32, [4]> q_with_bias_v_perm_0 = const()[name = string("q_with_bias_v_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            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<fp16, [1, 8, 128, 375]> op_4553_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1, 8, 128, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(582279040))), offset = tensor<int8, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(582663936))), scale = tensor<fp16, [1, 1, 1, 375]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(582663104))))[name = string("op_4553_to_fp16_quantized")];
+            tensor<fp16, [1, 8, 188, 128]> q_with_bias_v_cast_fp16 = transpose(perm = q_with_bias_v_perm_0, x = var_4551_cast_fp16)[name = string("transpose_151")];
+            tensor<fp16, [1, 8, 188, 375]> x_533_cast_fp16 = matmul(transpose_x = x_533_transpose_x_0, transpose_y = x_533_transpose_y_0, x = q_with_bias_v_cast_fp16, y = op_4553_to_fp16_quantized)[name = string("x_533_cast_fp16")];
+            tensor<int32, [8]> x_535_pad_0 = const()[name = string("x_535_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 1, 0])];
+            string x_535_mode_0 = const()[name = string("x_535_mode_0"), val = string("constant")];
+            fp16 const_318_to_fp16 = const()[name = string("const_318_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 8, 188, 376]> x_535_cast_fp16 = pad(constant_val = const_318_to_fp16, mode = x_535_mode_0, pad = x_535_pad_0, x = x_533_cast_fp16)[name = string("x_535_cast_fp16")];
+            tensor<int32, [4]> var_4561 = const()[name = string("op_4561"), val = tensor<int32, [4]>([1, 8, -1, 188])];
+            tensor<fp16, [1, 8, 376, 188]> x_537_cast_fp16 = reshape(shape = var_4561, x = x_535_cast_fp16)[name = string("x_537_cast_fp16")];
+            tensor<int32, [4]> var_4565_begin_0 = const()[name = string("op_4565_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
+            tensor<int32, [4]> var_4565_end_0 = const()[name = string("op_4565_end_0"), val = tensor<int32, [4]>([1, 8, 376, 188])];
+            tensor<bool, [4]> var_4565_end_mask_0 = const()[name = string("op_4565_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 8, 375, 188]> var_4565_cast_fp16 = slice_by_index(begin = var_4565_begin_0, end = var_4565_end_0, end_mask = var_4565_end_mask_0, x = x_537_cast_fp16)[name = string("op_4565_cast_fp16")];
+            tensor<int32, [4]> var_4566 = const()[name = string("op_4566"), val = tensor<int32, [4]>([1, 8, 188, 375])];
+            tensor<fp16, [1, 8, 188, 375]> matrix_bd_93_cast_fp16 = reshape(shape = var_4566, x = var_4565_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<int32, [4]> transpose_142_perm_0 = const()[name = string("transpose_142_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_143_perm_0 = const()[name = string("transpose_143_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 8, 128, 188]> transpose_143 = transpose(perm = transpose_143_perm_0, x = k_93_cast_fp16)[name = string("transpose_149")];
+            tensor<fp16, [1, 8, 188, 128]> transpose_142 = transpose(perm = transpose_142_perm_0, x = var_4549_cast_fp16)[name = string("transpose_150")];
+            tensor<fp16, [1, 8, 188, 188]> 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<int32, [4]> matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> matrix_bd_end_0 = const()[name = string("matrix_bd_end_0"), val = tensor<int32, [4]>([1, 8, 188, 188])];
+            tensor<bool, [4]> matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 8, 188, 188]> 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<fp16, [1, 8, 188, 188]> var_4575_cast_fp16 = add(x = matrix_ac_cast_fp16, y = matrix_bd_cast_fp16)[name = string("op_4575_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<fp16, [1, 8, 188, 188]> _inversed_scores_93_cast_fp16 = mul(x = var_4575_cast_fp16, y = _inversed_scores_93_y_0_to_fp16)[name = string("_inversed_scores_93_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> scores_cast_fp16 = select(a = var_163_to_fp16, b = _inversed_scores_93_cast_fp16, cond = mask_15)[name = string("scores_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> var_4581_cast_fp16 = softmax(axis = var_152, x = scores_cast_fp16)[name = string("op_4581_cast_fp16")];
+            tensor<fp16, [1, 8, 188, 188]> input_1237_cast_fp16 = select(a = var_164_to_fp16, b = var_4581_cast_fp16, cond = mask_15)[name = string("input_1237_cast_fp16")];
+            bool x_539_transpose_x_0 = const()[name = string("x_539_transpose_x_0"), val = bool(false)];
+            bool x_539_transpose_y_0 = const()[name = string("x_539_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 8, 188, 128]> value_cast_fp16 = transpose(perm = value_perm_0, x = v_cast_fp16)[name = string("transpose_148")];
+            tensor<fp16, [1, 8, 188, 128]> x_539_cast_fp16 = matmul(transpose_x = x_539_transpose_x_0, transpose_y = x_539_transpose_y_0, x = input_1237_cast_fp16, y = value_cast_fp16)[name = string("x_539_cast_fp16")];
+            tensor<int32, [4]> var_4585_perm_0 = const()[name = string("op_4585_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_4586 = const()[name = string("op_4586"), val = tensor<int32, [3]>([1, -1, 1024])];
+            tensor<fp16, [1, 188, 8, 128]> var_4585_cast_fp16 = transpose(perm = var_4585_perm_0, x = x_539_cast_fp16)[name = string("transpose_147")];
+            tensor<fp16, [1, 188, 1024]> input_1239_cast_fp16 = reshape(shape = var_4586, x = var_4585_cast_fp16)[name = string("input_1239_cast_fp16")];
+            tensor<fp16, [1024, 1024]> encoder_module_layers_23_self_attn_linear_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(582664384))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583715136))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583713024))))[name = string("encoder_module_layers_23_self_attn_linear_out_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_self_attn_linear_out_bias_to_fp16 = const()[name = string("encoder_module_layers_23_self_attn_linear_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583716224)))];
+            tensor<fp16, [1, 188, 1024]> linear_214_cast_fp16 = linear(bias = encoder_module_layers_23_self_attn_linear_out_bias_to_fp16, weight = encoder_module_layers_23_self_attn_linear_out_weight_to_fp16_quantized, x = input_1239_cast_fp16)[name = string("linear_214_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_1243_cast_fp16 = add(x = input_1235_cast_fp16, y = linear_214_cast_fp16)[name = string("input_1243_cast_fp16")];
+            tensor<int32, [1]> x_543_axes_0 = const()[name = string("x_543_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_conv_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_conv_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583718336)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_conv_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583720448)))];
+            tensor<fp16, [1, 188, 1024]> x_543_cast_fp16 = layer_norm(axes = x_543_axes_0, beta = encoder_module_layers_23_norm_conv_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_conv_weight_to_fp16, x = input_1243_cast_fp16)[name = string("x_543_cast_fp16")];
+            tensor<int32, [3]> input_1245_perm_0 = const()[name = string("input_1245_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string input_1247_pad_type_0 = const()[name = string("input_1247_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_1247_strides_0 = const()[name = string("input_1247_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1247_pad_0 = const()[name = string("input_1247_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1247_dilations_0 = const()[name = string("input_1247_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_1247_groups_0 = const()[name = string("input_1247_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 1024, 1]> encoder_module_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583722560))), offset = tensor<int8, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(585823936))), scale = tensor<fp16, [2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(585819776))))[name = string("encoder_module_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized")];
+            tensor<fp16, [2048]> encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(585826048)))];
+            tensor<fp16, [1, 1024, 188]> input_1245_cast_fp16 = transpose(perm = input_1245_perm_0, x = x_543_cast_fp16)[name = string("transpose_146")];
+            tensor<fp16, [1, 2048, 188]> input_1247_cast_fp16 = conv(bias = encoder_module_layers_23_conv_pointwise_conv1_bias_to_fp16, 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_module_layers_23_conv_pointwise_conv1_weight_to_fp16_quantized, x = input_1245_cast_fp16)[name = string("input_1247_cast_fp16")];
+            int32 x_545_split_num_splits_0 = const()[name = string("x_545_split_num_splits_0"), val = int32(2)];
+            int32 x_545_split_axis_0 = const()[name = string("x_545_split_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 188]> x_545_split_cast_fp16_0, tensor<fp16, [1, 1024, 188]> x_545_split_cast_fp16_1 = split(axis = x_545_split_axis_0, num_splits = x_545_split_num_splits_0, x = input_1247_cast_fp16)[name = string("x_545_split_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_545_split_1_sigmoid_cast_fp16 = sigmoid(x = x_545_split_cast_fp16_1)[name = string("x_545_split_1_sigmoid_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> x_545_cast_fp16 = mul(x = x_545_split_cast_fp16_0, y = x_545_split_1_sigmoid_cast_fp16)[name = string("x_545_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1249_cast_fp16 = select(a = var_164_to_fp16, b = x_545_cast_fp16, cond = var_608)[name = string("input_1249_cast_fp16")];
+            tensor<int32, [6]> input_1251_pad_0 = const()[name = string("input_1251_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
+            string input_1251_mode_0 = const()[name = string("input_1251_mode_0"), val = string("constant")];
+            fp16 const_321_to_fp16 = const()[name = string("const_321_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 1024, 196]> input_1251_cast_fp16 = pad(constant_val = const_321_to_fp16, mode = input_1251_mode_0, pad = input_1251_pad_0, x = input_1249_cast_fp16)[name = string("input_1251_cast_fp16")];
+            string input_1253_pad_type_0 = const()[name = string("input_1253_pad_type_0"), val = string("valid")];
+            int32 input_1253_groups_0 = const()[name = string("input_1253_groups_0"), val = int32(1024)];
+            tensor<int32, [1]> input_1253_strides_0 = const()[name = string("input_1253_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_1253_pad_0 = const()[name = string("input_1253_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_1253_dilations_0 = const()[name = string("input_1253_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1024, 1, 9]> const_368_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1, 9]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(585830208))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(585841600))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(585839488))))[name = string("const_368_to_fp16_quantized")];
+            tensor<fp16, [1024]> const_369_to_fp16 = const()[name = string("const_369_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(585842688)))];
+            tensor<fp16, [1, 1024, 188]> input_1255_cast_fp16 = conv(bias = const_369_to_fp16, dilations = input_1253_dilations_0, groups = input_1253_groups_0, pad = input_1253_pad_0, pad_type = input_1253_pad_type_0, strides = input_1253_strides_0, weight = const_368_to_fp16_quantized, x = input_1251_cast_fp16)[name = string("input_1255_cast_fp16")];
+            tensor<fp16, [1, 1024, 188]> input_1257_cast_fp16 = silu(x = input_1255_cast_fp16)[name = string("input_1257_cast_fp16")];
+            string x_547_pad_type_0 = const()[name = string("x_547_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_547_strides_0 = const()[name = string("x_547_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_547_pad_0 = const()[name = string("x_547_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_547_dilations_0 = const()[name = string("x_547_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_547_groups_0 = const()[name = string("x_547_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 1]> encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(585844800))), offset = tensor<int8, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(586895552))), scale = tensor<fp16, [1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(586893440))))[name = string("encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(586896640)))];
+            tensor<fp16, [1, 1024, 188]> x_547_cast_fp16 = conv(bias = encoder_module_layers_23_conv_pointwise_conv2_bias_to_fp16, dilations = x_547_dilations_0, groups = x_547_groups_0, pad = x_547_pad_0, pad_type = x_547_pad_type_0, strides = x_547_strides_0, weight = encoder_module_layers_23_conv_pointwise_conv2_weight_to_fp16_quantized, x = input_1257_cast_fp16)[name = string("x_547_cast_fp16")];
+            tensor<int32, [3]> input_1259_perm_0 = const()[name = string("input_1259_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 188, 1024]> input_1259_cast_fp16 = transpose(perm = input_1259_perm_0, x = x_547_cast_fp16)[name = string("transpose_145")];
+            tensor<fp16, [1, 188, 1024]> input_1261_cast_fp16 = add(x = input_1243_cast_fp16, y = input_1259_cast_fp16)[name = string("input_1261_cast_fp16")];
+            tensor<int32, [1]> input_1263_axes_0 = const()[name = string("input_1263_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward2_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward2_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(586898752)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_feed_forward2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_feed_forward2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(586900864)))];
+            tensor<fp16, [1, 188, 1024]> input_1263_cast_fp16 = layer_norm(axes = input_1263_axes_0, beta = encoder_module_layers_23_norm_feed_forward2_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_feed_forward2_weight_to_fp16, x = input_1261_cast_fp16)[name = string("input_1263_cast_fp16")];
+            tensor<fp16, [4096, 1024]> encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(586902976))), offset = tensor<int8, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(591105600))), scale = tensor<fp16, [4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(591097344))))[name = string("encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16_quantized")];
+            tensor<fp16, [4096]> encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(591109760)))];
+            tensor<fp16, [1, 188, 4096]> linear_215_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward2_linear1_bias_to_fp16, weight = encoder_module_layers_23_feed_forward2_linear1_weight_to_fp16_quantized, x = input_1263_cast_fp16)[name = string("linear_215_cast_fp16")];
+            tensor<fp16, [1, 188, 4096]> input_1267_cast_fp16 = silu(x = linear_215_cast_fp16)[name = string("input_1267_cast_fp16")];
+            tensor<fp16, [1024, 4096]> encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(591118016))), offset = tensor<int8, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595314496))), scale = tensor<fp16, [1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595312384))))[name = string("encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16_quantized")];
+            tensor<fp16, [1024]> encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16 = const()[name = string("encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595315584)))];
+            tensor<fp16, [1, 188, 1024]> linear_216_cast_fp16 = linear(bias = encoder_module_layers_23_feed_forward2_linear2_bias_to_fp16, weight = encoder_module_layers_23_feed_forward2_linear2_weight_to_fp16_quantized, x = input_1267_cast_fp16)[name = string("linear_216_cast_fp16")];
+            fp16 var_4652_to_fp16 = const()[name = string("op_4652_to_fp16"), val = fp16(0x1p-1)];
+            tensor<fp16, [1, 188, 1024]> var_4653_cast_fp16 = mul(x = linear_216_cast_fp16, y = var_4652_to_fp16)[name = string("op_4653_cast_fp16")];
+            tensor<fp16, [1, 188, 1024]> input_cast_fp16 = add(x = input_1261_cast_fp16, y = var_4653_cast_fp16)[name = string("input_cast_fp16")];
+            tensor<int32, [1]> audio_signal_axes_0 = const()[name = string("audio_signal_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_out_weight_to_fp16 = const()[name = string("encoder_module_layers_23_norm_out_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595317696)))];
+            tensor<fp16, [1024]> encoder_module_layers_23_norm_out_bias_to_fp16 = const()[name = string("encoder_module_layers_23_norm_out_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595319808)))];
+            tensor<fp16, [1, 188, 1024]> audio_signal_cast_fp16 = layer_norm(axes = audio_signal_axes_0, beta = encoder_module_layers_23_norm_out_bias_to_fp16, epsilon = var_166_to_fp16, gamma = encoder_module_layers_23_norm_out_weight_to_fp16, x = input_cast_fp16)[name = string("audio_signal_cast_fp16")];
+            tensor<int32, [3]> obj_3_perm_0 = const()[name = string("obj_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = string("obj_3_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp16, [1, 1024, 188]> obj_3_cast_fp16 = transpose(perm = obj_3_perm_0, x = audio_signal_cast_fp16)[name = string("transpose_144")];
+            tensor<fp32, [1, 1024, 188]> encoder = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = string("cast_0")];
+        } -> (encoder, encoder_length);
+}
\ No newline at end of file
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new file mode 100644
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+++ b/compiled/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlmodelc/weights/weight.bin
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new file mode 100644
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+++ b/parakeet_ctc_coreml/metadata.json
@@ -0,0 +1,58 @@
+{
+  "model_id": "nvidia/parakeet-ctc-0.6b",
+  "sample_rate": 16000,
+  "max_audio_seconds": 15.0,
+  "max_audio_samples": 240000,
+  "vocab_size": 1024,
+  "blank_id": 1024,
+  "subsampling_factor": 8,
+  "checkpoint": {
+    "type": "file",
+    "path": "/Users/tigregotico/parakeet-ctc-0.6b/parakeet-ctc-0.6b.nemo"
+  },
+  "coreml": {
+    "compute_units": "CPU_AND_NE",
+    "compute_precision": "FLOAT32"
+  },
+  "components": {
+    "mel_encoder": {
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+        "audio_signal": [
+          1,
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+        ],
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+          1
+        ]
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+        "encoder_length": [
+          1
+        ],
+        "encoder": [
+          1,
+          1024,
+          188
+        ]
+      },
+      "path": "parakeet_ctc_mel_encoder.mlpackage"
+    },
+    "ctc_decoder": {
+      "inputs": {
+        "encoder": [
+          1,
+          1024,
+          188
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+      },
+      "outputs": {
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+          1,
+          188,
+          1025
+        ]
+      },
+      "path": "parakeet_ctc_decoder.mlpackage"
+    }
+  }
+}
\ No newline at end of file
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@@ -0,0 +1,3 @@
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new file mode 100644
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@@ -0,0 +1,18 @@
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+            "path": "com.apple.CoreML/weights"
+        },
+        "AF4F2660-A2EF-4055-95B0-C4D9A938944E": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "AF4F2660-A2EF-4055-95B0-C4D9A938944E"
+}
diff --git a/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlpackage/Data/com.apple.CoreML/model.mlmodel b/parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlpackage/Data/com.apple.CoreML/model.mlmodel
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new file mode 100644
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@@ -0,0 +1,18 @@
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+            "path": "com.apple.CoreML/model.mlmodel"
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+            "author": "com.apple.CoreML",
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+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "0DACBB7E-DF8B-4CE0-A04F-6D8AB585FC08"
+}
diff --git a/parakeet_ctc_coreml_quantized/4bit_palettize/metadata.json b/parakeet_ctc_coreml_quantized/4bit_palettize/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..20cde1a91f59e66cdccbe303113464caf4f9c2a2
--- /dev/null
+++ b/parakeet_ctc_coreml_quantized/4bit_palettize/metadata.json
@@ -0,0 +1,58 @@
+{
+  "model_id": "nvidia/parakeet-ctc-0.6b",
+  "sample_rate": 16000,
+  "max_audio_seconds": 15.0,
+  "max_audio_samples": 240000,
+  "vocab_size": 1024,
+  "blank_id": 1024,
+  "subsampling_factor": 8,
+  "checkpoint": {
+    "type": "file",
+    "path": "/Users/tigregotico/parakeet-ctc-0.6b/parakeet-ctc-0.6b.nemo"
+  },
+  "coreml": {
+    "compute_units": "CPU_AND_NE",
+    "compute_precision": "FLOAT32"
+  },
+  "components": {
+    "mel_encoder": {
+      "inputs": {
+        "audio_signal": [
+          1,
+          240000
+        ],
+        "audio_length": [
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+      "outputs": {
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+          1
+        ],
+        "encoder": [
+          1,
+          1024,
+          188
+        ]
+      },
+      "path": "parakeet_ctc_mel_encoder.mlpackage"
+    },
+    "ctc_decoder": {
+      "inputs": {
+        "encoder": [
+          1,
+          1024,
+          188
+        ]
+      },
+      "outputs": {
+        "log_probs": [
+          1,
+          188,
+          1025
+        ]
+      },
+      "path": "parakeet_ctc_decoder.mlpackage"
+    }
+  }
+}
\ No newline at end of file
diff --git a/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel b/parakeet_ctc_coreml_quantized/4bit_palettize/parakeet_ctc_decoder.mlpackage/Data/com.apple.CoreML/model.mlmodel
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@@ -0,0 +1,18 @@
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+            "path": "com.apple.CoreML/model.mlmodel"
+        }
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+++ b/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlpackage/Data/com.apple.CoreML/model.mlmodel
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+version https://git-lfs.github.com/spec/v1
+oid sha256:faa0945e366a783fa10e14b8e2d7c24cb35362b2defc0c613dd7c0989ec27a9a
+size 788351
diff --git a/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin
new file mode 100644
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--- /dev/null
+++ b/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d93fd59e9f558fa8127254aafdf5c68927ddb3b42ad7d4cdc7a3e330faea9f8
+size 595321920
diff --git a/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlpackage/Manifest.json b/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlpackage/Manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..92ab1dc2a6e069de6c0f136f355ef0cc6bb44109
--- /dev/null
+++ b/parakeet_ctc_coreml_quantized/int8_linear/parakeet_ctc_mel_encoder.mlpackage/Manifest.json
@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "1679B125-7FD5-4F10-8806-B30D05F5DDAB": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        },
+        "22E2523F-4309-4F51-A03E-53D63AC0EBEC": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "1679B125-7FD5-4F10-8806-B30D05F5DDAB"
+}
diff --git a/parakeet_ctc_coreml_quantized/quantization_summary.json b/parakeet_ctc_coreml_quantized/quantization_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..706698d3b44b2ac23e76fbd1d3d60f2fecb309bf
--- /dev/null
+++ b/parakeet_ctc_coreml_quantized/quantization_summary.json
@@ -0,0 +1,50 @@
+{
+  "int8_linear": {
+    "mel_encoder": {
+      "size_mb": 568.5,
+      "baseline_mb": 1131.11,
+      "compression_ratio": 1.99
+    },
+    "ctc_decoder": {
+      "size_mb": 1.01,
+      "baseline_mb": 2.01,
+      "compression_ratio": 1.99
+    }
+  },
+  "int4_linear": {
+    "mel_encoder": {
+      "size_mb": 320.78,
+      "baseline_mb": 1131.11,
+      "compression_ratio": 3.53
+    },
+    "ctc_decoder": {
+      "size_mb": 0.57,
+      "baseline_mb": 2.01,
+      "compression_ratio": 3.53
+    }
+  },
+  "6bit_palettize": {
+    "mel_encoder": {
+      "size_mb": 426.19,
+      "baseline_mb": 1131.11,
+      "compression_ratio": 2.65
+    },
+    "ctc_decoder": {
+      "size_mb": 0.76,
+      "baseline_mb": 2.01,
+      "compression_ratio": 2.65
+    }
+  },
+  "4bit_palettize": {
+    "mel_encoder": {
+      "size_mb": 285.17,
+      "baseline_mb": 1131.11,
+      "compression_ratio": 3.97
+    },
+    "ctc_decoder": {
+      "size_mb": 0.51,
+      "baseline_mb": 2.01,
+      "compression_ratio": 3.96
+    }
+  }
+}
\ No newline at end of file
diff --git a/test_inference.py b/test_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c3fd74698f36e143e09730a0496c869f9d3a3e6
--- /dev/null
+++ b/test_inference.py
@@ -0,0 +1,61 @@
+import coremltools as ct
+import numpy as np
+import soundfile as sf
+import json
+
+# Load metadata
+with open("parakeet_ctc_coreml/metadata.json") as f:
+    meta = json.load(f)
+
+SAMPLE_RATE = meta["sample_rate"]
+MAX_SAMPLES = meta["max_audio_samples"]
+BLANK_ID = meta["blank_id"]
+
+# Load models
+mel_encoder = ct.models.MLModel("parakeet_ctc_coreml/parakeet_ctc_mel_encoder.mlpackage")
+ctc_decoder = ct.models.MLModel("parakeet_ctc_coreml/parakeet_ctc_decoder.mlpackage")
+
+# Load and pad/trim audio
+audio, sr = sf.read("yc_first_minute_16k_15s.wav", dtype="float32", always_2d=False)
+assert sr == SAMPLE_RATE, f"Expected {SAMPLE_RATE}Hz, got {sr}Hz"
+original_len = len(audio)
+if len(audio) < MAX_SAMPLES:
+    audio = np.pad(audio, (0, MAX_SAMPLES - len(audio)))
+else:
+    audio = audio[:MAX_SAMPLES]
+
+audio_signal = audio[np.newaxis, :].astype(np.float32)  # [1, N]
+audio_length = np.array([min(original_len, MAX_SAMPLES)], dtype=np.int32)  # [1]
+
+# Stage 1: Mel + Encoder
+enc_out = mel_encoder.predict({
+    "audio_signal": audio_signal,
+    "audio_length": audio_length,
+})
+encoder = enc_out["encoder"]
+encoder_length = enc_out["encoder_length"]
+print(f"Encoder output shape: {encoder.shape}")  # [1, hidden, T]
+
+# Stage 2: CTC Decoder
+dec_out = ctc_decoder.predict({"encoder": encoder})
+log_probs = dec_out["log_probs"]  # [1, T, vocab+1]
+print(f"Log probs shape: {log_probs.shape}")
+
+# Greedy decode
+token_ids = np.argmax(log_probs[0], axis=-1)  # [T]
+
+# CTC collapse (remove blanks and repeated tokens)
+decoded = []
+prev = None
+for t in token_ids:
+    if t != BLANK_ID and t != prev:
+        decoded.append(int(t))
+    prev = t
+
+# Load vocab and decode
+with open("vocab.json") as f:
+    vocab = json.load(f)
+
+text = "".join(vocab[i] for i in decoded).replace("", " ").strip()
+print(f"Transcription: {text}")
+
diff --git a/vocab.json b/vocab.json
new file mode 100644
index 0000000000000000000000000000000000000000..2c73a6e8a6b8c7bcbd259247e76d06d48db826c5
--- /dev/null
+++ b/vocab.json
@@ -0,0 +1,1027 @@
+[
+  "<unk>",
+  "▁t",
+  "▁th",
+  "▁a",
+  "▁i",
+  "▁the",
+  "re",
+  "▁w",
+  "▁s",
+  "▁o",
+  "in",
+  "at",
+  "er",
+  "ou",
+  "nd",
+  "▁c",
+  "▁b",
+  "▁h",
+  "on",
+  "▁m",
+  "▁f",
+  "ing",
+  "▁to",
+  "en",
+  "▁p",
+  "▁and",
+  "▁d",
+  "es",
+  "or",
+  "an",
+  "ll",
+  "▁y",
+  "▁l",
+  "ed",
+  "▁of",
+  "▁in",
+  "it",
+  "is",
+  "▁you",
+  "▁that",
+  "ar",
+  "▁g",
+  "▁n",
+  "as",
+  "om",
+  "▁it",
+  "ic",
+  "ve",
+  "▁e",
+  "▁wh",
+  "▁be",
+  "us",
+  "le",
+  "al",
+  "ion",
+  "ow",
+  "▁we",
+  "▁re",
+  "▁is",
+  "ut",
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+  "ent",
+  "▁on",
+  "et",
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+  "ay",
+  "ct",
+  "▁he",
+  "id",
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+  "ro",
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+  "▁so",
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+  "▁this",
+  "ke",
+  "▁u",
+  "se",
+  "all",
+  "st",
+  "ur",
+  "ce",
+  "ch",
+  "im",
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+  "▁as",
+  "▁k",
+  "▁an",
+  "▁was",
+  "▁j",
+  "▁with",
+  "ir",
+  "▁go",
+  "ra",
+  "▁do",
+  "▁have",
+  "▁li",
+  "▁sh",
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+  "▁they",
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+  "▁all",
+  "ers",
+  "▁like",
+  "ge",
+  "▁ex",
+  "▁som",
+  "ul",
+  "▁your",
+  "▁v",
+  "pp",
+  "use",
+  "▁if",
+  "ess",
+  "ate",
+  "est",
+  "▁know",
+  "out",
+  "if",
+  "▁just",
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+  "qu",
+  "op",
+  "ain",
+  "▁one",
+  "ol",
+  "ri",
+  "art",
+  "very",
+  "▁wor",
+  "ive",
+  "ist",
+  "▁my",
+  "nt",
+  "ab",
+  "▁from",
+  "ort",
+  "▁ma",
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+  "▁think",
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+  "▁tim",
+  "▁how",
+  "ven",
+  "▁tr",
+  "▁who",
+  "▁them",
+  "ure",
+  "▁ar",
+  "▁ye",
+  "▁more",
+  "▁going",
+  "ect",
+  "▁sa",
+  "▁cl",
+  "▁had",
+  "▁now",
+  "▁which",
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+  "▁peop",
+  "ough",
+  "▁no",
+  "▁really",
+  "▁any",
+  "▁people",
+  "▁want",
+  "▁she",
+  "▁en",
+  "▁fa",
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+  "ame",
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+  "red",
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+  "pt",
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+  "ff",
+  "ions",
+  "▁comp",
+  "▁did",
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+  "▁well",
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+  "iv",
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+  "▁two",
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+  "▁way",
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+  "▁where",
+  "ite",
+  "▁dis",
+  "▁than",
+  "▁every",
+  "▁pr",
+  "▁po",
+  "ag",
+  "▁need",
+  "ach",
+  "iff",
+  "ence",
+  "pl",
+  "own",
+  "▁ac",
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+]
\ No newline at end of file