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program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
{
func main<ios17>(tensor<fp32, [2, 1, 8, 256, 64]> attn0_cache, tensor<fp32, [1]> attn0_end_offset, tensor<fp32, [1]> attn0_offset, tensor<fp32, [2, 1, 8, 256, 64]> attn1_cache, tensor<fp32, [1]> attn1_end_offset, tensor<fp32, [1]> attn1_offset, tensor<fp32, [1]> conv0_first, tensor<fp32, [1, 512, 6]> conv0_prev, tensor<fp32, [1]> conv_final_first, tensor<fp32, [1, 64, 2]> conv_final_prev, tensor<fp32, [1, 256, 6]> convtr0_partial, tensor<fp32, [1, 128, 5]> convtr1_partial, tensor<fp32, [1, 64, 4]> convtr2_partial, tensor<fp32, [1, 32]> latent, tensor<fp32, [1]> res0_conv0_first, tensor<fp32, [1, 256, 2]> res0_conv0_prev, tensor<fp32, [1]> res0_conv1_first, tensor<fp32, [1, 128, 0]> res0_conv1_prev, tensor<fp32, [1]> res1_conv0_first, tensor<fp32, [1, 128, 2]> res1_conv0_prev, tensor<fp32, [1]> res1_conv1_first, tensor<fp32, [1, 64, 0]> res1_conv1_prev, tensor<fp32, [1]> res2_conv0_first, tensor<fp32, [1, 64, 2]> res2_conv0_prev, tensor<fp32, [1]> res2_conv1_first, tensor<fp32, [1, 32, 0]> res2_conv1_prev, tensor<fp32, [1, 512, 16]> upsample_partial) {
tensor<fp32, [32]> emb_mean = const()[name = tensor<string, []>("emb_mean"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
tensor<fp32, [32]> emb_std = const()[name = tensor<string, []>("emb_std"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(256)))];
tensor<fp32, [512, 32, 1]> mimi_quantizer_output_proj_weight = const()[name = tensor<string, []>("mimi_quantizer_output_proj_weight"), val = tensor<fp32, [512, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448)))];
tensor<fp32, [512, 1, 32]> mimi_upsample_convtr_convtr_weight = const()[name = tensor<string, []>("mimi_upsample_convtr_convtr_weight"), val = tensor<fp32, [512, 1, 32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66048)))];
tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_0_norm1_bias = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_norm1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131648)))];
tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_0_norm1_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_norm1_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133760)))];
tensor<fp32, [1536, 512]> mimi_decoder_transformer_transformer_layers_0_self_attn_in_proj_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_self_attn_in_proj_weight"), val = tensor<fp32, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135872)))];
tensor<fp32, [512, 512]> mimi_decoder_transformer_transformer_layers_0_self_attn_out_proj_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_self_attn_out_proj_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3281664)))];
tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4330304)))];
tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_0_norm2_bias = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_norm2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4332416)))];
tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_0_norm2_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_norm2_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4334528)))];
tensor<fp32, [2048, 512]> mimi_decoder_transformer_transformer_layers_0_linear1_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_linear1_weight"), val = tensor<fp32, [2048, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4336640)))];
tensor<fp32, [512, 2048]> mimi_decoder_transformer_transformer_layers_0_linear2_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_linear2_weight"), val = tensor<fp32, [512, 2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8531008)))];
tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12725376)))];
tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_1_norm1_bias = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_norm1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12727488)))];
tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_1_norm1_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_norm1_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12729600)))];
tensor<fp32, [1536, 512]> mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight"), val = tensor<fp32, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12731712)))];
tensor<fp32, [512, 512]> mimi_decoder_transformer_transformer_layers_1_self_attn_out_proj_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_self_attn_out_proj_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15877504)))];
tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16926144)))];
tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_1_norm2_bias = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_norm2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16928256)))];
tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_1_norm2_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_norm2_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16930368)))];
tensor<fp32, [2048, 512]> mimi_decoder_transformer_transformer_layers_1_linear1_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_linear1_weight"), val = tensor<fp32, [2048, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16932480)))];
tensor<fp32, [512, 2048]> mimi_decoder_transformer_transformer_layers_1_linear2_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_linear2_weight"), val = tensor<fp32, [512, 2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21126848)))];
tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25321216)))];
tensor<fp32, [512]> mimi_decoder_model_0_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_0_conv_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25323328)))];
tensor<fp32, [512, 512, 7]> mimi_decoder_model_0_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_0_conv_weight"), val = tensor<fp32, [512, 512, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25325440)))];
tensor<fp32, [256]> mimi_decoder_model_2_convtr_bias = const()[name = tensor<string, []>("mimi_decoder_model_2_convtr_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32665536)))];
tensor<fp32, [512, 256, 12]> mimi_decoder_model_2_convtr_weight = const()[name = tensor<string, []>("mimi_decoder_model_2_convtr_weight"), val = tensor<fp32, [512, 256, 12]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32666624)))];
tensor<fp32, [128]> mimi_decoder_model_3_block_1_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_3_block_1_conv_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38958144)))];
tensor<fp32, [128, 256, 3]> mimi_decoder_model_3_block_1_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_3_block_1_conv_weight"), val = tensor<fp32, [128, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38958720)))];
tensor<fp32, [256]> mimi_decoder_model_3_block_3_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_3_block_3_conv_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39352000)))];
tensor<fp32, [256, 128, 1]> mimi_decoder_model_3_block_3_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_3_block_3_conv_weight"), val = tensor<fp32, [256, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39353088)))];
tensor<fp32, [128]> mimi_decoder_model_5_convtr_bias = const()[name = tensor<string, []>("mimi_decoder_model_5_convtr_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39484224)))];
tensor<fp32, [256, 128, 10]> mimi_decoder_model_5_convtr_weight = const()[name = tensor<string, []>("mimi_decoder_model_5_convtr_weight"), val = tensor<fp32, [256, 128, 10]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39484800)))];
tensor<fp32, [64]> mimi_decoder_model_6_block_1_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_6_block_1_conv_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40795584)))];
tensor<fp32, [64, 128, 3]> mimi_decoder_model_6_block_1_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_6_block_1_conv_weight"), val = tensor<fp32, [64, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40795904)))];
tensor<fp32, [128]> mimi_decoder_model_6_block_3_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_6_block_3_conv_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40894272)))];
tensor<fp32, [128, 64, 1]> mimi_decoder_model_6_block_3_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_6_block_3_conv_weight"), val = tensor<fp32, [128, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40894848)))];
tensor<fp32, [64]> mimi_decoder_model_8_convtr_bias = const()[name = tensor<string, []>("mimi_decoder_model_8_convtr_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40927680)))];
tensor<fp32, [128, 64, 8]> mimi_decoder_model_8_convtr_weight = const()[name = tensor<string, []>("mimi_decoder_model_8_convtr_weight"), val = tensor<fp32, [128, 64, 8]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40928000)))];
tensor<fp32, [32]> mimi_decoder_model_9_block_1_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_9_block_1_conv_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41190208)))];
tensor<fp32, [32, 64, 3]> mimi_decoder_model_9_block_1_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_9_block_1_conv_weight"), val = tensor<fp32, [32, 64, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41190400)))];
tensor<fp32, [64]> mimi_decoder_model_9_block_3_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_9_block_3_conv_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41215040)))];
tensor<fp32, [64, 32, 1]> mimi_decoder_model_9_block_3_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_9_block_3_conv_weight"), val = tensor<fp32, [64, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41215360)))];
tensor<fp32, [1]> mimi_decoder_model_11_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_11_conv_bias"), val = tensor<fp32, [1]>([0x1.38p-15])];
tensor<fp32, [1, 64, 3]> mimi_decoder_model_11_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_11_conv_weight"), val = tensor<fp32, [1, 64, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41223616)))];
tensor<fp32, [1, 32]> var_40 = mul(x = latent, y = emb_std)[name = tensor<string, []>("op_40")];
tensor<fp32, [1, 32]> denorm = add(x = var_40, y = emb_mean)[name = tensor<string, []>("denorm")];
tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, 32, 1]> input_1 = expand_dims(axes = input_1_axes_0, x = denorm)[name = tensor<string, []>("input_1")];
tensor<string, []> x_1_pad_type_0 = const()[name = tensor<string, []>("x_1_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [1]> x_1_strides_0 = const()[name = tensor<string, []>("x_1_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> x_1_pad_0 = const()[name = tensor<string, []>("x_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> x_1_dilations_0 = const()[name = tensor<string, []>("x_1_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<int32, []> x_1_groups_0 = const()[name = tensor<string, []>("x_1_groups_0"), val = tensor<int32, []>(1)];
tensor<fp32, [1, 512, 1]> x_1 = conv(dilations = x_1_dilations_0, groups = x_1_groups_0, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = x_1_strides_0, weight = mimi_quantizer_output_proj_weight, x = input_1)[name = tensor<string, []>("x_1")];
tensor<int32, []> var_64 = const()[name = tensor<string, []>("op_64"), val = tensor<int32, []>(-1)];
tensor<string, []> y_1_pad_type_0 = const()[name = tensor<string, []>("y_1_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [1]> y_1_strides_0 = const()[name = tensor<string, []>("y_1_strides_0"), val = tensor<int32, [1]>([16])];
tensor<int32, []> y_1_groups_0 = const()[name = tensor<string, []>("y_1_groups_0"), val = tensor<int32, []>(512)];
tensor<int32, [2]> y_1_pad_0 = const()[name = tensor<string, []>("y_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> y_1_dilations_0 = const()[name = tensor<string, []>("y_1_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [3]> y_1_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("y_1_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 512, 32])];
tensor<fp32, [1, 512, 32]> y_1_has_output_shape = conv_transpose(dilations = y_1_dilations_0, groups = y_1_groups_0, output_shape = y_1_has_output_shape_output_shape_0, pad = y_1_pad_0, pad_type = y_1_pad_type_0, strides = y_1_strides_0, weight = mimi_upsample_convtr_convtr_weight, x = x_1)[name = tensor<string, []>("y_1_has_output_shape")];
tensor<int32, [3]> var_75_begin_0 = const()[name = tensor<string, []>("op_75_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
tensor<int32, [3]> var_75_end_0 = const()[name = tensor<string, []>("op_75_end_0"), val = tensor<int32, [3]>([1, 512, 16])];
tensor<bool, [3]> var_75_end_mask_0 = const()[name = tensor<string, []>("op_75_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
tensor<fp32, [1, 512, 16]> var_75 = slice_by_index(begin = var_75_begin_0, end = var_75_end_0, end_mask = var_75_end_mask_0, x = y_1_has_output_shape)[name = tensor<string, []>("op_75")];
tensor<fp32, [1, 512, 16]> var_76 = add(x = var_75, y = upsample_partial)[name = tensor<string, []>("op_76")];
tensor<int32, [3]> var_77_begin_0 = const()[name = tensor<string, []>("op_77_begin_0"), val = tensor<int32, [3]>([0, 0, 16])];
tensor<int32, [3]> var_77_end_0 = const()[name = tensor<string, []>("op_77_end_0"), val = tensor<int32, [3]>([1, 512, 32])];
tensor<bool, [3]> var_77_end_mask_0 = const()[name = tensor<string, []>("op_77_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
tensor<fp32, [1, 512, 16]> var_77 = slice_by_index(begin = var_77_begin_0, end = var_77_end_0, end_mask = var_77_end_mask_0, x = y_1_has_output_shape)[name = tensor<string, []>("op_77")];
tensor<bool, []> y_3_interleave_0 = const()[name = tensor<string, []>("y_3_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 512, 32]> y_3 = concat(axis = var_64, interleave = y_3_interleave_0, values = (var_76, var_77))[name = tensor<string, []>("y_3")];
tensor<int32, [3]> var_82_begin_0 = const()[name = tensor<string, []>("op_82_begin_0"), val = tensor<int32, [3]>([0, 0, 16])];
tensor<int32, [3]> var_82_end_0 = const()[name = tensor<string, []>("op_82_end_0"), val = tensor<int32, [3]>([1, 512, 32])];
tensor<bool, [3]> var_82_end_mask_0 = const()[name = tensor<string, []>("op_82_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
tensor<fp32, [1, 512, 16]> var_82 = slice_by_index(begin = var_82_begin_0, end = var_82_end_0, end_mask = var_82_end_mask_0, x = y_3)[name = tensor<string, []>("op_82")];
tensor<int32, [3]> x_3_begin_0 = const()[name = tensor<string, []>("x_3_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
tensor<int32, [3]> x_3_end_0 = const()[name = tensor<string, []>("x_3_end_0"), val = tensor<int32, [3]>([1, 512, 16])];
tensor<bool, [3]> x_3_end_mask_0 = const()[name = tensor<string, []>("x_3_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
tensor<fp32, [1, 512, 16]> x_3 = slice_by_index(begin = x_3_begin_0, end = x_3_end_0, end_mask = x_3_end_mask_0, x = y_3)[name = tensor<string, []>("x_3")];
tensor<int32, []> var_99 = const()[name = tensor<string, []>("op_99"), val = tensor<int32, []>(-1)];
tensor<fp32, []> var_107 = const()[name = tensor<string, []>("op_107"), val = tensor<fp32, []>(-0x1.ff933cp+127)];
tensor<fp32, []> var_109 = const()[name = tensor<string, []>("op_109"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
tensor<int32, [3]> input_3_perm_0 = const()[name = tensor<string, []>("input_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> query_1_axes_0 = const()[name = tensor<string, []>("query_1_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, 16, 512]> input_3 = transpose(perm = input_3_perm_0, x = x_3)[name = tensor<string, []>("transpose_17")];
tensor<fp32, [1, 16, 512]> query_1 = layer_norm(axes = query_1_axes_0, beta = mimi_decoder_transformer_transformer_layers_0_norm1_bias, epsilon = var_109, gamma = mimi_decoder_transformer_transformer_layers_0_norm1_weight, x = input_3)[name = tensor<string, []>("query_1")];
tensor<fp32, [1536]> linear_0_bias_0 = const()[name = tensor<string, []>("linear_0_bias_0"), val = tensor<fp32, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41224448)))];
tensor<fp32, [1, 16, 1536]> x_5 = linear(bias = linear_0_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_self_attn_in_proj_weight, x = query_1)[name = tensor<string, []>("linear_0")];
tensor<int32, [5]> var_141 = const()[name = tensor<string, []>("op_141"), val = tensor<int32, [5]>([1, 16, 3, 8, 64])];
tensor<fp32, [1, 16, 3, 8, 64]> x_7 = reshape(shape = var_141, x = x_5)[name = tensor<string, []>("x_7")];
tensor<int32, [5]> var_143 = const()[name = tensor<string, []>("op_143"), val = tensor<int32, [5]>([2, 0, 3, 1, 4])];
tensor<int32, [3]> var_145_split_sizes_0 = const()[name = tensor<string, []>("op_145_split_sizes_0"), val = tensor<int32, [3]>([1, 1, 1])];
tensor<int32, []> var_145_axis_0 = const()[name = tensor<string, []>("op_145_axis_0"), val = tensor<int32, []>(0)];
tensor<fp32, [3, 1, 8, 16, 64]> var_144 = transpose(perm = var_143, x = x_7)[name = tensor<string, []>("transpose_16")];
tensor<fp32, [1, 1, 8, 16, 64]> var_145_0, tensor<fp32, [1, 1, 8, 16, 64]> var_145_1, tensor<fp32, [1, 1, 8, 16, 64]> var_145_2 = split(axis = var_145_axis_0, split_sizes = var_145_split_sizes_0, x = var_144)[name = tensor<string, []>("op_145")];
tensor<int32, [1]> squeeze_0_axes_0 = const()[name = tensor<string, []>("squeeze_0_axes_0"), val = tensor<int32, [1]>([0])];
tensor<fp32, [1, 8, 16, 64]> squeeze_0 = squeeze(axes = squeeze_0_axes_0, x = var_145_0)[name = tensor<string, []>("squeeze_0")];
tensor<int32, [1]> squeeze_1_axes_0 = const()[name = tensor<string, []>("squeeze_1_axes_0"), val = tensor<int32, [1]>([0])];
tensor<fp32, [1, 8, 16, 64]> squeeze_1 = squeeze(axes = squeeze_1_axes_0, x = var_145_1)[name = tensor<string, []>("squeeze_1")];
tensor<int32, [1]> squeeze_2_axes_0 = const()[name = tensor<string, []>("squeeze_2_axes_0"), val = tensor<int32, [1]>([0])];
tensor<fp32, [1, 8, 16, 64]> squeeze_2 = squeeze(axes = squeeze_2_axes_0, x = var_145_2)[name = tensor<string, []>("squeeze_2")];
tensor<int32, [4]> var_149 = const()[name = tensor<string, []>("op_149"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_151 = const()[name = tensor<string, []>("op_151"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp32, [32]> freqs_1 = const()[name = tensor<string, []>("freqs_1"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41230656)))];
tensor<fp32, [16]> ts_1_promoted = const()[name = tensor<string, []>("ts_1_promoted"), val = tensor<fp32, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41230848)))];
tensor<fp32, [16]> ts_3 = add(x = ts_1_promoted, y = attn0_offset)[name = tensor<string, []>("ts_3")];
tensor<int32, [3]> var_167 = const()[name = tensor<string, []>("op_167"), val = tensor<int32, [3]>([-1, 1, 1])];
tensor<fp32, [16, 1, 1]> ts_5 = reshape(shape = var_167, x = ts_3)[name = tensor<string, []>("ts_5")];
tensor<int32, [5]> var_171 = const()[name = tensor<string, []>("op_171"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])];
tensor<fp32, [1, 16, 8, 64]> q_3 = transpose(perm = var_149, x = squeeze_0)[name = tensor<string, []>("transpose_15")];
tensor<fp32, [1, 16, 8, 32, 2]> q_5 = reshape(shape = var_171, x = q_3)[name = tensor<string, []>("q_5")];
tensor<int32, [5]> var_175 = const()[name = tensor<string, []>("op_175"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])];
tensor<fp32, [1, 16, 8, 64]> k_3 = transpose(perm = var_151, x = squeeze_1)[name = tensor<string, []>("transpose_14")];
tensor<fp32, [1, 16, 8, 32, 2]> k_5 = reshape(shape = var_175, x = k_3)[name = tensor<string, []>("k_5")];
tensor<int32, [5]> var_177_begin_0 = const()[name = tensor<string, []>("op_177_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 0])];
tensor<int32, [5]> var_177_end_0 = const()[name = tensor<string, []>("op_177_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 1])];
tensor<bool, [5]> var_177_end_mask_0 = const()[name = tensor<string, []>("op_177_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])];
tensor<bool, [5]> var_177_squeeze_mask_0 = const()[name = tensor<string, []>("op_177_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])];
tensor<fp32, [1, 16, 8, 32]> var_177 = slice_by_index(begin = var_177_begin_0, end = var_177_end_0, end_mask = var_177_end_mask_0, squeeze_mask = var_177_squeeze_mask_0, x = q_5)[name = tensor<string, []>("op_177")];
tensor<int32, [5]> var_179_begin_0 = const()[name = tensor<string, []>("op_179_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 1])];
tensor<int32, [5]> var_179_end_0 = const()[name = tensor<string, []>("op_179_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])];
tensor<bool, [5]> var_179_end_mask_0 = const()[name = tensor<string, []>("op_179_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])];
tensor<bool, [5]> var_179_squeeze_mask_0 = const()[name = tensor<string, []>("op_179_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])];
tensor<fp32, [1, 16, 8, 32]> var_179 = slice_by_index(begin = var_179_begin_0, end = var_179_end_0, end_mask = var_179_end_mask_0, squeeze_mask = var_179_squeeze_mask_0, x = q_5)[name = tensor<string, []>("op_179")];
tensor<int32, [5]> var_181_begin_0 = const()[name = tensor<string, []>("op_181_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 0])];
tensor<int32, [5]> var_181_end_0 = const()[name = tensor<string, []>("op_181_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 1])];
tensor<bool, [5]> var_181_end_mask_0 = const()[name = tensor<string, []>("op_181_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])];
tensor<bool, [5]> var_181_squeeze_mask_0 = const()[name = tensor<string, []>("op_181_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])];
tensor<fp32, [1, 16, 8, 32]> var_181 = slice_by_index(begin = var_181_begin_0, end = var_181_end_0, end_mask = var_181_end_mask_0, squeeze_mask = var_181_squeeze_mask_0, x = k_5)[name = tensor<string, []>("op_181")];
tensor<int32, [5]> var_183_begin_0 = const()[name = tensor<string, []>("op_183_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 1])];
tensor<int32, [5]> var_183_end_0 = const()[name = tensor<string, []>("op_183_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])];
tensor<bool, [5]> var_183_end_mask_0 = const()[name = tensor<string, []>("op_183_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])];
tensor<bool, [5]> var_183_squeeze_mask_0 = const()[name = tensor<string, []>("op_183_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])];
tensor<fp32, [1, 16, 8, 32]> var_183 = slice_by_index(begin = var_183_begin_0, end = var_183_end_0, end_mask = var_183_end_mask_0, squeeze_mask = var_183_squeeze_mask_0, x = k_5)[name = tensor<string, []>("op_183")];
tensor<fp32, [16, 1, 32]> var_185 = mul(x = freqs_1, y = ts_5)[name = tensor<string, []>("op_185")];
tensor<fp32, [16, 1, 32]> rotr_1 = cos(x = var_185)[name = tensor<string, []>("rotr_1")];
tensor<fp32, [16, 1, 32]> roti_1 = sin(x = var_185)[name = tensor<string, []>("roti_1")];
tensor<fp32, [1, 16, 8, 32]> var_189 = mul(x = var_177, y = rotr_1)[name = tensor<string, []>("op_189")];
tensor<fp32, [1, 16, 8, 32]> var_190 = mul(x = var_179, y = roti_1)[name = tensor<string, []>("op_190")];
tensor<fp32, [1, 16, 8, 32]> qor_1 = sub(x = var_189, y = var_190)[name = tensor<string, []>("qor_1")];
tensor<fp32, [1, 16, 8, 32]> var_192 = mul(x = var_177, y = roti_1)[name = tensor<string, []>("op_192")];
tensor<fp32, [1, 16, 8, 32]> var_193 = mul(x = var_179, y = rotr_1)[name = tensor<string, []>("op_193")];
tensor<fp32, [1, 16, 8, 32]> qoi_1 = add(x = var_192, y = var_193)[name = tensor<string, []>("qoi_1")];
tensor<fp32, [1, 16, 8, 32]> var_195 = mul(x = var_181, y = rotr_1)[name = tensor<string, []>("op_195")];
tensor<fp32, [1, 16, 8, 32]> var_196 = mul(x = var_183, y = roti_1)[name = tensor<string, []>("op_196")];
tensor<fp32, [1, 16, 8, 32]> kor_1 = sub(x = var_195, y = var_196)[name = tensor<string, []>("kor_1")];
tensor<fp32, [1, 16, 8, 32]> var_198 = mul(x = var_181, y = roti_1)[name = tensor<string, []>("op_198")];
tensor<fp32, [1, 16, 8, 32]> var_199 = mul(x = var_183, y = rotr_1)[name = tensor<string, []>("op_199")];
tensor<fp32, [1, 16, 8, 32]> koi_1 = add(x = var_198, y = var_199)[name = tensor<string, []>("koi_1")];
tensor<int32, []> qo_1_axis_0 = const()[name = tensor<string, []>("qo_1_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp32, [1, 16, 8, 32, 2]> qo_1 = stack(axis = qo_1_axis_0, values = (qor_1, qoi_1))[name = tensor<string, []>("qo_1")];
tensor<int32, []> ko_1_axis_0 = const()[name = tensor<string, []>("ko_1_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp32, [1, 16, 8, 32, 2]> ko_1 = stack(axis = ko_1_axis_0, values = (kor_1, koi_1))[name = tensor<string, []>("ko_1")];
tensor<int32, [4]> var_209 = const()[name = tensor<string, []>("op_209"), val = tensor<int32, [4]>([1, 16, 8, 64])];
tensor<fp32, [1, 16, 8, 64]> q_7 = reshape(shape = var_209, x = qo_1)[name = tensor<string, []>("q_7")];
tensor<int32, [4]> var_211 = const()[name = tensor<string, []>("op_211"), val = tensor<int32, [4]>([1, 16, 8, 64])];
tensor<fp32, [1, 16, 8, 64]> k_7 = reshape(shape = var_211, x = ko_1)[name = tensor<string, []>("k_7")];
tensor<int32, [4]> var_218 = const()[name = tensor<string, []>("op_218"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [1]> capacity_1 = const()[name = tensor<string, []>("capacity_1"), val = tensor<int32, [1]>([256])];
tensor<int32, [16]> indexes_1 = const()[name = tensor<string, []>("indexes_1"), val = tensor<int32, [16]>([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])];
tensor<string, []> cast_14_dtype_0 = const()[name = tensor<string, []>("cast_14_dtype_0"), val = tensor<string, []>("int32")];
tensor<int32, [2]> var_229 = const()[name = tensor<string, []>("op_229"), val = tensor<int32, [2]>([-1, 1])];
tensor<int32, [1]> cast_14 = cast(dtype = cast_14_dtype_0, x = attn0_end_offset)[name = tensor<string, []>("cast_61")];
tensor<int32, [1, 1]> var_230 = reshape(shape = var_229, x = cast_14)[name = tensor<string, []>("op_230")];
tensor<int32, [1, 16]> indexes_3 = add(x = indexes_1, y = var_230)[name = tensor<string, []>("indexes_3")];
tensor<int32, [1, 16]> indexes_5_div = floor_div(x = indexes_3, y = capacity_1)[name = tensor<string, []>("indexes_5_div")];
tensor<int32, [1, 16]> indexes_5_div_scaled = mul(x = indexes_5_div, y = capacity_1)[name = tensor<string, []>("indexes_5_div_scaled")];
tensor<int32, [1, 16]> indexes_5 = sub(x = indexes_3, y = indexes_5_div_scaled)[name = tensor<string, []>("indexes_5")];
tensor<int32, [4]> var_233 = const()[name = tensor<string, []>("op_233"), val = tensor<int32, [4]>([1, 1, 16, 1])];
tensor<int32, [1, 1, 16, 1]> var_234 = reshape(shape = var_233, x = indexes_5)[name = tensor<string, []>("op_234")];
tensor<int32, [4]> var_236_reps_0 = const()[name = tensor<string, []>("op_236_reps_0"), val = tensor<int32, [4]>([1, 8, 1, 64])];
tensor<int32, [1, 8, 16, 64]> var_236 = tile(reps = var_236_reps_0, x = var_234)[name = tensor<string, []>("op_236")];
tensor<int32, [5]> var_238_begin_0 = const()[name = tensor<string, []>("op_238_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 0])];
tensor<int32, [5]> var_238_end_0 = const()[name = tensor<string, []>("op_238_end_0"), val = tensor<int32, [5]>([1, 1, 8, 256, 64])];
tensor<bool, [5]> var_238_end_mask_0 = const()[name = tensor<string, []>("op_238_end_mask_0"), val = tensor<bool, [5]>([false, true, true, true, true])];
tensor<bool, [5]> var_238_squeeze_mask_0 = const()[name = tensor<string, []>("op_238_squeeze_mask_0"), val = tensor<bool, [5]>([true, false, false, false, false])];
tensor<fp32, [1, 8, 256, 64]> var_238 = slice_by_index(begin = var_238_begin_0, end = var_238_end_0, end_mask = var_238_end_mask_0, squeeze_mask = var_238_squeeze_mask_0, x = attn0_cache)[name = tensor<string, []>("op_238")];
tensor<int32, []> new_k_1_axis_0 = const()[name = tensor<string, []>("new_k_1_axis_0"), val = tensor<int32, []>(2)];
tensor<string, []> new_k_1_mode_0 = const()[name = tensor<string, []>("new_k_1_mode_0"), val = tensor<string, []>("update")];
tensor<bool, []> new_k_1_validate_indices_0 = const()[name = tensor<string, []>("new_k_1_validate_indices_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 8, 16, 64]> k_9 = transpose(perm = var_218, x = k_7)[name = tensor<string, []>("transpose_13")];
tensor<fp32, [1, 8, 256, 64]> new_k_1 = scatter_along_axis(axis = new_k_1_axis_0, data = var_238, indices = var_236, mode = new_k_1_mode_0, updates = k_9, validate_indices = new_k_1_validate_indices_0)[name = tensor<string, []>("new_k_1")];
tensor<int32, [5]> var_240_begin_0 = const()[name = tensor<string, []>("op_240_begin_0"), val = tensor<int32, [5]>([1, 0, 0, 0, 0])];
tensor<int32, [5]> var_240_end_0 = const()[name = tensor<string, []>("op_240_end_0"), val = tensor<int32, [5]>([2, 1, 8, 256, 64])];
tensor<bool, [5]> var_240_end_mask_0 = const()[name = tensor<string, []>("op_240_end_mask_0"), val = tensor<bool, [5]>([false, true, true, true, true])];
tensor<bool, [5]> var_240_squeeze_mask_0 = const()[name = tensor<string, []>("op_240_squeeze_mask_0"), val = tensor<bool, [5]>([true, false, false, false, false])];
tensor<fp32, [1, 8, 256, 64]> var_240 = slice_by_index(begin = var_240_begin_0, end = var_240_end_0, end_mask = var_240_end_mask_0, squeeze_mask = var_240_squeeze_mask_0, x = attn0_cache)[name = tensor<string, []>("op_240")];
tensor<int32, []> new_v_1_axis_0 = const()[name = tensor<string, []>("new_v_1_axis_0"), val = tensor<int32, []>(2)];
tensor<string, []> new_v_1_mode_0 = const()[name = tensor<string, []>("new_v_1_mode_0"), val = tensor<string, []>("update")];
tensor<bool, []> new_v_1_validate_indices_0 = const()[name = tensor<string, []>("new_v_1_validate_indices_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 8, 256, 64]> new_v_1 = scatter_along_axis(axis = new_v_1_axis_0, data = var_240, indices = var_236, mode = new_v_1_mode_0, updates = squeeze_2, validate_indices = new_v_1_validate_indices_0)[name = tensor<string, []>("new_v_1")];
tensor<int32, [2]> var_243 = const()[name = tensor<string, []>("op_243"), val = tensor<int32, [2]>([-1, 1])];
tensor<fp32, [1, 1]> var_244 = reshape(shape = var_243, x = attn0_end_offset)[name = tensor<string, []>("op_244")];
tensor<fp32, [1]> T_3_promoted = const()[name = tensor<string, []>("T_3_promoted"), val = tensor<fp32, [1]>([0x1p+4])];
tensor<fp32, [1, 1]> var_245 = add(x = var_244, y = T_3_promoted)[name = tensor<string, []>("op_245")];
tensor<fp32, []> var_246_promoted = const()[name = tensor<string, []>("op_246_promoted"), val = tensor<fp32, []>(0x1p+0)];
tensor<fp32, [1, 1]> last_offset_1 = sub(x = var_245, y = var_246_promoted)[name = tensor<string, []>("last_offset_1")];
tensor<fp32, [1]> capacity_1_promoted = const()[name = tensor<string, []>("capacity_1_promoted"), val = tensor<fp32, [1]>([0x1p+8])];
tensor<fp32, [1, 1]> end_index_1_div = floor_div(x = last_offset_1, y = capacity_1_promoted)[name = tensor<string, []>("end_index_1_div")];
tensor<fp32, [1, 1]> end_index_1_div_scaled = mul(x = end_index_1_div, y = capacity_1_promoted)[name = tensor<string, []>("end_index_1_div_scaled")];
tensor<fp32, [1, 1]> end_index_1 = sub(x = last_offset_1, y = end_index_1_div_scaled)[name = tensor<string, []>("end_index_1")];
tensor<fp32, [256]> indexes_7_promoted = const()[name = tensor<string, []>("indexes_7_promoted"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41230976)))];
tensor<fp32, [1, 256]> delta_1 = sub(x = indexes_7_promoted, y = end_index_1)[name = tensor<string, []>("delta_1")];
tensor<fp32, []> var_94_promoted = const()[name = tensor<string, []>("op_94_promoted"), val = tensor<fp32, []>(0x0p+0)];
tensor<bool, [1, 256]> var_250 = less_equal(x = delta_1, y = var_94_promoted)[name = tensor<string, []>("op_250")];
tensor<fp32, [1, 256]> var_251 = add(x = last_offset_1, y = delta_1)[name = tensor<string, []>("op_251")];
tensor<fp32, [1]> capacity_1_promoted_1 = const()[name = tensor<string, []>("capacity_1_promoted_1"), val = tensor<fp32, [1]>([0x1p+8])];
tensor<fp32, [1, 256]> var_253 = sub(x = var_251, y = capacity_1_promoted_1)[name = tensor<string, []>("op_253")];
tensor<fp32, [1, 256]> positions_1 = select(a = var_251, b = var_253, cond = var_250)[name = tensor<string, []>("positions_1")];
tensor<fp32, [1]> T_3_promoted_1 = const()[name = tensor<string, []>("T_3_promoted_1"), val = tensor<fp32, [1]>([0x1p+4])];
tensor<fp32, [1]> new_end_offset_1 = add(x = attn0_end_offset, y = T_3_promoted_1)[name = tensor<string, []>("new_end_offset_1")];
tensor<int32, [2]> var_256 = const()[name = tensor<string, []>("op_256"), val = tensor<int32, [2]>([-1, 1])];
tensor<fp32, [1, 1]> var_257 = reshape(shape = var_256, x = new_end_offset_1)[name = tensor<string, []>("op_257")];
tensor<bool, [1, 256]> invalid_1 = greater_equal(x = indexes_7_promoted, y = var_257)[name = tensor<string, []>("invalid_1")];
tensor<int32, []> var_259_value_0 = const()[name = tensor<string, []>("op_259_value_0"), val = tensor<int32, []>(-1)];
tensor<int32, [1, 256]> var_259 = fill_like(ref_tensor = positions_1, value = var_259_value_0)[name = tensor<string, []>("op_259")];
tensor<string, []> var_259_promoted_dtype_0 = const()[name = tensor<string, []>("op_259_promoted_dtype_0"), val = tensor<string, []>("fp32")];
tensor<fp32, [1, 256]> var_259_promoted = cast(dtype = var_259_promoted_dtype_0, x = var_259)[name = tensor<string, []>("cast_60")];
tensor<fp32, [1, 256]> pos_k_1 = select(a = var_259_promoted, b = positions_1, cond = invalid_1)[name = tensor<string, []>("pos_k_1")];
tensor<int32, []> var_262_axis_0 = const()[name = tensor<string, []>("op_262_axis_0"), val = tensor<int32, []>(0)];
tensor<fp32, [2, 1, 8, 256, 64]> var_262 = stack(axis = var_262_axis_0, values = (new_k_1, new_v_1))[name = tensor<string, []>("op_262")];
tensor<int32, [1]> pos_k_3_axes_0 = const()[name = tensor<string, []>("pos_k_3_axes_0"), val = tensor<int32, [1]>([1])];
tensor<fp32, [1, 1, 256]> pos_k_3 = expand_dims(axes = pos_k_3_axes_0, x = pos_k_1)[name = tensor<string, []>("pos_k_3")];
tensor<int32, [3]> var_265 = const()[name = tensor<string, []>("op_265"), val = tensor<int32, [3]>([-1, 1, 1])];
tensor<fp32, [1, 1, 1]> var_266 = reshape(shape = var_265, x = attn0_offset)[name = tensor<string, []>("op_266")];
tensor<fp32, [16, 1]> var_269_promoted = const()[name = tensor<string, []>("op_269_promoted"), val = tensor<fp32, [16, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41232064)))];
tensor<fp32, [1, 16, 1]> pos_q_1 = add(x = var_266, y = var_269_promoted)[name = tensor<string, []>("pos_q_1")];
tensor<fp32, [1, 16, 256]> delta_3 = sub(x = pos_q_1, y = pos_k_3)[name = tensor<string, []>("delta_3")];
tensor<fp32, []> var_94_promoted_1 = const()[name = tensor<string, []>("op_94_promoted_1"), val = tensor<fp32, []>(0x0p+0)];
tensor<bool, [1, 1, 256]> var_272 = greater_equal(x = pos_k_3, y = var_94_promoted_1)[name = tensor<string, []>("op_272")];
tensor<fp32, []> var_94_promoted_2 = const()[name = tensor<string, []>("op_94_promoted_2"), val = tensor<fp32, []>(0x0p+0)];
tensor<bool, [1, 16, 256]> var_273 = greater_equal(x = delta_3, y = var_94_promoted_2)[name = tensor<string, []>("op_273")];
tensor<bool, [1, 16, 256]> attn_bias_1 = logical_and(x = var_272, y = var_273)[name = tensor<string, []>("attn_bias_1")];
tensor<fp32, []> var_105_promoted = const()[name = tensor<string, []>("op_105_promoted"), val = tensor<fp32, []>(0x1.f4p+7)];
tensor<bool, [1, 16, 256]> var_275 = less(x = delta_3, y = var_105_promoted)[name = tensor<string, []>("op_275")];
tensor<bool, [1, 16, 256]> attn_bias_3 = logical_and(x = attn_bias_1, y = var_275)[name = tensor<string, []>("attn_bias_3")];
tensor<int32, [1]> attn_bias_5_axes_0 = const()[name = tensor<string, []>("attn_bias_5_axes_0"), val = tensor<int32, [1]>([1])];
tensor<bool, [1, 1, 16, 256]> attn_bias_5 = expand_dims(axes = attn_bias_5_axes_0, x = attn_bias_3)[name = tensor<string, []>("attn_bias_5")];
tensor<int32, [4]> transpose_0_perm_0 = const()[name = tensor<string, []>("transpose_0_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<bool, []> var_280_transpose_x_1 = const()[name = tensor<string, []>("op_280_transpose_x_1"), val = tensor<bool, []>(false)];
tensor<bool, []> var_280_transpose_y_1 = const()[name = tensor<string, []>("op_280_transpose_y_1"), val = tensor<bool, []>(true)];
tensor<fp32, [1, 8, 16, 64]> transpose_0 = transpose(perm = transpose_0_perm_0, x = q_7)[name = tensor<string, []>("transpose_12")];
tensor<fp32, [1, 8, 16, 256]> var_280 = matmul(transpose_x = var_280_transpose_x_1, transpose_y = var_280_transpose_y_1, x = transpose_0, y = new_k_1)[name = tensor<string, []>("op_280")];
tensor<fp32, []> var_281 = const()[name = tensor<string, []>("op_281"), val = tensor<fp32, []>(0x1p-3)];
tensor<fp32, [1, 8, 16, 256]> attn_1 = mul(x = var_280, y = var_281)[name = tensor<string, []>("attn_1")];
tensor<bool, [1, 1, 16, 256]> var_283 = logical_not(x = attn_bias_5)[name = tensor<string, []>("op_283")];
tensor<fp32, [1, 8, 16, 256]> attn_3 = select(a = var_107, b = attn_1, cond = var_283)[name = tensor<string, []>("attn_3")];
tensor<fp32, [1, 8, 16, 256]> attn_5 = softmax(axis = var_99, x = attn_3)[name = tensor<string, []>("attn_5")];
tensor<bool, []> x_9_transpose_x_0 = const()[name = tensor<string, []>("x_9_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> x_9_transpose_y_0 = const()[name = tensor<string, []>("x_9_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 8, 16, 64]> x_9 = matmul(transpose_x = x_9_transpose_x_0, transpose_y = x_9_transpose_y_0, x = attn_5, y = new_v_1)[name = tensor<string, []>("x_9")];
tensor<int32, [4]> var_305 = const()[name = tensor<string, []>("op_305"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_307 = const()[name = tensor<string, []>("op_307"), val = tensor<int32, [3]>([1, 16, 512])];
tensor<fp32, [1, 16, 8, 64]> x_11 = transpose(perm = var_305, x = x_9)[name = tensor<string, []>("transpose_11")];
tensor<fp32, [1, 16, 512]> input_5 = reshape(shape = var_307, x = x_11)[name = tensor<string, []>("input_5")];
tensor<fp32, [512]> linear_1_bias_0 = const()[name = tensor<string, []>("linear_1_bias_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41232192)))];
tensor<fp32, [1, 16, 512]> x_13 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_self_attn_out_proj_weight, x = input_5)[name = tensor<string, []>("linear_1")];
tensor<fp32, [1, 16, 512]> var_317 = mul(x = mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale, y = x_13)[name = tensor<string, []>("op_317")];
tensor<fp32, [1, 16, 512]> input_7 = add(x = input_3, y = var_317)[name = tensor<string, []>("input_7")];
tensor<int32, [1]> input_9_axes_0 = const()[name = tensor<string, []>("input_9_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, 16, 512]> input_9 = layer_norm(axes = input_9_axes_0, beta = mimi_decoder_transformer_transformer_layers_0_norm2_bias, epsilon = var_109, gamma = mimi_decoder_transformer_transformer_layers_0_norm2_weight, x = input_7)[name = tensor<string, []>("input_9")];
tensor<fp32, [2048]> linear_2_bias_0 = const()[name = tensor<string, []>("linear_2_bias_0"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41234304)))];
tensor<fp32, [1, 16, 2048]> var_324 = linear(bias = linear_2_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_linear1_weight, x = input_9)[name = tensor<string, []>("linear_2")];
tensor<string, []> input_11_mode_0 = const()[name = tensor<string, []>("input_11_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp32, [1, 16, 2048]> input_11 = gelu(mode = input_11_mode_0, x = var_324)[name = tensor<string, []>("input_11")];
tensor<fp32, [1, 16, 512]> x_15 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_linear2_weight, x = input_11)[name = tensor<string, []>("linear_3")];
tensor<fp32, [1, 16, 512]> var_330 = mul(x = mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale, y = x_15)[name = tensor<string, []>("op_330")];
tensor<fp32, [1, 16, 512]> input_13 = add(x = input_7, y = var_330)[name = tensor<string, []>("input_13")];
tensor<int32, [1]> query_axes_0 = const()[name = tensor<string, []>("query_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, 16, 512]> query = layer_norm(axes = query_axes_0, beta = mimi_decoder_transformer_transformer_layers_1_norm1_bias, epsilon = var_109, gamma = mimi_decoder_transformer_transformer_layers_1_norm1_weight, x = input_13)[name = tensor<string, []>("query")];
tensor<fp32, [1, 16, 1536]> x_17 = linear(bias = linear_0_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight, x = query)[name = tensor<string, []>("linear_4")];
tensor<int32, [5]> var_358 = const()[name = tensor<string, []>("op_358"), val = tensor<int32, [5]>([1, 16, 3, 8, 64])];
tensor<fp32, [1, 16, 3, 8, 64]> x_19 = reshape(shape = var_358, x = x_17)[name = tensor<string, []>("x_19")];
tensor<int32, [5]> var_360 = const()[name = tensor<string, []>("op_360"), val = tensor<int32, [5]>([2, 0, 3, 1, 4])];
tensor<int32, [3]> var_362_split_sizes_0 = const()[name = tensor<string, []>("op_362_split_sizes_0"), val = tensor<int32, [3]>([1, 1, 1])];
tensor<int32, []> var_362_axis_0 = const()[name = tensor<string, []>("op_362_axis_0"), val = tensor<int32, []>(0)];
tensor<fp32, [3, 1, 8, 16, 64]> var_361 = transpose(perm = var_360, x = x_19)[name = tensor<string, []>("transpose_10")];
tensor<fp32, [1, 1, 8, 16, 64]> var_362_0, tensor<fp32, [1, 1, 8, 16, 64]> var_362_1, tensor<fp32, [1, 1, 8, 16, 64]> var_362_2 = split(axis = var_362_axis_0, split_sizes = var_362_split_sizes_0, x = var_361)[name = tensor<string, []>("op_362")];
tensor<int32, [1]> squeeze_3_axes_0 = const()[name = tensor<string, []>("squeeze_3_axes_0"), val = tensor<int32, [1]>([0])];
tensor<fp32, [1, 8, 16, 64]> squeeze_3 = squeeze(axes = squeeze_3_axes_0, x = var_362_0)[name = tensor<string, []>("squeeze_3")];
tensor<int32, [1]> squeeze_4_axes_0 = const()[name = tensor<string, []>("squeeze_4_axes_0"), val = tensor<int32, [1]>([0])];
tensor<fp32, [1, 8, 16, 64]> squeeze_4 = squeeze(axes = squeeze_4_axes_0, x = var_362_1)[name = tensor<string, []>("squeeze_4")];
tensor<int32, [1]> squeeze_5_axes_0 = const()[name = tensor<string, []>("squeeze_5_axes_0"), val = tensor<int32, [1]>([0])];
tensor<fp32, [1, 8, 16, 64]> squeeze_5 = squeeze(axes = squeeze_5_axes_0, x = var_362_2)[name = tensor<string, []>("squeeze_5")];
tensor<int32, [4]> var_366 = const()[name = tensor<string, []>("op_366"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> var_368 = const()[name = tensor<string, []>("op_368"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp32, [32]> freqs = const()[name = tensor<string, []>("freqs"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41242560)))];
tensor<fp32, [16]> ts_7_promoted = const()[name = tensor<string, []>("ts_7_promoted"), val = tensor<fp32, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41242752)))];
tensor<fp32, [16]> ts_9 = add(x = ts_7_promoted, y = attn1_offset)[name = tensor<string, []>("ts_9")];
tensor<int32, [3]> var_384 = const()[name = tensor<string, []>("op_384"), val = tensor<int32, [3]>([-1, 1, 1])];
tensor<fp32, [16, 1, 1]> ts = reshape(shape = var_384, x = ts_9)[name = tensor<string, []>("ts")];
tensor<int32, [5]> var_388 = const()[name = tensor<string, []>("op_388"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])];
tensor<fp32, [1, 16, 8, 64]> q_13 = transpose(perm = var_366, x = squeeze_3)[name = tensor<string, []>("transpose_9")];
tensor<fp32, [1, 16, 8, 32, 2]> q_15 = reshape(shape = var_388, x = q_13)[name = tensor<string, []>("q_15")];
tensor<int32, [5]> var_392 = const()[name = tensor<string, []>("op_392"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])];
tensor<fp32, [1, 16, 8, 64]> k_13 = transpose(perm = var_368, x = squeeze_4)[name = tensor<string, []>("transpose_8")];
tensor<fp32, [1, 16, 8, 32, 2]> k_15 = reshape(shape = var_392, x = k_13)[name = tensor<string, []>("k_15")];
tensor<int32, [5]> var_394_begin_0 = const()[name = tensor<string, []>("op_394_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 0])];
tensor<int32, [5]> var_394_end_0 = const()[name = tensor<string, []>("op_394_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 1])];
tensor<bool, [5]> var_394_end_mask_0 = const()[name = tensor<string, []>("op_394_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])];
tensor<bool, [5]> var_394_squeeze_mask_0 = const()[name = tensor<string, []>("op_394_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])];
tensor<fp32, [1, 16, 8, 32]> var_394 = slice_by_index(begin = var_394_begin_0, end = var_394_end_0, end_mask = var_394_end_mask_0, squeeze_mask = var_394_squeeze_mask_0, x = q_15)[name = tensor<string, []>("op_394")];
tensor<int32, [5]> var_396_begin_0 = const()[name = tensor<string, []>("op_396_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 1])];
tensor<int32, [5]> var_396_end_0 = const()[name = tensor<string, []>("op_396_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])];
tensor<bool, [5]> var_396_end_mask_0 = const()[name = tensor<string, []>("op_396_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])];
tensor<bool, [5]> var_396_squeeze_mask_0 = const()[name = tensor<string, []>("op_396_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])];
tensor<fp32, [1, 16, 8, 32]> var_396 = slice_by_index(begin = var_396_begin_0, end = var_396_end_0, end_mask = var_396_end_mask_0, squeeze_mask = var_396_squeeze_mask_0, x = q_15)[name = tensor<string, []>("op_396")];
tensor<int32, [5]> var_398_begin_0 = const()[name = tensor<string, []>("op_398_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 0])];
tensor<int32, [5]> var_398_end_0 = const()[name = tensor<string, []>("op_398_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 1])];
tensor<bool, [5]> var_398_end_mask_0 = const()[name = tensor<string, []>("op_398_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])];
tensor<bool, [5]> var_398_squeeze_mask_0 = const()[name = tensor<string, []>("op_398_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])];
tensor<fp32, [1, 16, 8, 32]> var_398 = slice_by_index(begin = var_398_begin_0, end = var_398_end_0, end_mask = var_398_end_mask_0, squeeze_mask = var_398_squeeze_mask_0, x = k_15)[name = tensor<string, []>("op_398")];
tensor<int32, [5]> var_400_begin_0 = const()[name = tensor<string, []>("op_400_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 1])];
tensor<int32, [5]> var_400_end_0 = const()[name = tensor<string, []>("op_400_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])];
tensor<bool, [5]> var_400_end_mask_0 = const()[name = tensor<string, []>("op_400_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])];
tensor<bool, [5]> var_400_squeeze_mask_0 = const()[name = tensor<string, []>("op_400_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])];
tensor<fp32, [1, 16, 8, 32]> var_400 = slice_by_index(begin = var_400_begin_0, end = var_400_end_0, end_mask = var_400_end_mask_0, squeeze_mask = var_400_squeeze_mask_0, x = k_15)[name = tensor<string, []>("op_400")];
tensor<fp32, [16, 1, 32]> var_402 = mul(x = freqs, y = ts)[name = tensor<string, []>("op_402")];
tensor<fp32, [16, 1, 32]> rotr = cos(x = var_402)[name = tensor<string, []>("rotr")];
tensor<fp32, [16, 1, 32]> roti = sin(x = var_402)[name = tensor<string, []>("roti")];
tensor<fp32, [1, 16, 8, 32]> var_406 = mul(x = var_394, y = rotr)[name = tensor<string, []>("op_406")];
tensor<fp32, [1, 16, 8, 32]> var_407 = mul(x = var_396, y = roti)[name = tensor<string, []>("op_407")];
tensor<fp32, [1, 16, 8, 32]> qor_5 = sub(x = var_406, y = var_407)[name = tensor<string, []>("qor_5")];
tensor<fp32, [1, 16, 8, 32]> var_409 = mul(x = var_394, y = roti)[name = tensor<string, []>("op_409")];
tensor<fp32, [1, 16, 8, 32]> var_410 = mul(x = var_396, y = rotr)[name = tensor<string, []>("op_410")];
tensor<fp32, [1, 16, 8, 32]> qoi_5 = add(x = var_409, y = var_410)[name = tensor<string, []>("qoi_5")];
tensor<fp32, [1, 16, 8, 32]> var_412 = mul(x = var_398, y = rotr)[name = tensor<string, []>("op_412")];
tensor<fp32, [1, 16, 8, 32]> var_413 = mul(x = var_400, y = roti)[name = tensor<string, []>("op_413")];
tensor<fp32, [1, 16, 8, 32]> kor_5 = sub(x = var_412, y = var_413)[name = tensor<string, []>("kor_5")];
tensor<fp32, [1, 16, 8, 32]> var_415 = mul(x = var_398, y = roti)[name = tensor<string, []>("op_415")];
tensor<fp32, [1, 16, 8, 32]> var_416 = mul(x = var_400, y = rotr)[name = tensor<string, []>("op_416")];
tensor<fp32, [1, 16, 8, 32]> koi_5 = add(x = var_415, y = var_416)[name = tensor<string, []>("koi_5")];
tensor<int32, []> qo_axis_0 = const()[name = tensor<string, []>("qo_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp32, [1, 16, 8, 32, 2]> qo = stack(axis = qo_axis_0, values = (qor_5, qoi_5))[name = tensor<string, []>("qo")];
tensor<int32, []> ko_axis_0 = const()[name = tensor<string, []>("ko_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp32, [1, 16, 8, 32, 2]> ko = stack(axis = ko_axis_0, values = (kor_5, koi_5))[name = tensor<string, []>("ko")];
tensor<int32, [4]> var_426 = const()[name = tensor<string, []>("op_426"), val = tensor<int32, [4]>([1, 16, 8, 64])];
tensor<fp32, [1, 16, 8, 64]> q_17 = reshape(shape = var_426, x = qo)[name = tensor<string, []>("q_17")];
tensor<int32, [4]> var_428 = const()[name = tensor<string, []>("op_428"), val = tensor<int32, [4]>([1, 16, 8, 64])];
tensor<fp32, [1, 16, 8, 64]> k_17 = reshape(shape = var_428, x = ko)[name = tensor<string, []>("k_17")];
tensor<int32, [4]> var_435 = const()[name = tensor<string, []>("op_435"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [1]> capacity = const()[name = tensor<string, []>("capacity"), val = tensor<int32, [1]>([256])];
tensor<int32, [16]> indexes_9 = const()[name = tensor<string, []>("indexes_9"), val = tensor<int32, [16]>([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])];
tensor<string, []> cast_43_dtype_0 = const()[name = tensor<string, []>("cast_43_dtype_0"), val = tensor<string, []>("int32")];
tensor<int32, [2]> var_446 = const()[name = tensor<string, []>("op_446"), val = tensor<int32, [2]>([-1, 1])];
tensor<int32, [1]> cast_43 = cast(dtype = cast_43_dtype_0, x = attn1_end_offset)[name = tensor<string, []>("cast_59")];
tensor<int32, [1, 1]> var_447 = reshape(shape = var_446, x = cast_43)[name = tensor<string, []>("op_447")];
tensor<int32, [1, 16]> indexes_11 = add(x = indexes_9, y = var_447)[name = tensor<string, []>("indexes_11")];
tensor<int32, [1, 16]> indexes_13_div = floor_div(x = indexes_11, y = capacity)[name = tensor<string, []>("indexes_13_div")];
tensor<int32, [1, 16]> indexes_13_div_scaled = mul(x = indexes_13_div, y = capacity)[name = tensor<string, []>("indexes_13_div_scaled")];
tensor<int32, [1, 16]> indexes_13 = sub(x = indexes_11, y = indexes_13_div_scaled)[name = tensor<string, []>("indexes_13")];
tensor<int32, [4]> var_450 = const()[name = tensor<string, []>("op_450"), val = tensor<int32, [4]>([1, 1, 16, 1])];
tensor<int32, [1, 1, 16, 1]> var_451 = reshape(shape = var_450, x = indexes_13)[name = tensor<string, []>("op_451")];
tensor<int32, [4]> var_453_reps_0 = const()[name = tensor<string, []>("op_453_reps_0"), val = tensor<int32, [4]>([1, 8, 1, 64])];
tensor<int32, [1, 8, 16, 64]> var_453 = tile(reps = var_453_reps_0, x = var_451)[name = tensor<string, []>("op_453")];
tensor<int32, [5]> var_455_begin_0 = const()[name = tensor<string, []>("op_455_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 0])];
tensor<int32, [5]> var_455_end_0 = const()[name = tensor<string, []>("op_455_end_0"), val = tensor<int32, [5]>([1, 1, 8, 256, 64])];
tensor<bool, [5]> var_455_end_mask_0 = const()[name = tensor<string, []>("op_455_end_mask_0"), val = tensor<bool, [5]>([false, true, true, true, true])];
tensor<bool, [5]> var_455_squeeze_mask_0 = const()[name = tensor<string, []>("op_455_squeeze_mask_0"), val = tensor<bool, [5]>([true, false, false, false, false])];
tensor<fp32, [1, 8, 256, 64]> var_455 = slice_by_index(begin = var_455_begin_0, end = var_455_end_0, end_mask = var_455_end_mask_0, squeeze_mask = var_455_squeeze_mask_0, x = attn1_cache)[name = tensor<string, []>("op_455")];
tensor<int32, []> new_k_axis_0 = const()[name = tensor<string, []>("new_k_axis_0"), val = tensor<int32, []>(2)];
tensor<string, []> new_k_mode_0 = const()[name = tensor<string, []>("new_k_mode_0"), val = tensor<string, []>("update")];
tensor<bool, []> new_k_validate_indices_0 = const()[name = tensor<string, []>("new_k_validate_indices_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 8, 16, 64]> k = transpose(perm = var_435, x = k_17)[name = tensor<string, []>("transpose_7")];
tensor<fp32, [1, 8, 256, 64]> new_k = scatter_along_axis(axis = new_k_axis_0, data = var_455, indices = var_453, mode = new_k_mode_0, updates = k, validate_indices = new_k_validate_indices_0)[name = tensor<string, []>("new_k")];
tensor<int32, [5]> var_457_begin_0 = const()[name = tensor<string, []>("op_457_begin_0"), val = tensor<int32, [5]>([1, 0, 0, 0, 0])];
tensor<int32, [5]> var_457_end_0 = const()[name = tensor<string, []>("op_457_end_0"), val = tensor<int32, [5]>([2, 1, 8, 256, 64])];
tensor<bool, [5]> var_457_end_mask_0 = const()[name = tensor<string, []>("op_457_end_mask_0"), val = tensor<bool, [5]>([false, true, true, true, true])];
tensor<bool, [5]> var_457_squeeze_mask_0 = const()[name = tensor<string, []>("op_457_squeeze_mask_0"), val = tensor<bool, [5]>([true, false, false, false, false])];
tensor<fp32, [1, 8, 256, 64]> var_457 = slice_by_index(begin = var_457_begin_0, end = var_457_end_0, end_mask = var_457_end_mask_0, squeeze_mask = var_457_squeeze_mask_0, x = attn1_cache)[name = tensor<string, []>("op_457")];
tensor<int32, []> new_v_axis_0 = const()[name = tensor<string, []>("new_v_axis_0"), val = tensor<int32, []>(2)];
tensor<string, []> new_v_mode_0 = const()[name = tensor<string, []>("new_v_mode_0"), val = tensor<string, []>("update")];
tensor<bool, []> new_v_validate_indices_0 = const()[name = tensor<string, []>("new_v_validate_indices_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 8, 256, 64]> new_v = scatter_along_axis(axis = new_v_axis_0, data = var_457, indices = var_453, mode = new_v_mode_0, updates = squeeze_5, validate_indices = new_v_validate_indices_0)[name = tensor<string, []>("new_v")];
tensor<int32, [2]> var_460 = const()[name = tensor<string, []>("op_460"), val = tensor<int32, [2]>([-1, 1])];
tensor<fp32, [1, 1]> var_461 = reshape(shape = var_460, x = attn1_end_offset)[name = tensor<string, []>("op_461")];
tensor<fp32, [1]> T_9_promoted = const()[name = tensor<string, []>("T_9_promoted"), val = tensor<fp32, [1]>([0x1p+4])];
tensor<fp32, [1, 1]> var_462 = add(x = var_461, y = T_9_promoted)[name = tensor<string, []>("op_462")];
tensor<fp32, []> var_463_promoted = const()[name = tensor<string, []>("op_463_promoted"), val = tensor<fp32, []>(0x1p+0)];
tensor<fp32, [1, 1]> last_offset = sub(x = var_462, y = var_463_promoted)[name = tensor<string, []>("last_offset")];
tensor<fp32, [1]> capacity_promoted = const()[name = tensor<string, []>("capacity_promoted"), val = tensor<fp32, [1]>([0x1p+8])];
tensor<fp32, [1, 1]> end_index_div = floor_div(x = last_offset, y = capacity_promoted)[name = tensor<string, []>("end_index_div")];
tensor<fp32, [1, 1]> end_index_div_scaled = mul(x = end_index_div, y = capacity_promoted)[name = tensor<string, []>("end_index_div_scaled")];
tensor<fp32, [1, 1]> end_index = sub(x = last_offset, y = end_index_div_scaled)[name = tensor<string, []>("end_index")];
tensor<fp32, [1, 256]> delta_5 = sub(x = indexes_7_promoted, y = end_index)[name = tensor<string, []>("delta_5")];
tensor<fp32, []> var_94_promoted_3 = const()[name = tensor<string, []>("op_94_promoted_3"), val = tensor<fp32, []>(0x0p+0)];
tensor<bool, [1, 256]> var_467 = less_equal(x = delta_5, y = var_94_promoted_3)[name = tensor<string, []>("op_467")];
tensor<fp32, [1, 256]> var_468 = add(x = last_offset, y = delta_5)[name = tensor<string, []>("op_468")];
tensor<fp32, [1]> capacity_promoted_1 = const()[name = tensor<string, []>("capacity_promoted_1"), val = tensor<fp32, [1]>([0x1p+8])];
tensor<fp32, [1, 256]> var_470 = sub(x = var_468, y = capacity_promoted_1)[name = tensor<string, []>("op_470")];
tensor<fp32, [1, 256]> positions = select(a = var_468, b = var_470, cond = var_467)[name = tensor<string, []>("positions")];
tensor<fp32, [1]> T_9_promoted_1 = const()[name = tensor<string, []>("T_9_promoted_1"), val = tensor<fp32, [1]>([0x1p+4])];
tensor<fp32, [1]> new_end_offset = add(x = attn1_end_offset, y = T_9_promoted_1)[name = tensor<string, []>("new_end_offset")];
tensor<int32, [2]> var_473 = const()[name = tensor<string, []>("op_473"), val = tensor<int32, [2]>([-1, 1])];
tensor<fp32, [1, 1]> var_474 = reshape(shape = var_473, x = new_end_offset)[name = tensor<string, []>("op_474")];
tensor<bool, [1, 256]> invalid = greater_equal(x = indexes_7_promoted, y = var_474)[name = tensor<string, []>("invalid")];
tensor<int32, []> var_476_value_0 = const()[name = tensor<string, []>("op_476_value_0"), val = tensor<int32, []>(-1)];
tensor<int32, [1, 256]> var_476 = fill_like(ref_tensor = positions, value = var_476_value_0)[name = tensor<string, []>("op_476")];
tensor<string, []> var_476_promoted_dtype_0 = const()[name = tensor<string, []>("op_476_promoted_dtype_0"), val = tensor<string, []>("fp32")];
tensor<fp32, [1, 256]> var_476_promoted = cast(dtype = var_476_promoted_dtype_0, x = var_476)[name = tensor<string, []>("cast_58")];
tensor<fp32, [1, 256]> pos_k_5 = select(a = var_476_promoted, b = positions, cond = invalid)[name = tensor<string, []>("pos_k_5")];
tensor<int32, []> var_479_axis_0 = const()[name = tensor<string, []>("op_479_axis_0"), val = tensor<int32, []>(0)];
tensor<fp32, [2, 1, 8, 256, 64]> var_479 = stack(axis = var_479_axis_0, values = (new_k, new_v))[name = tensor<string, []>("op_479")];
tensor<int32, [1]> pos_k_axes_0 = const()[name = tensor<string, []>("pos_k_axes_0"), val = tensor<int32, [1]>([1])];
tensor<fp32, [1, 1, 256]> pos_k = expand_dims(axes = pos_k_axes_0, x = pos_k_5)[name = tensor<string, []>("pos_k")];
tensor<int32, [3]> var_482 = const()[name = tensor<string, []>("op_482"), val = tensor<int32, [3]>([-1, 1, 1])];
tensor<fp32, [1, 1, 1]> var_483 = reshape(shape = var_482, x = attn1_offset)[name = tensor<string, []>("op_483")];
tensor<fp32, [16, 1]> var_486_promoted = const()[name = tensor<string, []>("op_486_promoted"), val = tensor<fp32, [16, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41242880)))];
tensor<fp32, [1, 16, 1]> pos_q = add(x = var_483, y = var_486_promoted)[name = tensor<string, []>("pos_q")];
tensor<fp32, [1, 16, 256]> delta = sub(x = pos_q, y = pos_k)[name = tensor<string, []>("delta")];
tensor<fp32, []> var_94_promoted_4 = const()[name = tensor<string, []>("op_94_promoted_4"), val = tensor<fp32, []>(0x0p+0)];
tensor<bool, [1, 1, 256]> var_489 = greater_equal(x = pos_k, y = var_94_promoted_4)[name = tensor<string, []>("op_489")];
tensor<fp32, []> var_94_promoted_5 = const()[name = tensor<string, []>("op_94_promoted_5"), val = tensor<fp32, []>(0x0p+0)];
tensor<bool, [1, 16, 256]> var_490 = greater_equal(x = delta, y = var_94_promoted_5)[name = tensor<string, []>("op_490")];
tensor<bool, [1, 16, 256]> attn_bias_7 = logical_and(x = var_489, y = var_490)[name = tensor<string, []>("attn_bias_7")];
tensor<fp32, []> var_105_promoted_1 = const()[name = tensor<string, []>("op_105_promoted_1"), val = tensor<fp32, []>(0x1.f4p+7)];
tensor<bool, [1, 16, 256]> var_492 = less(x = delta, y = var_105_promoted_1)[name = tensor<string, []>("op_492")];
tensor<bool, [1, 16, 256]> attn_bias_9 = logical_and(x = attn_bias_7, y = var_492)[name = tensor<string, []>("attn_bias_9")];
tensor<int32, [1]> attn_bias_axes_0 = const()[name = tensor<string, []>("attn_bias_axes_0"), val = tensor<int32, [1]>([1])];
tensor<bool, [1, 1, 16, 256]> attn_bias = expand_dims(axes = attn_bias_axes_0, x = attn_bias_9)[name = tensor<string, []>("attn_bias")];
tensor<int32, [4]> transpose_2_perm_0 = const()[name = tensor<string, []>("transpose_2_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<bool, []> var_497_transpose_x_1 = const()[name = tensor<string, []>("op_497_transpose_x_1"), val = tensor<bool, []>(false)];
tensor<bool, []> var_497_transpose_y_1 = const()[name = tensor<string, []>("op_497_transpose_y_1"), val = tensor<bool, []>(true)];
tensor<fp32, [1, 8, 16, 64]> transpose_2 = transpose(perm = transpose_2_perm_0, x = q_17)[name = tensor<string, []>("transpose_6")];
tensor<fp32, [1, 8, 16, 256]> var_497 = matmul(transpose_x = var_497_transpose_x_1, transpose_y = var_497_transpose_y_1, x = transpose_2, y = new_k)[name = tensor<string, []>("op_497")];
tensor<fp32, []> var_498 = const()[name = tensor<string, []>("op_498"), val = tensor<fp32, []>(0x1p-3)];
tensor<fp32, [1, 8, 16, 256]> attn_7 = mul(x = var_497, y = var_498)[name = tensor<string, []>("attn_7")];
tensor<bool, [1, 1, 16, 256]> var_500 = logical_not(x = attn_bias)[name = tensor<string, []>("op_500")];
tensor<fp32, [1, 8, 16, 256]> attn_9 = select(a = var_107, b = attn_7, cond = var_500)[name = tensor<string, []>("attn_9")];
tensor<fp32, [1, 8, 16, 256]> attn = softmax(axis = var_99, x = attn_9)[name = tensor<string, []>("attn")];
tensor<bool, []> x_21_transpose_x_0 = const()[name = tensor<string, []>("x_21_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> x_21_transpose_y_0 = const()[name = tensor<string, []>("x_21_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 8, 16, 64]> x_21 = matmul(transpose_x = x_21_transpose_x_0, transpose_y = x_21_transpose_y_0, x = attn, y = new_v)[name = tensor<string, []>("x_21")];
tensor<int32, [4]> var_522 = const()[name = tensor<string, []>("op_522"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_524 = const()[name = tensor<string, []>("op_524"), val = tensor<int32, [3]>([1, 16, 512])];
tensor<fp32, [1, 16, 8, 64]> x_23 = transpose(perm = var_522, x = x_21)[name = tensor<string, []>("transpose_5")];
tensor<fp32, [1, 16, 512]> input_15 = reshape(shape = var_524, x = x_23)[name = tensor<string, []>("input_15")];
tensor<fp32, [1, 16, 512]> x_25 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_self_attn_out_proj_weight, x = input_15)[name = tensor<string, []>("linear_5")];
tensor<fp32, [1, 16, 512]> var_534 = mul(x = mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale, y = x_25)[name = tensor<string, []>("op_534")];
tensor<fp32, [1, 16, 512]> input_17 = add(x = input_13, y = var_534)[name = tensor<string, []>("input_17")];
tensor<int32, [1]> input_19_axes_0 = const()[name = tensor<string, []>("input_19_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, 16, 512]> input_19 = layer_norm(axes = input_19_axes_0, beta = mimi_decoder_transformer_transformer_layers_1_norm2_bias, epsilon = var_109, gamma = mimi_decoder_transformer_transformer_layers_1_norm2_weight, x = input_17)[name = tensor<string, []>("input_19")];
tensor<fp32, [1, 16, 2048]> var_541 = linear(bias = linear_2_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_linear1_weight, x = input_19)[name = tensor<string, []>("linear_6")];
tensor<string, []> input_21_mode_0 = const()[name = tensor<string, []>("input_21_mode_0"), val = tensor<string, []>("EXACT")];
tensor<fp32, [1, 16, 2048]> input_21 = gelu(mode = input_21_mode_0, x = var_541)[name = tensor<string, []>("input_21")];
tensor<fp32, [1, 16, 512]> x_27 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_linear2_weight, x = input_21)[name = tensor<string, []>("linear_7")];
tensor<fp32, [1, 16, 512]> var_547 = mul(x = mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale, y = x_27)[name = tensor<string, []>("op_547")];
tensor<fp32, [1, 16, 512]> z = add(x = input_17, y = var_547)[name = tensor<string, []>("z")];
tensor<int32, [3]> x_29_perm_0 = const()[name = tensor<string, []>("x_29_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<fp32, []> var_569 = const()[name = tensor<string, []>("op_569"), val = tensor<fp32, []>(0x1p+0)];
tensor<int32, []> var_571 = const()[name = tensor<string, []>("op_571"), val = tensor<int32, []>(-1)];
tensor<bool, []> input_23_interleave_0 = const()[name = tensor<string, []>("input_23_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 512, 16]> x_29 = transpose(perm = x_29_perm_0, x = z)[name = tensor<string, []>("transpose_4")];
tensor<fp32, [1, 512, 22]> input_23 = concat(axis = var_571, interleave = input_23_interleave_0, values = (conv0_prev, x_29))[name = tensor<string, []>("input_23")];
tensor<string, []> input_25_pad_type_0 = const()[name = tensor<string, []>("input_25_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [1]> input_25_strides_0 = const()[name = tensor<string, []>("input_25_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> input_25_pad_0 = const()[name = tensor<string, []>("input_25_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> input_25_dilations_0 = const()[name = tensor<string, []>("input_25_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<int32, []> input_25_groups_0 = const()[name = tensor<string, []>("input_25_groups_0"), val = tensor<int32, []>(1)];
tensor<fp32, [1, 512, 16]> input_25 = conv(bias = mimi_decoder_model_0_conv_bias, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = mimi_decoder_model_0_conv_weight, x = input_23)[name = tensor<string, []>("input_25")];
tensor<int32, [3]> var_607_begin_0 = const()[name = tensor<string, []>("op_607_begin_0"), val = tensor<int32, [3]>([0, 0, 16])];
tensor<int32, [3]> var_607_end_0 = const()[name = tensor<string, []>("op_607_end_0"), val = tensor<int32, [3]>([1, 512, 22])];
tensor<bool, [3]> var_607_end_mask_0 = const()[name = tensor<string, []>("op_607_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
tensor<fp32, [1, 512, 6]> var_607 = slice_by_index(begin = var_607_begin_0, end = var_607_end_0, end_mask = var_607_end_mask_0, x = input_23)[name = tensor<string, []>("op_607")];
tensor<fp32, [1, 512, 16]> input_27 = elu(alpha = var_569, x = input_25)[name = tensor<string, []>("input_27")];
tensor<string, []> y_5_pad_type_0 = const()[name = tensor<string, []>("y_5_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [1]> y_5_strides_0 = const()[name = tensor<string, []>("y_5_strides_0"), val = tensor<int32, [1]>([6])];
tensor<int32, [2]> y_5_pad_0 = const()[name = tensor<string, []>("y_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> y_5_dilations_0 = const()[name = tensor<string, []>("y_5_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<int32, []> y_5_groups_0 = const()[name = tensor<string, []>("y_5_groups_0"), val = tensor<int32, []>(1)];
tensor<int32, [3]> y_5_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("y_5_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 256, 102])];
tensor<fp32, [1, 256, 102]> y_5_has_output_shape = conv_transpose(bias = mimi_decoder_model_2_convtr_bias, dilations = y_5_dilations_0, groups = y_5_groups_0, output_shape = y_5_has_output_shape_output_shape_0, pad = y_5_pad_0, pad_type = y_5_pad_type_0, strides = y_5_strides_0, weight = mimi_decoder_model_2_convtr_weight, x = input_27)[name = tensor<string, []>("y_5_has_output_shape")];
tensor<int32, [3]> var_624_begin_0 = const()[name = tensor<string, []>("op_624_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
tensor<int32, [3]> var_624_end_0 = const()[name = tensor<string, []>("op_624_end_0"), val = tensor<int32, [3]>([1, 256, 6])];
tensor<bool, [3]> var_624_end_mask_0 = const()[name = tensor<string, []>("op_624_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
tensor<fp32, [1, 256, 6]> var_624 = slice_by_index(begin = var_624_begin_0, end = var_624_end_0, end_mask = var_624_end_mask_0, x = y_5_has_output_shape)[name = tensor<string, []>("op_624")];
tensor<fp32, [1, 256, 6]> var_625 = add(x = var_624, y = convtr0_partial)[name = tensor<string, []>("op_625")];
tensor<int32, [3]> var_626_begin_0 = const()[name = tensor<string, []>("op_626_begin_0"), val = tensor<int32, [3]>([0, 0, 6])];
tensor<int32, [3]> var_626_end_0 = const()[name = tensor<string, []>("op_626_end_0"), val = tensor<int32, [3]>([1, 256, 102])];
tensor<bool, [3]> var_626_end_mask_0 = const()[name = tensor<string, []>("op_626_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
tensor<fp32, [1, 256, 96]> var_626 = slice_by_index(begin = var_626_begin_0, end = var_626_end_0, end_mask = var_626_end_mask_0, x = y_5_has_output_shape)[name = tensor<string, []>("op_626")];
tensor<bool, []> y_7_interleave_0 = const()[name = tensor<string, []>("y_7_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 256, 102]> y_7 = concat(axis = var_571, interleave = y_7_interleave_0, values = (var_625, var_626))[name = tensor<string, []>("y_7")];
tensor<int32, [3]> new_partial_1_begin_0 = const()[name = tensor<string, []>("new_partial_1_begin_0"), val = tensor<int32, [3]>([0, 0, 96])];
tensor<int32, [3]> new_partial_1_end_0 = const()[name = tensor<string, []>("new_partial_1_end_0"), val = tensor<int32, [3]>([1, 256, 102])];
tensor<bool, [3]> new_partial_1_end_mask_0 = const()[name = tensor<string, []>("new_partial_1_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
tensor<fp32, [1, 256, 6]> new_partial_1 = slice_by_index(begin = new_partial_1_begin_0, end = new_partial_1_end_0, end_mask = new_partial_1_end_mask_0, x = y_7)[name = tensor<string, []>("new_partial_1")];
tensor<fp32, [256, 1]> var_633 = const()[name = tensor<string, []>("op_633"), val = tensor<fp32, [256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41243008)))];
tensor<fp32, [1, 256, 6]> var_634 = sub(x = new_partial_1, y = var_633)[name = tensor<string, []>("op_634")];
tensor<int32, [3]> input_29_begin_0 = const()[name = tensor<string, []>("input_29_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
tensor<int32, [3]> input_29_end_0 = const()[name = tensor<string, []>("input_29_end_0"), val = tensor<int32, [3]>([1, 256, 96])];
tensor<bool, [3]> input_29_end_mask_0 = const()[name = tensor<string, []>("input_29_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
tensor<fp32, [1, 256, 96]> input_29 = slice_by_index(begin = input_29_begin_0, end = input_29_end_0, end_mask = input_29_end_mask_0, x = y_7)[name = tensor<string, []>("input_29")];
tensor<fp32, [1, 256, 96]> x_31 = elu(alpha = var_569, x = input_29)[name = tensor<string, []>("x_31")];
tensor<bool, []> input_31_interleave_0 = const()[name = tensor<string, []>("input_31_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 256, 98]> input_31 = concat(axis = var_571, interleave = input_31_interleave_0, values = (res0_conv0_prev, x_31))[name = tensor<string, []>("input_31")];
tensor<string, []> input_33_pad_type_0 = const()[name = tensor<string, []>("input_33_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [1]> input_33_strides_0 = const()[name = tensor<string, []>("input_33_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> input_33_pad_0 = const()[name = tensor<string, []>("input_33_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> input_33_dilations_0 = const()[name = tensor<string, []>("input_33_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<int32, []> input_33_groups_0 = const()[name = tensor<string, []>("input_33_groups_0"), val = tensor<int32, []>(1)];
tensor<fp32, [1, 128, 96]> input_33 = conv(bias = mimi_decoder_model_3_block_1_conv_bias, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = mimi_decoder_model_3_block_1_conv_weight, x = input_31)[name = tensor<string, []>("input_33")];
tensor<int32, [3]> var_660_begin_0 = const()[name = tensor<string, []>("op_660_begin_0"), val = tensor<int32, [3]>([0, 0, 96])];
tensor<int32, [3]> var_660_end_0 = const()[name = tensor<string, []>("op_660_end_0"), val = tensor<int32, [3]>([1, 256, 98])];
tensor<bool, [3]> var_660_end_mask_0 = const()[name = tensor<string, []>("op_660_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
tensor<fp32, [1, 256, 2]> var_660 = slice_by_index(begin = var_660_begin_0, end = var_660_end_0, end_mask = var_660_end_mask_0, x = input_31)[name = tensor<string, []>("op_660")];
tensor<fp32, [1, 128, 96]> x_33 = elu(alpha = var_569, x = input_33)[name = tensor<string, []>("x_33")];
tensor<string, []> v_5_pad_type_0 = const()[name = tensor<string, []>("v_5_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [1]> v_5_strides_0 = const()[name = tensor<string, []>("v_5_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> v_5_pad_0 = const()[name = tensor<string, []>("v_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> v_5_dilations_0 = const()[name = tensor<string, []>("v_5_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<int32, []> v_5_groups_0 = const()[name = tensor<string, []>("v_5_groups_0"), val = tensor<int32, []>(1)];
tensor<fp32, [1, 256, 96]> v_5 = conv(bias = mimi_decoder_model_3_block_3_conv_bias, dilations = v_5_dilations_0, groups = v_5_groups_0, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = v_5_strides_0, weight = mimi_decoder_model_3_block_3_conv_weight, x = x_33)[name = tensor<string, []>("v_5")];
tensor<fp32, [1, 256, 96]> input_35 = add(x = input_29, y = v_5)[name = tensor<string, []>("input_35")];
tensor<fp32, [1, 256, 96]> input_37 = elu(alpha = var_569, x = input_35)[name = tensor<string, []>("input_37")];
tensor<string, []> y_9_pad_type_0 = const()[name = tensor<string, []>("y_9_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [1]> y_9_strides_0 = const()[name = tensor<string, []>("y_9_strides_0"), val = tensor<int32, [1]>([5])];
tensor<int32, [2]> y_9_pad_0 = const()[name = tensor<string, []>("y_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> y_9_dilations_0 = const()[name = tensor<string, []>("y_9_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<int32, []> y_9_groups_0 = const()[name = tensor<string, []>("y_9_groups_0"), val = tensor<int32, []>(1)];
tensor<int32, [3]> y_9_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("y_9_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 128, 485])];
tensor<fp32, [1, 128, 485]> y_9_has_output_shape = conv_transpose(bias = mimi_decoder_model_5_convtr_bias, dilations = y_9_dilations_0, groups = y_9_groups_0, output_shape = y_9_has_output_shape_output_shape_0, pad = y_9_pad_0, pad_type = y_9_pad_type_0, strides = y_9_strides_0, weight = mimi_decoder_model_5_convtr_weight, x = input_37)[name = tensor<string, []>("y_9_has_output_shape")];
tensor<int32, [3]> var_690_begin_0 = const()[name = tensor<string, []>("op_690_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
tensor<int32, [3]> var_690_end_0 = const()[name = tensor<string, []>("op_690_end_0"), val = tensor<int32, [3]>([1, 128, 5])];
tensor<bool, [3]> var_690_end_mask_0 = const()[name = tensor<string, []>("op_690_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
tensor<fp32, [1, 128, 5]> var_690 = slice_by_index(begin = var_690_begin_0, end = var_690_end_0, end_mask = var_690_end_mask_0, x = y_9_has_output_shape)[name = tensor<string, []>("op_690")];
tensor<fp32, [1, 128, 5]> var_691 = add(x = var_690, y = convtr1_partial)[name = tensor<string, []>("op_691")];
tensor<int32, [3]> var_692_begin_0 = const()[name = tensor<string, []>("op_692_begin_0"), val = tensor<int32, [3]>([0, 0, 5])];
tensor<int32, [3]> var_692_end_0 = const()[name = tensor<string, []>("op_692_end_0"), val = tensor<int32, [3]>([1, 128, 485])];
tensor<bool, [3]> var_692_end_mask_0 = const()[name = tensor<string, []>("op_692_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
tensor<fp32, [1, 128, 480]> var_692 = slice_by_index(begin = var_692_begin_0, end = var_692_end_0, end_mask = var_692_end_mask_0, x = y_9_has_output_shape)[name = tensor<string, []>("op_692")];
tensor<bool, []> y_11_interleave_0 = const()[name = tensor<string, []>("y_11_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 128, 485]> y_11 = concat(axis = var_571, interleave = y_11_interleave_0, values = (var_691, var_692))[name = tensor<string, []>("y_11")];
tensor<int32, [3]> new_partial_3_begin_0 = const()[name = tensor<string, []>("new_partial_3_begin_0"), val = tensor<int32, [3]>([0, 0, 480])];
tensor<int32, [3]> new_partial_3_end_0 = const()[name = tensor<string, []>("new_partial_3_end_0"), val = tensor<int32, [3]>([1, 128, 485])];
tensor<bool, [3]> new_partial_3_end_mask_0 = const()[name = tensor<string, []>("new_partial_3_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
tensor<fp32, [1, 128, 5]> new_partial_3 = slice_by_index(begin = new_partial_3_begin_0, end = new_partial_3_end_0, end_mask = new_partial_3_end_mask_0, x = y_11)[name = tensor<string, []>("new_partial_3")];
tensor<fp32, [128, 1]> var_699 = const()[name = tensor<string, []>("op_699"), val = tensor<fp32, [128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41244096)))];
tensor<fp32, [1, 128, 5]> var_700 = sub(x = new_partial_3, y = var_699)[name = tensor<string, []>("op_700")];
tensor<int32, [3]> input_39_begin_0 = const()[name = tensor<string, []>("input_39_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
tensor<int32, [3]> input_39_end_0 = const()[name = tensor<string, []>("input_39_end_0"), val = tensor<int32, [3]>([1, 128, 480])];
tensor<bool, [3]> input_39_end_mask_0 = const()[name = tensor<string, []>("input_39_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
tensor<fp32, [1, 128, 480]> input_39 = slice_by_index(begin = input_39_begin_0, end = input_39_end_0, end_mask = input_39_end_mask_0, x = y_11)[name = tensor<string, []>("input_39")];
tensor<fp32, [1, 128, 480]> x_35 = elu(alpha = var_569, x = input_39)[name = tensor<string, []>("x_35")];
tensor<bool, []> input_41_interleave_0 = const()[name = tensor<string, []>("input_41_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 128, 482]> input_41 = concat(axis = var_571, interleave = input_41_interleave_0, values = (res1_conv0_prev, x_35))[name = tensor<string, []>("input_41")];
tensor<string, []> input_43_pad_type_0 = const()[name = tensor<string, []>("input_43_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [1]> input_43_strides_0 = const()[name = tensor<string, []>("input_43_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> input_43_pad_0 = const()[name = tensor<string, []>("input_43_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> input_43_dilations_0 = const()[name = tensor<string, []>("input_43_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<int32, []> input_43_groups_0 = const()[name = tensor<string, []>("input_43_groups_0"), val = tensor<int32, []>(1)];
tensor<fp32, [1, 64, 480]> input_43 = conv(bias = mimi_decoder_model_6_block_1_conv_bias, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = mimi_decoder_model_6_block_1_conv_weight, x = input_41)[name = tensor<string, []>("input_43")];
tensor<int32, [3]> var_726_begin_0 = const()[name = tensor<string, []>("op_726_begin_0"), val = tensor<int32, [3]>([0, 0, 480])];
tensor<int32, [3]> var_726_end_0 = const()[name = tensor<string, []>("op_726_end_0"), val = tensor<int32, [3]>([1, 128, 482])];
tensor<bool, [3]> var_726_end_mask_0 = const()[name = tensor<string, []>("op_726_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
tensor<fp32, [1, 128, 2]> var_726 = slice_by_index(begin = var_726_begin_0, end = var_726_end_0, end_mask = var_726_end_mask_0, x = input_41)[name = tensor<string, []>("op_726")];
tensor<fp32, [1, 64, 480]> x_37 = elu(alpha = var_569, x = input_43)[name = tensor<string, []>("x_37")];
tensor<string, []> v_7_pad_type_0 = const()[name = tensor<string, []>("v_7_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [1]> v_7_strides_0 = const()[name = tensor<string, []>("v_7_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> v_7_pad_0 = const()[name = tensor<string, []>("v_7_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> v_7_dilations_0 = const()[name = tensor<string, []>("v_7_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<int32, []> v_7_groups_0 = const()[name = tensor<string, []>("v_7_groups_0"), val = tensor<int32, []>(1)];
tensor<fp32, [1, 128, 480]> v_7 = conv(bias = mimi_decoder_model_6_block_3_conv_bias, dilations = v_7_dilations_0, groups = v_7_groups_0, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = v_7_strides_0, weight = mimi_decoder_model_6_block_3_conv_weight, x = x_37)[name = tensor<string, []>("v_7")];
tensor<fp32, [1, 128, 480]> input_45 = add(x = input_39, y = v_7)[name = tensor<string, []>("input_45")];
tensor<fp32, [1, 128, 480]> input_47 = elu(alpha = var_569, x = input_45)[name = tensor<string, []>("input_47")];
tensor<string, []> y_13_pad_type_0 = const()[name = tensor<string, []>("y_13_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [1]> y_13_strides_0 = const()[name = tensor<string, []>("y_13_strides_0"), val = tensor<int32, [1]>([4])];
tensor<int32, [2]> y_13_pad_0 = const()[name = tensor<string, []>("y_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> y_13_dilations_0 = const()[name = tensor<string, []>("y_13_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<int32, []> y_13_groups_0 = const()[name = tensor<string, []>("y_13_groups_0"), val = tensor<int32, []>(1)];
tensor<int32, [3]> y_13_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("y_13_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 64, 1924])];
tensor<fp32, [1, 64, 1924]> y_13_has_output_shape = conv_transpose(bias = mimi_decoder_model_8_convtr_bias, dilations = y_13_dilations_0, groups = y_13_groups_0, output_shape = y_13_has_output_shape_output_shape_0, pad = y_13_pad_0, pad_type = y_13_pad_type_0, strides = y_13_strides_0, weight = mimi_decoder_model_8_convtr_weight, x = input_47)[name = tensor<string, []>("y_13_has_output_shape")];
tensor<int32, [3]> var_756_begin_0 = const()[name = tensor<string, []>("op_756_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
tensor<int32, [3]> var_756_end_0 = const()[name = tensor<string, []>("op_756_end_0"), val = tensor<int32, [3]>([1, 64, 4])];
tensor<bool, [3]> var_756_end_mask_0 = const()[name = tensor<string, []>("op_756_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
tensor<fp32, [1, 64, 4]> var_756 = slice_by_index(begin = var_756_begin_0, end = var_756_end_0, end_mask = var_756_end_mask_0, x = y_13_has_output_shape)[name = tensor<string, []>("op_756")];
tensor<fp32, [1, 64, 4]> var_757 = add(x = var_756, y = convtr2_partial)[name = tensor<string, []>("op_757")];
tensor<int32, [3]> var_758_begin_0 = const()[name = tensor<string, []>("op_758_begin_0"), val = tensor<int32, [3]>([0, 0, 4])];
tensor<int32, [3]> var_758_end_0 = const()[name = tensor<string, []>("op_758_end_0"), val = tensor<int32, [3]>([1, 64, 1924])];
tensor<bool, [3]> var_758_end_mask_0 = const()[name = tensor<string, []>("op_758_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
tensor<fp32, [1, 64, 1920]> var_758 = slice_by_index(begin = var_758_begin_0, end = var_758_end_0, end_mask = var_758_end_mask_0, x = y_13_has_output_shape)[name = tensor<string, []>("op_758")];
tensor<bool, []> y_interleave_0 = const()[name = tensor<string, []>("y_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 64, 1924]> y = concat(axis = var_571, interleave = y_interleave_0, values = (var_757, var_758))[name = tensor<string, []>("y")];
tensor<int32, [3]> new_partial_begin_0 = const()[name = tensor<string, []>("new_partial_begin_0"), val = tensor<int32, [3]>([0, 0, 1920])];
tensor<int32, [3]> new_partial_end_0 = const()[name = tensor<string, []>("new_partial_end_0"), val = tensor<int32, [3]>([1, 64, 1924])];
tensor<bool, [3]> new_partial_end_mask_0 = const()[name = tensor<string, []>("new_partial_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
tensor<fp32, [1, 64, 4]> new_partial = slice_by_index(begin = new_partial_begin_0, end = new_partial_end_0, end_mask = new_partial_end_mask_0, x = y)[name = tensor<string, []>("new_partial")];
tensor<fp32, [64, 1]> var_765 = const()[name = tensor<string, []>("op_765"), val = tensor<fp32, [64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41244672)))];
tensor<fp32, [1, 64, 4]> var_766 = sub(x = new_partial, y = var_765)[name = tensor<string, []>("op_766")];
tensor<int32, [3]> input_49_begin_0 = const()[name = tensor<string, []>("input_49_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
tensor<int32, [3]> input_49_end_0 = const()[name = tensor<string, []>("input_49_end_0"), val = tensor<int32, [3]>([1, 64, 1920])];
tensor<bool, [3]> input_49_end_mask_0 = const()[name = tensor<string, []>("input_49_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
tensor<fp32, [1, 64, 1920]> input_49 = slice_by_index(begin = input_49_begin_0, end = input_49_end_0, end_mask = input_49_end_mask_0, x = y)[name = tensor<string, []>("input_49")];
tensor<fp32, [1, 64, 1920]> x_39 = elu(alpha = var_569, x = input_49)[name = tensor<string, []>("x_39")];
tensor<bool, []> input_51_interleave_0 = const()[name = tensor<string, []>("input_51_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 64, 1922]> input_51 = concat(axis = var_571, interleave = input_51_interleave_0, values = (res2_conv0_prev, x_39))[name = tensor<string, []>("input_51")];
tensor<string, []> input_53_pad_type_0 = const()[name = tensor<string, []>("input_53_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [1]> input_53_strides_0 = const()[name = tensor<string, []>("input_53_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> input_53_pad_0 = const()[name = tensor<string, []>("input_53_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> input_53_dilations_0 = const()[name = tensor<string, []>("input_53_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<int32, []> input_53_groups_0 = const()[name = tensor<string, []>("input_53_groups_0"), val = tensor<int32, []>(1)];
tensor<fp32, [1, 32, 1920]> input_53 = conv(bias = mimi_decoder_model_9_block_1_conv_bias, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = mimi_decoder_model_9_block_1_conv_weight, x = input_51)[name = tensor<string, []>("input_53")];
tensor<int32, [3]> var_792_begin_0 = const()[name = tensor<string, []>("op_792_begin_0"), val = tensor<int32, [3]>([0, 0, 1920])];
tensor<int32, [3]> var_792_end_0 = const()[name = tensor<string, []>("op_792_end_0"), val = tensor<int32, [3]>([1, 64, 1922])];
tensor<bool, [3]> var_792_end_mask_0 = const()[name = tensor<string, []>("op_792_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
tensor<fp32, [1, 64, 2]> var_792 = slice_by_index(begin = var_792_begin_0, end = var_792_end_0, end_mask = var_792_end_mask_0, x = input_51)[name = tensor<string, []>("op_792")];
tensor<fp32, [1, 32, 1920]> x_41 = elu(alpha = var_569, x = input_53)[name = tensor<string, []>("x_41")];
tensor<string, []> v_pad_type_0 = const()[name = tensor<string, []>("v_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [1]> v_strides_0 = const()[name = tensor<string, []>("v_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> v_pad_0 = const()[name = tensor<string, []>("v_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> v_dilations_0 = const()[name = tensor<string, []>("v_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<int32, []> v_groups_0 = const()[name = tensor<string, []>("v_groups_0"), val = tensor<int32, []>(1)];
tensor<fp32, [1, 64, 1920]> v = conv(bias = mimi_decoder_model_9_block_3_conv_bias, dilations = v_dilations_0, groups = v_groups_0, pad = v_pad_0, pad_type = v_pad_type_0, strides = v_strides_0, weight = mimi_decoder_model_9_block_3_conv_weight, x = x_41)[name = tensor<string, []>("v")];
tensor<fp32, [1, 64, 1920]> input_55 = add(x = input_49, y = v)[name = tensor<string, []>("input_55")];
tensor<fp32, [1, 64, 1920]> x = elu(alpha = var_569, x = input_55)[name = tensor<string, []>("x")];
tensor<bool, []> input_interleave_0 = const()[name = tensor<string, []>("input_interleave_0"), val = tensor<bool, []>(false)];
tensor<fp32, [1, 64, 1922]> input = concat(axis = var_571, interleave = input_interleave_0, values = (conv_final_prev, x))[name = tensor<string, []>("input")];
tensor<string, []> var_821_pad_type_0 = const()[name = tensor<string, []>("op_821_pad_type_0"), val = tensor<string, []>("valid")];
tensor<int32, [1]> var_821_strides_0 = const()[name = tensor<string, []>("op_821_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> var_821_pad_0 = const()[name = tensor<string, []>("op_821_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> var_821_dilations_0 = const()[name = tensor<string, []>("op_821_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<int32, []> var_821_groups_0 = const()[name = tensor<string, []>("op_821_groups_0"), val = tensor<int32, []>(1)];
tensor<fp32, [1, 1, 1920]> var_821 = conv(bias = mimi_decoder_model_11_conv_bias, dilations = var_821_dilations_0, groups = var_821_groups_0, pad = var_821_pad_0, pad_type = var_821_pad_type_0, strides = var_821_strides_0, weight = mimi_decoder_model_11_conv_weight, x = input)[name = tensor<string, []>("op_821")];
tensor<int32, [3]> var_824_begin_0 = const()[name = tensor<string, []>("op_824_begin_0"), val = tensor<int32, [3]>([0, 0, 1920])];
tensor<int32, [3]> var_824_end_0 = const()[name = tensor<string, []>("op_824_end_0"), val = tensor<int32, [3]>([1, 64, 1922])];
tensor<bool, [3]> var_824_end_mask_0 = const()[name = tensor<string, []>("op_824_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
tensor<fp32, [1, 64, 2]> var_824 = slice_by_index(begin = var_824_begin_0, end = var_824_end_0, end_mask = var_824_end_mask_0, x = input)[name = tensor<string, []>("op_824")];
tensor<fp32, []> var_839_promoted = const()[name = tensor<string, []>("op_839_promoted"), val = tensor<fp32, []>(0x1p+4)];
tensor<fp32, [1]> var_840 = add(x = attn0_offset, y = var_839_promoted)[name = tensor<string, []>("op_840")];
tensor<fp32, []> var_842_promoted = const()[name = tensor<string, []>("op_842_promoted"), val = tensor<fp32, []>(0x1p+4)];
tensor<fp32, [1]> var_843 = add(x = attn1_offset, y = var_842_promoted)[name = tensor<string, []>("op_843")];
tensor<fp32, [1]> conv0_first_tmp = identity(x = conv0_first)[name = tensor<string, []>("conv0_first_tmp")];
tensor<fp32, [1]> res0_conv0_first_tmp = identity(x = res0_conv0_first)[name = tensor<string, []>("res0_conv0_first_tmp")];
tensor<fp32, [1, 128, 0]> res0_conv1_prev_tmp = identity(x = res0_conv1_prev)[name = tensor<string, []>("res0_conv1_prev_tmp")];
tensor<fp32, [1]> res0_conv1_first_tmp = identity(x = res0_conv1_first)[name = tensor<string, []>("res0_conv1_first_tmp")];
tensor<fp32, [1]> res1_conv0_first_tmp = identity(x = res1_conv0_first)[name = tensor<string, []>("res1_conv0_first_tmp")];
tensor<fp32, [1, 64, 0]> res1_conv1_prev_tmp = identity(x = res1_conv1_prev)[name = tensor<string, []>("res1_conv1_prev_tmp")];
tensor<fp32, [1]> res1_conv1_first_tmp = identity(x = res1_conv1_first)[name = tensor<string, []>("res1_conv1_first_tmp")];
tensor<fp32, [1]> res2_conv0_first_tmp = identity(x = res2_conv0_first)[name = tensor<string, []>("res2_conv0_first_tmp")];
tensor<fp32, [1, 32, 0]> res2_conv1_prev_tmp = identity(x = res2_conv1_prev)[name = tensor<string, []>("res2_conv1_prev_tmp")];
tensor<fp32, [1]> res2_conv1_first_tmp = identity(x = res2_conv1_first)[name = tensor<string, []>("res2_conv1_first_tmp")];
tensor<fp32, [1]> conv_final_first_tmp = identity(x = conv_final_first)[name = tensor<string, []>("conv_final_first_tmp")];
} -> (var_821, var_82, var_262, var_840, new_end_offset_1, var_479, var_843, new_end_offset, var_607, conv0_first, var_634, var_660, res0_conv0_first, res0_conv1_prev, res0_conv1_first, var_700, var_726, res1_conv0_first, res1_conv1_prev, res1_conv1_first, var_766, var_792, res2_conv0_first, res2_conv1_prev, res2_conv1_first, var_824, conv_final_first);
} |