Upload 221 files

.gitattributes

@@ -41,3 +41,40 @@ iteration_1/voices/Gavin.wav filter=lfs diff=lfs merge=lfs -text
 iteration_1/voices/Nima.wav filter=lfs diff=lfs merge=lfs -text
 iteration_1/voices/Vinay.wav filter=lfs diff=lfs merge=lfs -text
 iteration_1/voices/Yinghao.wav filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/iter3-bench.dSYM/Contents/Resources/DWARF/iter3-bench filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/iter3-tts.dSYM/Contents/Resources/DWARF/iter3-tts filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/main.swift.o filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/Iter3TTS.build/main.swift.o filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/_AvailabilityInternal-1B5KVWYBQUUGE.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/_DarwinFoundation1-1B5KVWYBQUUGE.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/_DarwinFoundation2-2QRKVX2YW9EKB.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/_DarwinFoundation3-1AY9KULNE9YNH.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/ApplicationServices-2PYCH0QA1ZV36.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/CFNetwork-1Q26KUGZ3CGWH.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/ColorSync-HT6R949ZE64G.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/CoreFoundation-EDMSFLJAJ3BU.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/CoreGraphics-3QDEJS0X8OVJJ.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/CoreML-2HOW7YI1GEXGC.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/CoreServices-25VVZW5M8673J.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/CoreText-2VO6FSB5Y70KC.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/CoreVideo-1JYQWBGEH1VS1.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/CUPS-28JN5DGR7078T.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/Darwin-X9YXS4RX3XVN.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/DiskArbitration-2JZD40PZDRFNY.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/Dispatch-1USK9G7Y70Z5U.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/Foundation-3C3SN79YEWNG9.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/ImageIO-1BJ3JFECXF520.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/IOKit-1HUEP1NG6PT40.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/IOSurface-1OMYI417NCO2D.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/launch-BBBJ4XRZNQP9.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/MachO-162SHLP7JPO6Y.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/Metal-14U3N7JFT4JV4.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/ObjectiveC-3DJM3P4EV39IL.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/OpenGL-1VCTYZPBESOTA.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/Security-23T5KS5L5I10U.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/SwiftShims-2G7LFKBX04KYS.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/UniformTypeIdentifiers-19VC4W40XL29R.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/.build/arm64-apple-macosx/release/ModuleCache/3CXH9S7LQUVCL/XPC-2D1U01HLUX2UE.pcm filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/fixtures_python.wav filter=lfs diff=lfs merge=lfs -text
+iteration_3/swift/fixtures_swift.wav filter=lfs diff=lfs merge=lfs -text

iteration_3/compiled/bert_fp16.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d814f9b3864fe677eabec72c03d091945bab17675ee0bd9cfda8d9a6955121e
+size 243

iteration_3/compiled/bert_fp16.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7c341065633e4023d6368f8ff4dd9bdc8eee527b182472bac10a27345add346d
+size 437

iteration_3/compiled/bert_fp16.mlmodelc/metadata.json

@@ -0,0 +1,94 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 57 × 768)",
+        "shortDescription" : "",
+        "shape" : "[1, 57, 768]",
+        "name" : "sequence_output",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 512 × 57)",
+        "shortDescription" : "",
+        "shape" : "[1, 512, 57]",
+        "name" : "var_1030",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.linear" : 74,
+      "Ios18.scaledDotProductAttention" : 12,
+      "Ios18.sub" : 1,
+      "Select" : 2,
+      "Ios18.expandDims" : 2,
+      "Ios18.gelu" : 12,
+      "Ios18.gather" : 1,
+      "Ios18.add" : 27,
+      "Tile" : 1,
+      "Ios18.layerNorm" : 25,
+      "Ios18.transpose" : 49,
+      "Ios18.cast" : 5,
+      "Ios18.reshape" : 48,
+      "Ios18.greaterEqual" : 1
+    },
+    "computePrecision" : "Mixed (Float16, Int16, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-05-08",
+      "com.github.apple.coremltools.source" : "torch==2.11.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1 × 57)",
+        "shortDescription" : "",
+        "shape" : "[1, 57]",
+        "name" : "tokens",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1 × 57)",
+        "shortDescription" : "",
+        "shape" : "[1, 57]",
+        "name" : "attention_mask",
+        "type" : "MultiArray"
+      }
+    ],
+    "generatedClassName" : "bert_fp16",
+    "method" : "predict"
+  }
+]

iteration_3/compiled/bert_fp16.mlmodelc/model.mil

@@ -0,0 +1,442 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios18>(tensor<int32, [1, 57]> attention_mask, tensor<int32, [1, 57]> tokens) {
+            int32 inputs_embeds_batch_dims_0 = const()[name = string("inputs_embeds_batch_dims_0"), val = int32(0)];
+            bool inputs_embeds_validate_indices_0 = const()[name = string("inputs_embeds_validate_indices_0"), val = bool(false)];
+            tensor<fp16, [178, 128]> bert_embeddings_word_embeddings_weight_to_fp16 = const()[name = string("bert_embeddings_word_embeddings_weight_to_fp16"), val = tensor<fp16, [178, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            string tokens_to_int16_dtype_0 = const()[name = string("tokens_to_int16_dtype_0"), val = string("int16")];
+            string cast_53_dtype_0 = const()[name = string("cast_53_dtype_0"), val = string("int32")];
+            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
+            tensor<int16, [1, 57]> tokens_to_int16 = cast(dtype = tokens_to_int16_dtype_0, x = tokens)[name = string("cast_58")];
+            tensor<int32, [1, 57]> cast_53 = cast(dtype = cast_53_dtype_0, x = tokens_to_int16)[name = string("cast_57")];
+            tensor<bool, [1, 57]> greater_equal_0 = greater_equal(x = cast_53, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
+            int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(178)];
+            tensor<int32, [1, 57]> add_0 = add(x = cast_53, y = slice_by_index_0)[name = string("add_0")];
+            tensor<int32, [1, 57]> select_0 = select(a = cast_53, b = add_0, cond = greater_equal_0)[name = string("select_0")];
+            int32 inputs_embeds_cast_fp16_cast_uint16_axis_0 = const()[name = string("inputs_embeds_cast_fp16_cast_uint16_axis_0"), val = int32(0)];
+            string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")];
+            tensor<int16, [1, 57]> select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_56")];
+            tensor<fp16, [1, 57, 128]> inputs_embeds_cast_fp16_cast_uint16_cast_uint16 = gather(axis = inputs_embeds_cast_fp16_cast_uint16_axis_0, batch_dims = inputs_embeds_batch_dims_0, indices = select_0_to_int16, validate_indices = inputs_embeds_validate_indices_0, x = bert_embeddings_word_embeddings_weight_to_fp16)[name = string("inputs_embeds_cast_fp16_cast_uint16_cast_uint16")];
+            tensor<fp16, [1, 57, 128]> token_type_embeddings_1_to_fp16 = const()[name = string("token_type_embeddings_1_to_fp16"), val = tensor<fp16, [1, 57, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45696)))];
+            tensor<fp16, [1, 57, 128]> embeddings_1_cast_fp16 = add(x = inputs_embeds_cast_fp16_cast_uint16_cast_uint16, y = token_type_embeddings_1_to_fp16)[name = string("embeddings_1_cast_fp16")];
+            tensor<fp16, [1, 57, 128]> position_embeddings_1_to_fp16 = const()[name = string("position_embeddings_1_to_fp16"), val = tensor<fp16, [1, 57, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60352)))];
+            tensor<fp16, [1, 57, 128]> input_5_cast_fp16 = add(x = embeddings_1_cast_fp16, y = position_embeddings_1_to_fp16)[name = string("input_5_cast_fp16")];
+            tensor<int32, [1]> input_7_axes_0 = const()[name = string("input_7_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [128]> bert_embeddings_LayerNorm_weight_to_fp16 = const()[name = string("bert_embeddings_LayerNorm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75008)))];
+            tensor<fp16, [128]> bert_embeddings_LayerNorm_bias_to_fp16 = const()[name = string("bert_embeddings_LayerNorm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75328)))];
+            fp16 var_34_to_fp16 = const()[name = string("op_34_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [1, 57, 128]> input_7_cast_fp16 = layer_norm(axes = input_7_axes_0, beta = bert_embeddings_LayerNorm_bias_to_fp16, epsilon = var_34_to_fp16, gamma = bert_embeddings_LayerNorm_weight_to_fp16, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")];
+            tensor<int32, [1]> var_79_axes_0 = const()[name = string("op_79_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1, 57]> var_79 = expand_dims(axes = var_79_axes_0, x = attention_mask)[name = string("op_79")];
+            tensor<int32, [1]> var_81_axes_0 = const()[name = string("op_81_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<int32, [1, 1, 1, 57]> var_81 = expand_dims(axes = var_81_axes_0, x = var_79)[name = string("op_81")];
+            tensor<int32, [4]> var_90_reps_0 = const()[name = string("op_90_reps_0"), val = tensor<int32, [4]>([1, 1, 57, 1])];
+            tensor<int32, [1, 1, 57, 57]> var_90 = tile(reps = var_90_reps_0, x = var_81)[name = string("op_90")];
+            fp16 var_96_to_fp16 = const()[name = string("op_96_to_fp16"), val = fp16(0x1p+0)];
+            string var_95_to_fp16_dtype_0 = const()[name = string("op_95_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 1, 57, 57]> var_90_to_fp16 = cast(dtype = var_95_to_fp16_dtype_0, x = var_90)[name = string("cast_55")];
+            tensor<fp16, [1, 1, 57, 57]> inverted_mask_cast_fp16 = sub(x = var_96_to_fp16, y = var_90_to_fp16)[name = string("inverted_mask_cast_fp16")];
+            string var_103_dtype_0 = const()[name = string("op_103_dtype_0"), val = string("bool")];
+            fp16 var_104_to_fp16 = const()[name = string("op_104_to_fp16"), val = fp16(-inf)];
+            tensor<bool, [1, 1, 57, 57]> inverted_mask_cast_fp16_to_bool = cast(dtype = var_103_dtype_0, x = inverted_mask_cast_fp16)[name = string("cast_54")];
+            tensor<fp16, [1, 1, 57, 57]> attention_mask_cast_fp16 = select(a = var_104_to_fp16, b = inverted_mask_cast_fp16, cond = inverted_mask_cast_fp16_to_bool)[name = string("attention_mask_cast_fp16")];
+            tensor<fp16, [768, 128]> bert_encoder_embedding_hidden_mapping_in_weight_to_fp16 = const()[name = string("bert_encoder_embedding_hidden_mapping_in_weight_to_fp16"), val = tensor<fp16, [768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75648)))];
+            tensor<fp16, [768]> bert_encoder_embedding_hidden_mapping_in_bias_to_fp16 = const()[name = string("bert_encoder_embedding_hidden_mapping_in_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(272320)))];
+            tensor<fp16, [1, 57, 768]> linear_0_cast_fp16 = linear(bias = bert_encoder_embedding_hidden_mapping_in_bias_to_fp16, weight = bert_encoder_embedding_hidden_mapping_in_weight_to_fp16, x = input_7_cast_fp16)[name = string("linear_0_cast_fp16")];
+            tensor<fp16, [768, 768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(273920)))];
+            tensor<fp16, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1453632)))];
+            tensor<fp16, [1, 57, 768]> linear_1_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = linear_0_cast_fp16)[name = string("linear_1_cast_fp16")];
+            tensor<int32, [4]> var_143 = const()[name = string("op_143"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_3_cast_fp16 = reshape(shape = var_143, x = linear_1_cast_fp16)[name = string("x_3_cast_fp16")];
+            tensor<fp16, [768, 768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1455232)))];
+            tensor<fp16, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2634944)))];
+            tensor<fp16, [1, 57, 768]> linear_2_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = linear_0_cast_fp16)[name = string("linear_2_cast_fp16")];
+            tensor<int32, [4]> var_152 = const()[name = string("op_152"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_7_cast_fp16 = reshape(shape = var_152, x = linear_2_cast_fp16)[name = string("x_7_cast_fp16")];
+            tensor<fp16, [768, 768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2636544)))];
+            tensor<fp16, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3816256)))];
+            tensor<fp16, [1, 57, 768]> linear_3_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = linear_0_cast_fp16)[name = string("linear_3_cast_fp16")];
+            tensor<int32, [4]> var_161 = const()[name = string("op_161"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_11_cast_fp16 = reshape(shape = var_161, x = linear_3_cast_fp16)[name = string("x_11_cast_fp16")];
+            tensor<int32, [4]> transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [1, 12, 57, 64]> transpose_74 = transpose(perm = transpose_74_perm_0, x = x_11_cast_fp16)[name = string("transpose_154")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_73 = transpose(perm = transpose_73_perm_0, x = x_7_cast_fp16)[name = string("transpose_155")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_72 = transpose(perm = transpose_72_perm_0, x = x_3_cast_fp16)[name = string("transpose_156")];
+            tensor<fp16, [1, 12, 57, 64]> attention_output_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_73, query = transpose_72, value = transpose_74)[name = string("attention_output_1_cast_fp16")];
+            tensor<int32, [4]> attention_output_3_perm_0 = const()[name = string("attention_output_3_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_167 = const()[name = string("op_167"), val = tensor<int32, [3]>([1, 57, 768])];
+            tensor<fp16, [1, 57, 12, 64]> attention_output_3_cast_fp16 = transpose(perm = attention_output_3_perm_0, x = attention_output_1_cast_fp16)[name = string("transpose_153")];
+            tensor<fp16, [1, 57, 768]> input_9_cast_fp16 = reshape(shape = var_167, x = attention_output_3_cast_fp16)[name = string("input_9_cast_fp16")];
+            tensor<fp16, [768, 768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16"), val = tensor<fp16, [768, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3817856)))];
+            tensor<fp16, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4997568)))];
+            tensor<fp16, [1, 57, 768]> linear_4_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_4_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_11_cast_fp16 = add(x = linear_0_cast_fp16, y = linear_4_cast_fp16)[name = string("input_11_cast_fp16")];
+            tensor<int32, [1]> input_13_axes_0 = const()[name = string("input_13_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4999168)))];
+            tensor<fp16, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5000768)))];
+            fp16 var_118_to_fp16 = const()[name = string("op_118_to_fp16"), val = fp16(0x1p-24)];
+            tensor<fp16, [1, 57, 768]> input_13_cast_fp16 = layer_norm(axes = input_13_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")];
+            tensor<fp16, [2048, 768]> bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16"), val = tensor<fp16, [2048, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5002368)))];
+            tensor<fp16, [2048]> bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8148160)))];
+            tensor<fp16, [1, 57, 2048]> linear_5_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_13_cast_fp16)[name = string("linear_5_cast_fp16")];
+            string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("TANH_APPROXIMATION")];
+            tensor<fp16, [1, 57, 2048]> input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = linear_5_cast_fp16)[name = string("input_17_cast_fp16")];
+            tensor<fp16, [768, 2048]> bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16"), val = tensor<fp16, [768, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8152320)))];
+            tensor<fp16, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11298112)))];
+            tensor<fp16, [1, 57, 768]> linear_6_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_17_cast_fp16)[name = string("linear_6_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_19_cast_fp16 = add(x = linear_6_cast_fp16, y = input_13_cast_fp16)[name = string("input_19_cast_fp16")];
+            tensor<int32, [1]> hidden_states_3_axes_0 = const()[name = string("hidden_states_3_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11299712)))];
+            tensor<fp16, [768]> bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16 = const()[name = string("bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11301312)))];
+            tensor<fp16, [1, 57, 768]> hidden_states_3_cast_fp16 = layer_norm(axes = hidden_states_3_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_19_cast_fp16)[name = string("hidden_states_3_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_7_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_3_cast_fp16)[name = string("linear_7_cast_fp16")];
+            tensor<int32, [4]> var_218 = const()[name = string("op_218"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_15_cast_fp16 = reshape(shape = var_218, x = linear_7_cast_fp16)[name = string("x_15_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_8_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_3_cast_fp16)[name = string("linear_8_cast_fp16")];
+            tensor<int32, [4]> var_227 = const()[name = string("op_227"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_19_cast_fp16 = reshape(shape = var_227, x = linear_8_cast_fp16)[name = string("x_19_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_9_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_3_cast_fp16)[name = string("linear_9_cast_fp16")];
+            tensor<int32, [4]> var_236 = const()[name = string("op_236"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_23_cast_fp16 = reshape(shape = var_236, x = linear_9_cast_fp16)[name = string("x_23_cast_fp16")];
+            tensor<int32, [4]> transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [1, 12, 57, 64]> transpose_77 = transpose(perm = transpose_77_perm_0, x = x_23_cast_fp16)[name = string("transpose_150")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_76 = transpose(perm = transpose_76_perm_0, x = x_19_cast_fp16)[name = string("transpose_151")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_75 = transpose(perm = transpose_75_perm_0, x = x_15_cast_fp16)[name = string("transpose_152")];
+            tensor<fp16, [1, 12, 57, 64]> attention_output_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_76, query = transpose_75, value = transpose_77)[name = string("attention_output_5_cast_fp16")];
+            tensor<int32, [4]> attention_output_7_perm_0 = const()[name = string("attention_output_7_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_242 = const()[name = string("op_242"), val = tensor<int32, [3]>([1, 57, 768])];
+            tensor<fp16, [1, 57, 12, 64]> attention_output_7_cast_fp16 = transpose(perm = attention_output_7_perm_0, x = attention_output_5_cast_fp16)[name = string("transpose_149")];
+            tensor<fp16, [1, 57, 768]> input_21_cast_fp16 = reshape(shape = var_242, x = attention_output_7_cast_fp16)[name = string("input_21_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_10_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_21_cast_fp16)[name = string("linear_10_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_23_cast_fp16 = add(x = hidden_states_3_cast_fp16, y = linear_10_cast_fp16)[name = string("input_23_cast_fp16")];
+            tensor<int32, [1]> input_25_axes_0 = const()[name = string("input_25_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> input_25_cast_fp16 = layer_norm(axes = input_25_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")];
+            tensor<fp16, [1, 57, 2048]> linear_11_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_25_cast_fp16)[name = string("linear_11_cast_fp16")];
+            string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("TANH_APPROXIMATION")];
+            tensor<fp16, [1, 57, 2048]> input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = linear_11_cast_fp16)[name = string("input_29_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_12_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_29_cast_fp16)[name = string("linear_12_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_31_cast_fp16 = add(x = linear_12_cast_fp16, y = input_25_cast_fp16)[name = string("input_31_cast_fp16")];
+            tensor<int32, [1]> hidden_states_5_axes_0 = const()[name = string("hidden_states_5_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> hidden_states_5_cast_fp16 = layer_norm(axes = hidden_states_5_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_31_cast_fp16)[name = string("hidden_states_5_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_13_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_5_cast_fp16)[name = string("linear_13_cast_fp16")];
+            tensor<int32, [4]> var_293 = const()[name = string("op_293"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_27_cast_fp16 = reshape(shape = var_293, x = linear_13_cast_fp16)[name = string("x_27_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_14_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_5_cast_fp16)[name = string("linear_14_cast_fp16")];
+            tensor<int32, [4]> var_302 = const()[name = string("op_302"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_31_cast_fp16 = reshape(shape = var_302, x = linear_14_cast_fp16)[name = string("x_31_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_15_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_5_cast_fp16)[name = string("linear_15_cast_fp16")];
+            tensor<int32, [4]> var_311 = const()[name = string("op_311"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_35_cast_fp16 = reshape(shape = var_311, x = linear_15_cast_fp16)[name = string("x_35_cast_fp16")];
+            tensor<int32, [4]> transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [1, 12, 57, 64]> transpose_80 = transpose(perm = transpose_80_perm_0, x = x_35_cast_fp16)[name = string("transpose_146")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_79 = transpose(perm = transpose_79_perm_0, x = x_31_cast_fp16)[name = string("transpose_147")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_78 = transpose(perm = transpose_78_perm_0, x = x_27_cast_fp16)[name = string("transpose_148")];
+            tensor<fp16, [1, 12, 57, 64]> attention_output_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_79, query = transpose_78, value = transpose_80)[name = string("attention_output_9_cast_fp16")];
+            tensor<int32, [4]> attention_output_11_perm_0 = const()[name = string("attention_output_11_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_317 = const()[name = string("op_317"), val = tensor<int32, [3]>([1, 57, 768])];
+            tensor<fp16, [1, 57, 12, 64]> attention_output_11_cast_fp16 = transpose(perm = attention_output_11_perm_0, x = attention_output_9_cast_fp16)[name = string("transpose_145")];
+            tensor<fp16, [1, 57, 768]> input_33_cast_fp16 = reshape(shape = var_317, x = attention_output_11_cast_fp16)[name = string("input_33_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_16_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_33_cast_fp16)[name = string("linear_16_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_35_cast_fp16 = add(x = hidden_states_5_cast_fp16, y = linear_16_cast_fp16)[name = string("input_35_cast_fp16")];
+            tensor<int32, [1]> input_37_axes_0 = const()[name = string("input_37_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> input_37_cast_fp16 = layer_norm(axes = input_37_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_35_cast_fp16)[name = string("input_37_cast_fp16")];
+            tensor<fp16, [1, 57, 2048]> linear_17_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_37_cast_fp16)[name = string("linear_17_cast_fp16")];
+            string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("TANH_APPROXIMATION")];
+            tensor<fp16, [1, 57, 2048]> input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = linear_17_cast_fp16)[name = string("input_41_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_18_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_41_cast_fp16)[name = string("linear_18_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_43_cast_fp16 = add(x = linear_18_cast_fp16, y = input_37_cast_fp16)[name = string("input_43_cast_fp16")];
+            tensor<int32, [1]> hidden_states_7_axes_0 = const()[name = string("hidden_states_7_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> hidden_states_7_cast_fp16 = layer_norm(axes = hidden_states_7_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_43_cast_fp16)[name = string("hidden_states_7_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_19_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_7_cast_fp16)[name = string("linear_19_cast_fp16")];
+            tensor<int32, [4]> var_368 = const()[name = string("op_368"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_39_cast_fp16 = reshape(shape = var_368, x = linear_19_cast_fp16)[name = string("x_39_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_20_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_7_cast_fp16)[name = string("linear_20_cast_fp16")];
+            tensor<int32, [4]> var_377 = const()[name = string("op_377"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_43_cast_fp16 = reshape(shape = var_377, x = linear_20_cast_fp16)[name = string("x_43_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_21_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_7_cast_fp16)[name = string("linear_21_cast_fp16")];
+            tensor<int32, [4]> var_386 = const()[name = string("op_386"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_47_cast_fp16 = reshape(shape = var_386, x = linear_21_cast_fp16)[name = string("x_47_cast_fp16")];
+            tensor<int32, [4]> transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [1, 12, 57, 64]> transpose_83 = transpose(perm = transpose_83_perm_0, x = x_47_cast_fp16)[name = string("transpose_142")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_82 = transpose(perm = transpose_82_perm_0, x = x_43_cast_fp16)[name = string("transpose_143")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_81 = transpose(perm = transpose_81_perm_0, x = x_39_cast_fp16)[name = string("transpose_144")];
+            tensor<fp16, [1, 12, 57, 64]> attention_output_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_82, query = transpose_81, value = transpose_83)[name = string("attention_output_13_cast_fp16")];
+            tensor<int32, [4]> attention_output_15_perm_0 = const()[name = string("attention_output_15_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_392 = const()[name = string("op_392"), val = tensor<int32, [3]>([1, 57, 768])];
+            tensor<fp16, [1, 57, 12, 64]> attention_output_15_cast_fp16 = transpose(perm = attention_output_15_perm_0, x = attention_output_13_cast_fp16)[name = string("transpose_141")];
+            tensor<fp16, [1, 57, 768]> input_45_cast_fp16 = reshape(shape = var_392, x = attention_output_15_cast_fp16)[name = string("input_45_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_22_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_45_cast_fp16)[name = string("linear_22_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_47_cast_fp16 = add(x = hidden_states_7_cast_fp16, y = linear_22_cast_fp16)[name = string("input_47_cast_fp16")];
+            tensor<int32, [1]> input_49_axes_0 = const()[name = string("input_49_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> input_49_cast_fp16 = layer_norm(axes = input_49_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_47_cast_fp16)[name = string("input_49_cast_fp16")];
+            tensor<fp16, [1, 57, 2048]> linear_23_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_49_cast_fp16)[name = string("linear_23_cast_fp16")];
+            string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("TANH_APPROXIMATION")];
+            tensor<fp16, [1, 57, 2048]> input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = linear_23_cast_fp16)[name = string("input_53_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_24_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_53_cast_fp16)[name = string("linear_24_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_55_cast_fp16 = add(x = linear_24_cast_fp16, y = input_49_cast_fp16)[name = string("input_55_cast_fp16")];
+            tensor<int32, [1]> hidden_states_9_axes_0 = const()[name = string("hidden_states_9_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> hidden_states_9_cast_fp16 = layer_norm(axes = hidden_states_9_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_55_cast_fp16)[name = string("hidden_states_9_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_25_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = string("linear_25_cast_fp16")];
+            tensor<int32, [4]> var_443 = const()[name = string("op_443"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_51_cast_fp16 = reshape(shape = var_443, x = linear_25_cast_fp16)[name = string("x_51_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_26_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = string("linear_26_cast_fp16")];
+            tensor<int32, [4]> var_452 = const()[name = string("op_452"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_55_cast_fp16 = reshape(shape = var_452, x = linear_26_cast_fp16)[name = string("x_55_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_27_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_9_cast_fp16)[name = string("linear_27_cast_fp16")];
+            tensor<int32, [4]> var_461 = const()[name = string("op_461"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_59_cast_fp16 = reshape(shape = var_461, x = linear_27_cast_fp16)[name = string("x_59_cast_fp16")];
+            tensor<int32, [4]> transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [1, 12, 57, 64]> transpose_86 = transpose(perm = transpose_86_perm_0, x = x_59_cast_fp16)[name = string("transpose_138")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_85 = transpose(perm = transpose_85_perm_0, x = x_55_cast_fp16)[name = string("transpose_139")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_84 = transpose(perm = transpose_84_perm_0, x = x_51_cast_fp16)[name = string("transpose_140")];
+            tensor<fp16, [1, 12, 57, 64]> attention_output_17_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_85, query = transpose_84, value = transpose_86)[name = string("attention_output_17_cast_fp16")];
+            tensor<int32, [4]> attention_output_19_perm_0 = const()[name = string("attention_output_19_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_467 = const()[name = string("op_467"), val = tensor<int32, [3]>([1, 57, 768])];
+            tensor<fp16, [1, 57, 12, 64]> attention_output_19_cast_fp16 = transpose(perm = attention_output_19_perm_0, x = attention_output_17_cast_fp16)[name = string("transpose_137")];
+            tensor<fp16, [1, 57, 768]> input_57_cast_fp16 = reshape(shape = var_467, x = attention_output_19_cast_fp16)[name = string("input_57_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_28_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_57_cast_fp16)[name = string("linear_28_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_59_cast_fp16 = add(x = hidden_states_9_cast_fp16, y = linear_28_cast_fp16)[name = string("input_59_cast_fp16")];
+            tensor<int32, [1]> input_61_axes_0 = const()[name = string("input_61_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> input_61_cast_fp16 = layer_norm(axes = input_61_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")];
+            tensor<fp16, [1, 57, 2048]> linear_29_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_61_cast_fp16)[name = string("linear_29_cast_fp16")];
+            string input_65_mode_0 = const()[name = string("input_65_mode_0"), val = string("TANH_APPROXIMATION")];
+            tensor<fp16, [1, 57, 2048]> input_65_cast_fp16 = gelu(mode = input_65_mode_0, x = linear_29_cast_fp16)[name = string("input_65_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_30_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_65_cast_fp16)[name = string("linear_30_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_67_cast_fp16 = add(x = linear_30_cast_fp16, y = input_61_cast_fp16)[name = string("input_67_cast_fp16")];
+            tensor<int32, [1]> hidden_states_11_axes_0 = const()[name = string("hidden_states_11_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> hidden_states_11_cast_fp16 = layer_norm(axes = hidden_states_11_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_67_cast_fp16)[name = string("hidden_states_11_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_31_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_11_cast_fp16)[name = string("linear_31_cast_fp16")];
+            tensor<int32, [4]> var_518 = const()[name = string("op_518"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_63_cast_fp16 = reshape(shape = var_518, x = linear_31_cast_fp16)[name = string("x_63_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_32_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_11_cast_fp16)[name = string("linear_32_cast_fp16")];
+            tensor<int32, [4]> var_527 = const()[name = string("op_527"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_67_cast_fp16 = reshape(shape = var_527, x = linear_32_cast_fp16)[name = string("x_67_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_33_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_11_cast_fp16)[name = string("linear_33_cast_fp16")];
+            tensor<int32, [4]> var_536 = const()[name = string("op_536"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_71_cast_fp16 = reshape(shape = var_536, x = linear_33_cast_fp16)[name = string("x_71_cast_fp16")];
+            tensor<int32, [4]> transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [1, 12, 57, 64]> transpose_89 = transpose(perm = transpose_89_perm_0, x = x_71_cast_fp16)[name = string("transpose_134")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_88 = transpose(perm = transpose_88_perm_0, x = x_67_cast_fp16)[name = string("transpose_135")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_87 = transpose(perm = transpose_87_perm_0, x = x_63_cast_fp16)[name = string("transpose_136")];
+            tensor<fp16, [1, 12, 57, 64]> attention_output_21_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_88, query = transpose_87, value = transpose_89)[name = string("attention_output_21_cast_fp16")];
+            tensor<int32, [4]> attention_output_23_perm_0 = const()[name = string("attention_output_23_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_542 = const()[name = string("op_542"), val = tensor<int32, [3]>([1, 57, 768])];
+            tensor<fp16, [1, 57, 12, 64]> attention_output_23_cast_fp16 = transpose(perm = attention_output_23_perm_0, x = attention_output_21_cast_fp16)[name = string("transpose_133")];
+            tensor<fp16, [1, 57, 768]> input_69_cast_fp16 = reshape(shape = var_542, x = attention_output_23_cast_fp16)[name = string("input_69_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_34_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_69_cast_fp16)[name = string("linear_34_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_71_cast_fp16 = add(x = hidden_states_11_cast_fp16, y = linear_34_cast_fp16)[name = string("input_71_cast_fp16")];
+            tensor<int32, [1]> input_73_axes_0 = const()[name = string("input_73_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> input_73_cast_fp16 = layer_norm(axes = input_73_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_71_cast_fp16)[name = string("input_73_cast_fp16")];
+            tensor<fp16, [1, 57, 2048]> linear_35_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_73_cast_fp16)[name = string("linear_35_cast_fp16")];
+            string input_77_mode_0 = const()[name = string("input_77_mode_0"), val = string("TANH_APPROXIMATION")];
+            tensor<fp16, [1, 57, 2048]> input_77_cast_fp16 = gelu(mode = input_77_mode_0, x = linear_35_cast_fp16)[name = string("input_77_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_36_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_77_cast_fp16)[name = string("linear_36_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_79_cast_fp16 = add(x = linear_36_cast_fp16, y = input_73_cast_fp16)[name = string("input_79_cast_fp16")];
+            tensor<int32, [1]> hidden_states_13_axes_0 = const()[name = string("hidden_states_13_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> hidden_states_13_cast_fp16 = layer_norm(axes = hidden_states_13_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("hidden_states_13_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_37_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_13_cast_fp16)[name = string("linear_37_cast_fp16")];
+            tensor<int32, [4]> var_593 = const()[name = string("op_593"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_75_cast_fp16 = reshape(shape = var_593, x = linear_37_cast_fp16)[name = string("x_75_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_38_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_13_cast_fp16)[name = string("linear_38_cast_fp16")];
+            tensor<int32, [4]> var_602 = const()[name = string("op_602"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_79_cast_fp16 = reshape(shape = var_602, x = linear_38_cast_fp16)[name = string("x_79_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_39_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_13_cast_fp16)[name = string("linear_39_cast_fp16")];
+            tensor<int32, [4]> var_611 = const()[name = string("op_611"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_83_cast_fp16 = reshape(shape = var_611, x = linear_39_cast_fp16)[name = string("x_83_cast_fp16")];
+            tensor<int32, [4]> transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [1, 12, 57, 64]> transpose_92 = transpose(perm = transpose_92_perm_0, x = x_83_cast_fp16)[name = string("transpose_130")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_91 = transpose(perm = transpose_91_perm_0, x = x_79_cast_fp16)[name = string("transpose_131")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_90 = transpose(perm = transpose_90_perm_0, x = x_75_cast_fp16)[name = string("transpose_132")];
+            tensor<fp16, [1, 12, 57, 64]> attention_output_25_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_91, query = transpose_90, value = transpose_92)[name = string("attention_output_25_cast_fp16")];
+            tensor<int32, [4]> attention_output_27_perm_0 = const()[name = string("attention_output_27_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_617 = const()[name = string("op_617"), val = tensor<int32, [3]>([1, 57, 768])];
+            tensor<fp16, [1, 57, 12, 64]> attention_output_27_cast_fp16 = transpose(perm = attention_output_27_perm_0, x = attention_output_25_cast_fp16)[name = string("transpose_129")];
+            tensor<fp16, [1, 57, 768]> input_81_cast_fp16 = reshape(shape = var_617, x = attention_output_27_cast_fp16)[name = string("input_81_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_40_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_81_cast_fp16)[name = string("linear_40_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_83_cast_fp16 = add(x = hidden_states_13_cast_fp16, y = linear_40_cast_fp16)[name = string("input_83_cast_fp16")];
+            tensor<int32, [1]> input_85_axes_0 = const()[name = string("input_85_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> input_85_cast_fp16 = layer_norm(axes = input_85_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")];
+            tensor<fp16, [1, 57, 2048]> linear_41_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_85_cast_fp16)[name = string("linear_41_cast_fp16")];
+            string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("TANH_APPROXIMATION")];
+            tensor<fp16, [1, 57, 2048]> input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_41_cast_fp16)[name = string("input_89_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_42_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_89_cast_fp16)[name = string("linear_42_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_91_cast_fp16 = add(x = linear_42_cast_fp16, y = input_85_cast_fp16)[name = string("input_91_cast_fp16")];
+            tensor<int32, [1]> hidden_states_15_axes_0 = const()[name = string("hidden_states_15_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> hidden_states_15_cast_fp16 = layer_norm(axes = hidden_states_15_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_91_cast_fp16)[name = string("hidden_states_15_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_43_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_15_cast_fp16)[name = string("linear_43_cast_fp16")];
+            tensor<int32, [4]> var_668 = const()[name = string("op_668"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_87_cast_fp16 = reshape(shape = var_668, x = linear_43_cast_fp16)[name = string("x_87_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_44_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_15_cast_fp16)[name = string("linear_44_cast_fp16")];
+            tensor<int32, [4]> var_677 = const()[name = string("op_677"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_91_cast_fp16 = reshape(shape = var_677, x = linear_44_cast_fp16)[name = string("x_91_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_45_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_15_cast_fp16)[name = string("linear_45_cast_fp16")];
+            tensor<int32, [4]> var_686 = const()[name = string("op_686"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_95_cast_fp16 = reshape(shape = var_686, x = linear_45_cast_fp16)[name = string("x_95_cast_fp16")];
+            tensor<int32, [4]> transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [1, 12, 57, 64]> transpose_95 = transpose(perm = transpose_95_perm_0, x = x_95_cast_fp16)[name = string("transpose_126")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_94 = transpose(perm = transpose_94_perm_0, x = x_91_cast_fp16)[name = string("transpose_127")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_93 = transpose(perm = transpose_93_perm_0, x = x_87_cast_fp16)[name = string("transpose_128")];
+            tensor<fp16, [1, 12, 57, 64]> attention_output_29_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_94, query = transpose_93, value = transpose_95)[name = string("attention_output_29_cast_fp16")];
+            tensor<int32, [4]> attention_output_31_perm_0 = const()[name = string("attention_output_31_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_692 = const()[name = string("op_692"), val = tensor<int32, [3]>([1, 57, 768])];
+            tensor<fp16, [1, 57, 12, 64]> attention_output_31_cast_fp16 = transpose(perm = attention_output_31_perm_0, x = attention_output_29_cast_fp16)[name = string("transpose_125")];
+            tensor<fp16, [1, 57, 768]> input_93_cast_fp16 = reshape(shape = var_692, x = attention_output_31_cast_fp16)[name = string("input_93_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_46_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_93_cast_fp16)[name = string("linear_46_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_95_cast_fp16 = add(x = hidden_states_15_cast_fp16, y = linear_46_cast_fp16)[name = string("input_95_cast_fp16")];
+            tensor<int32, [1]> input_97_axes_0 = const()[name = string("input_97_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> input_97_cast_fp16 = layer_norm(axes = input_97_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_95_cast_fp16)[name = string("input_97_cast_fp16")];
+            tensor<fp16, [1, 57, 2048]> linear_47_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_97_cast_fp16)[name = string("linear_47_cast_fp16")];
+            string input_101_mode_0 = const()[name = string("input_101_mode_0"), val = string("TANH_APPROXIMATION")];
+            tensor<fp16, [1, 57, 2048]> input_101_cast_fp16 = gelu(mode = input_101_mode_0, x = linear_47_cast_fp16)[name = string("input_101_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_48_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_101_cast_fp16)[name = string("linear_48_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_103_cast_fp16 = add(x = linear_48_cast_fp16, y = input_97_cast_fp16)[name = string("input_103_cast_fp16")];
+            tensor<int32, [1]> hidden_states_17_axes_0 = const()[name = string("hidden_states_17_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> hidden_states_17_cast_fp16 = layer_norm(axes = hidden_states_17_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_103_cast_fp16)[name = string("hidden_states_17_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_49_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_17_cast_fp16)[name = string("linear_49_cast_fp16")];
+            tensor<int32, [4]> var_743 = const()[name = string("op_743"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_99_cast_fp16 = reshape(shape = var_743, x = linear_49_cast_fp16)[name = string("x_99_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_50_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_17_cast_fp16)[name = string("linear_50_cast_fp16")];
+            tensor<int32, [4]> var_752 = const()[name = string("op_752"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_103_cast_fp16 = reshape(shape = var_752, x = linear_50_cast_fp16)[name = string("x_103_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_51_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_17_cast_fp16)[name = string("linear_51_cast_fp16")];
+            tensor<int32, [4]> var_761 = const()[name = string("op_761"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_107_cast_fp16 = reshape(shape = var_761, x = linear_51_cast_fp16)[name = string("x_107_cast_fp16")];
+            tensor<int32, [4]> transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [1, 12, 57, 64]> transpose_98 = transpose(perm = transpose_98_perm_0, x = x_107_cast_fp16)[name = string("transpose_122")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_97 = transpose(perm = transpose_97_perm_0, x = x_103_cast_fp16)[name = string("transpose_123")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_96 = transpose(perm = transpose_96_perm_0, x = x_99_cast_fp16)[name = string("transpose_124")];
+            tensor<fp16, [1, 12, 57, 64]> attention_output_33_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_97, query = transpose_96, value = transpose_98)[name = string("attention_output_33_cast_fp16")];
+            tensor<int32, [4]> attention_output_35_perm_0 = const()[name = string("attention_output_35_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_767 = const()[name = string("op_767"), val = tensor<int32, [3]>([1, 57, 768])];
+            tensor<fp16, [1, 57, 12, 64]> attention_output_35_cast_fp16 = transpose(perm = attention_output_35_perm_0, x = attention_output_33_cast_fp16)[name = string("transpose_121")];
+            tensor<fp16, [1, 57, 768]> input_105_cast_fp16 = reshape(shape = var_767, x = attention_output_35_cast_fp16)[name = string("input_105_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_52_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_105_cast_fp16)[name = string("linear_52_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_107_cast_fp16 = add(x = hidden_states_17_cast_fp16, y = linear_52_cast_fp16)[name = string("input_107_cast_fp16")];
+            tensor<int32, [1]> input_109_axes_0 = const()[name = string("input_109_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> input_109_cast_fp16 = layer_norm(axes = input_109_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_107_cast_fp16)[name = string("input_109_cast_fp16")];
+            tensor<fp16, [1, 57, 2048]> linear_53_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_109_cast_fp16)[name = string("linear_53_cast_fp16")];
+            string input_113_mode_0 = const()[name = string("input_113_mode_0"), val = string("TANH_APPROXIMATION")];
+            tensor<fp16, [1, 57, 2048]> input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = linear_53_cast_fp16)[name = string("input_113_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_54_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_113_cast_fp16)[name = string("linear_54_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_115_cast_fp16 = add(x = linear_54_cast_fp16, y = input_109_cast_fp16)[name = string("input_115_cast_fp16")];
+            tensor<int32, [1]> hidden_states_19_axes_0 = const()[name = string("hidden_states_19_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> hidden_states_19_cast_fp16 = layer_norm(axes = hidden_states_19_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("hidden_states_19_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_55_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_19_cast_fp16)[name = string("linear_55_cast_fp16")];
+            tensor<int32, [4]> var_818 = const()[name = string("op_818"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_111_cast_fp16 = reshape(shape = var_818, x = linear_55_cast_fp16)[name = string("x_111_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_56_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_19_cast_fp16)[name = string("linear_56_cast_fp16")];
+            tensor<int32, [4]> var_827 = const()[name = string("op_827"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_115_cast_fp16 = reshape(shape = var_827, x = linear_56_cast_fp16)[name = string("x_115_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_57_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_19_cast_fp16)[name = string("linear_57_cast_fp16")];
+            tensor<int32, [4]> var_836 = const()[name = string("op_836"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_119_cast_fp16 = reshape(shape = var_836, x = linear_57_cast_fp16)[name = string("x_119_cast_fp16")];
+            tensor<int32, [4]> transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [1, 12, 57, 64]> transpose_101 = transpose(perm = transpose_101_perm_0, x = x_119_cast_fp16)[name = string("transpose_118")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_100 = transpose(perm = transpose_100_perm_0, x = x_115_cast_fp16)[name = string("transpose_119")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_99 = transpose(perm = transpose_99_perm_0, x = x_111_cast_fp16)[name = string("transpose_120")];
+            tensor<fp16, [1, 12, 57, 64]> attention_output_37_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_100, query = transpose_99, value = transpose_101)[name = string("attention_output_37_cast_fp16")];
+            tensor<int32, [4]> attention_output_39_perm_0 = const()[name = string("attention_output_39_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_842 = const()[name = string("op_842"), val = tensor<int32, [3]>([1, 57, 768])];
+            tensor<fp16, [1, 57, 12, 64]> attention_output_39_cast_fp16 = transpose(perm = attention_output_39_perm_0, x = attention_output_37_cast_fp16)[name = string("transpose_117")];
+            tensor<fp16, [1, 57, 768]> input_117_cast_fp16 = reshape(shape = var_842, x = attention_output_39_cast_fp16)[name = string("input_117_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_58_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_117_cast_fp16)[name = string("linear_58_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_119_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = linear_58_cast_fp16)[name = string("input_119_cast_fp16")];
+            tensor<int32, [1]> input_121_axes_0 = const()[name = string("input_121_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> input_121_cast_fp16 = layer_norm(axes = input_121_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_119_cast_fp16)[name = string("input_121_cast_fp16")];
+            tensor<fp16, [1, 57, 2048]> linear_59_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_121_cast_fp16)[name = string("linear_59_cast_fp16")];
+            string input_125_mode_0 = const()[name = string("input_125_mode_0"), val = string("TANH_APPROXIMATION")];
+            tensor<fp16, [1, 57, 2048]> input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = linear_59_cast_fp16)[name = string("input_125_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_60_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_125_cast_fp16)[name = string("linear_60_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_127_cast_fp16 = add(x = linear_60_cast_fp16, y = input_121_cast_fp16)[name = string("input_127_cast_fp16")];
+            tensor<int32, [1]> hidden_states_21_axes_0 = const()[name = string("hidden_states_21_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> hidden_states_21_cast_fp16 = layer_norm(axes = hidden_states_21_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_127_cast_fp16)[name = string("hidden_states_21_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_61_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_21_cast_fp16)[name = string("linear_61_cast_fp16")];
+            tensor<int32, [4]> var_893 = const()[name = string("op_893"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_123_cast_fp16 = reshape(shape = var_893, x = linear_61_cast_fp16)[name = string("x_123_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_62_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_21_cast_fp16)[name = string("linear_62_cast_fp16")];
+            tensor<int32, [4]> var_902 = const()[name = string("op_902"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_127_cast_fp16 = reshape(shape = var_902, x = linear_62_cast_fp16)[name = string("x_127_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_63_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_21_cast_fp16)[name = string("linear_63_cast_fp16")];
+            tensor<int32, [4]> var_911 = const()[name = string("op_911"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_131_cast_fp16 = reshape(shape = var_911, x = linear_63_cast_fp16)[name = string("x_131_cast_fp16")];
+            tensor<int32, [4]> transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [1, 12, 57, 64]> transpose_104 = transpose(perm = transpose_104_perm_0, x = x_131_cast_fp16)[name = string("transpose_114")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_103 = transpose(perm = transpose_103_perm_0, x = x_127_cast_fp16)[name = string("transpose_115")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_102 = transpose(perm = transpose_102_perm_0, x = x_123_cast_fp16)[name = string("transpose_116")];
+            tensor<fp16, [1, 12, 57, 64]> attention_output_41_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_103, query = transpose_102, value = transpose_104)[name = string("attention_output_41_cast_fp16")];
+            tensor<int32, [4]> attention_output_43_perm_0 = const()[name = string("attention_output_43_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_917 = const()[name = string("op_917"), val = tensor<int32, [3]>([1, 57, 768])];
+            tensor<fp16, [1, 57, 12, 64]> attention_output_43_cast_fp16 = transpose(perm = attention_output_43_perm_0, x = attention_output_41_cast_fp16)[name = string("transpose_113")];
+            tensor<fp16, [1, 57, 768]> input_129_cast_fp16 = reshape(shape = var_917, x = attention_output_43_cast_fp16)[name = string("input_129_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_64_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_129_cast_fp16)[name = string("linear_64_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_131_cast_fp16 = add(x = hidden_states_21_cast_fp16, y = linear_64_cast_fp16)[name = string("input_131_cast_fp16")];
+            tensor<int32, [1]> input_133_axes_0 = const()[name = string("input_133_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> input_133_cast_fp16 = layer_norm(axes = input_133_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_131_cast_fp16)[name = string("input_133_cast_fp16")];
+            tensor<fp16, [1, 57, 2048]> linear_65_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_133_cast_fp16)[name = string("linear_65_cast_fp16")];
+            string input_137_mode_0 = const()[name = string("input_137_mode_0"), val = string("TANH_APPROXIMATION")];
+            tensor<fp16, [1, 57, 2048]> input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = linear_65_cast_fp16)[name = string("input_137_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_66_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_137_cast_fp16)[name = string("linear_66_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_139_cast_fp16 = add(x = linear_66_cast_fp16, y = input_133_cast_fp16)[name = string("input_139_cast_fp16")];
+            tensor<int32, [1]> hidden_states_axes_0 = const()[name = string("hidden_states_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> hidden_states_cast_fp16 = layer_norm(axes = hidden_states_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("hidden_states_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_67_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight_to_fp16, x = hidden_states_cast_fp16)[name = string("linear_67_cast_fp16")];
+            tensor<int32, [4]> var_968 = const()[name = string("op_968"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_135_cast_fp16 = reshape(shape = var_968, x = linear_67_cast_fp16)[name = string("x_135_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_68_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight_to_fp16, x = hidden_states_cast_fp16)[name = string("linear_68_cast_fp16")];
+            tensor<int32, [4]> var_977 = const()[name = string("op_977"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_139_cast_fp16 = reshape(shape = var_977, x = linear_68_cast_fp16)[name = string("x_139_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_69_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight_to_fp16, x = hidden_states_cast_fp16)[name = string("linear_69_cast_fp16")];
+            tensor<int32, [4]> var_986 = const()[name = string("op_986"), val = tensor<int32, [4]>([1, 57, 12, 64])];
+            tensor<fp16, [1, 57, 12, 64]> x_cast_fp16 = reshape(shape = var_986, x = linear_69_cast_fp16)[name = string("x_cast_fp16")];
+            tensor<int32, [4]> transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [4]> transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<fp16, [1, 12, 57, 64]> transpose_107 = transpose(perm = transpose_107_perm_0, x = x_cast_fp16)[name = string("transpose_110")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_106 = transpose(perm = transpose_106_perm_0, x = x_139_cast_fp16)[name = string("transpose_111")];
+            tensor<fp16, [1, 12, 57, 64]> transpose_105 = transpose(perm = transpose_105_perm_0, x = x_135_cast_fp16)[name = string("transpose_112")];
+            tensor<fp16, [1, 12, 57, 64]> attention_output_45_cast_fp16 = scaled_dot_product_attention(attn_mask = attention_mask_cast_fp16, key = transpose_106, query = transpose_105, value = transpose_107)[name = string("attention_output_45_cast_fp16")];
+            tensor<int32, [4]> attention_output_perm_0 = const()[name = string("attention_output_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> var_992 = const()[name = string("op_992"), val = tensor<int32, [3]>([1, 57, 768])];
+            tensor<fp16, [1, 57, 12, 64]> attention_output_cast_fp16 = transpose(perm = attention_output_perm_0, x = attention_output_45_cast_fp16)[name = string("transpose_109")];
+            tensor<fp16, [1, 57, 768]> input_141_cast_fp16 = reshape(shape = var_992, x = attention_output_cast_fp16)[name = string("input_141_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_70_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight_to_fp16, x = input_141_cast_fp16)[name = string("linear_70_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_143_cast_fp16 = add(x = hidden_states_cast_fp16, y = linear_70_cast_fp16)[name = string("input_143_cast_fp16")];
+            tensor<int32, [1]> input_145_axes_0 = const()[name = string("input_145_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> input_145_cast_fp16 = layer_norm(axes = input_145_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight_to_fp16, x = input_143_cast_fp16)[name = string("input_145_cast_fp16")];
+            tensor<fp16, [1, 57, 2048]> linear_71_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight_to_fp16, x = input_145_cast_fp16)[name = string("linear_71_cast_fp16")];
+            string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("TANH_APPROXIMATION")];
+            tensor<fp16, [1, 57, 2048]> input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_71_cast_fp16)[name = string("input_149_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> linear_72_cast_fp16 = linear(bias = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias_to_fp16, weight = bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight_to_fp16, x = input_149_cast_fp16)[name = string("linear_72_cast_fp16")];
+            tensor<fp16, [1, 57, 768]> input_151_cast_fp16 = add(x = linear_72_cast_fp16, y = input_145_cast_fp16)[name = string("input_151_cast_fp16")];
+            tensor<int32, [1]> sequence_output_axes_0 = const()[name = string("sequence_output_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 57, 768]> sequence_output = layer_norm(axes = sequence_output_axes_0, beta = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias_to_fp16, epsilon = var_118_to_fp16, gamma = bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight_to_fp16, x = input_151_cast_fp16)[name = string("sequence_output_cast_fp16")];
+            tensor<fp16, [512, 768]> bert_encoder_weight_to_fp16 = const()[name = string("bert_encoder_weight_to_fp16"), val = tensor<fp16, [512, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11302912)))];
+            tensor<fp16, [512]> bert_encoder_bias_to_fp16 = const()[name = string("bert_encoder_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12089408)))];
+            tensor<fp16, [1, 57, 512]> linear_73_cast_fp16 = linear(bias = bert_encoder_bias_to_fp16, weight = bert_encoder_weight_to_fp16, x = sequence_output)[name = string("linear_73_cast_fp16")];
+            tensor<int32, [3]> var_1030_perm_0 = const()[name = string("op_1030_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
+            tensor<fp16, [1, 512, 57]> var_1030 = transpose(perm = var_1030_perm_0, x = linear_73_cast_fp16)[name = string("transpose_108")];
+        } -> (sequence_output, var_1030);
+}

iteration_3/compiled/bert_fp16.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc4a9fb3870729f9572b0830993351524b04b99eba6cab982cef2a17507d9ba0
+size 12090496

iteration_3/compiled/decoder_pre_fp16.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b5d73b4fd5e456dd180b2e38abf8348b265ebe3fca95c7c671da76d64e1d0cd
+size 243

iteration_3/compiled/decoder_pre_fp16.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:42eac5d6b57aa155f4be14e845e550857b4a22289034b26eb37184ea3e80a85a
+size 490

iteration_3/compiled/decoder_pre_fp16.mlmodelc/metadata.json

@@ -0,0 +1,111 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "var_436",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.instanceNorm" : 10,
+      "Ios18.linear" : 10,
+      "Ios18.convTranspose" : 1,
+      "Ios18.leakyRelu" : 10,
+      "Ios18.expandDims" : 3,
+      "Ios18.conv" : 18,
+      "Ios18.concat" : 5,
+      "Ios18.add" : 25,
+      "UpsampleNearestNeighbor" : 1,
+      "Ios18.sliceByIndex" : 1,
+      "Ios18.cast" : 4,
+      "Ios18.squeeze" : 1,
+      "Ios18.reshape" : 10,
+      "Split" : 10,
+      "Ios18.mul" : 15
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-05-08",
+      "com.github.apple.coremltools.source" : "torch==2.11.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "inputSchema" : [
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 512 × 1...2048",
+        "shapeRange" : "[[1, 1], [512, 512], [1, 2048]]",
+        "formattedType" : "MultiArray (Float32 1 × 512 × 147)",
+        "type" : "MultiArray",
+        "shape" : "[1, 512, 147]",
+        "name" : "asr",
+        "shortDescription" : ""
+      },
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 2...4096",
+        "shapeRange" : "[[1, 1], [2, 4096]]",
+        "formattedType" : "MultiArray (Float32 1 × 294)",
+        "type" : "MultiArray",
+        "shape" : "[1, 294]",
+        "name" : "f0_pred",
+        "shortDescription" : ""
+      },
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 2...4096",
+        "shapeRange" : "[[1, 1], [2, 4096]]",
+        "formattedType" : "MultiArray (Float32 1 × 294)",
+        "type" : "MultiArray",
+        "shape" : "[1, 294]",
+        "name" : "n_pred",
+        "shortDescription" : ""
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 128)",
+        "shortDescription" : "",
+        "shape" : "[1, 128]",
+        "name" : "ref",
+        "type" : "MultiArray"
+      }
+    ],
+    "generatedClassName" : "decoder_pre_fp16",
+    "method" : "predict"
+  }
+]

iteration_3/compiled/decoder_pre_fp16.mlmodelc/model.mil

@@ -0,0 +1,357 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 512, ?]> asr, tensor<fp32, [1, ?]> f0_pred, tensor<fp32, [1, ?]> n_pred, tensor<fp32, [1, 128]> ref) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"asr", [1, 512, 147]}, {"f0_pred", [1, 294]}, {"n_pred", [1, 294]}}), ("RangeDims", {{"asr", [[1, 1], [512, 512], [1, 2048]]}, {"f0_pred", [[1, 1], [2, 4096]]}, {"n_pred", [[1, 1], [2, 4096]]}})))] {
+            tensor<int32, [1]> input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor<int32, [1]>([1])];
+            string f0_pred_to_fp16_dtype_0 = const()[name = string("f0_pred_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, ?]> f0_pred_to_fp16 = cast(dtype = f0_pred_to_fp16_dtype_0, x = f0_pred)[name = string("cast_24")];
+            tensor<fp16, [1, 1, ?]> input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = f0_pred_to_fp16)[name = string("input_1_cast_fp16")];
+            string F0_pad_type_0 = const()[name = string("F0_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> F0_pad_0 = const()[name = string("F0_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> F0_strides_0 = const()[name = string("F0_strides_0"), val = tensor<int32, [1]>([2])];
+            tensor<int32, [1]> F0_dilations_0 = const()[name = string("F0_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 F0_groups_0 = const()[name = string("F0_groups_0"), val = int32(1)];
+            tensor<fp16, [1, 1, 3]> decoder_F0_conv_weight_to_fp16 = const()[name = string("decoder_F0_conv_weight_to_fp16"), val = tensor<fp16, [1, 1, 3]>([[[0x1.568p-5, -0x1.864p-5, -0x1.504p-4]]])];
+            tensor<fp16, [1]> decoder_F0_conv_bias_to_fp16 = const()[name = string("decoder_F0_conv_bias_to_fp16"), val = tensor<fp16, [1]>([0x1.844p-2])];
+            tensor<fp16, [1, 1, ?]> F0_cast_fp16 = conv(bias = decoder_F0_conv_bias_to_fp16, dilations = F0_dilations_0, groups = F0_groups_0, pad = F0_pad_0, pad_type = F0_pad_type_0, strides = F0_strides_0, weight = decoder_F0_conv_weight_to_fp16, x = input_1_cast_fp16)[name = string("F0_cast_fp16")];
+            tensor<int32, [1]> input_3_axes_0 = const()[name = string("input_3_axes_0"), val = tensor<int32, [1]>([1])];
+            string n_pred_to_fp16_dtype_0 = const()[name = string("n_pred_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, ?]> n_pred_to_fp16 = cast(dtype = n_pred_to_fp16_dtype_0, x = n_pred)[name = string("cast_23")];
+            tensor<fp16, [1, 1, ?]> input_3_cast_fp16 = expand_dims(axes = input_3_axes_0, x = n_pred_to_fp16)[name = string("input_3_cast_fp16")];
+            string N_pad_type_0 = const()[name = string("N_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> N_pad_0 = const()[name = string("N_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> N_strides_0 = const()[name = string("N_strides_0"), val = tensor<int32, [1]>([2])];
+            tensor<int32, [1]> N_dilations_0 = const()[name = string("N_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 N_groups_0 = const()[name = string("N_groups_0"), val = int32(1)];
+            tensor<fp16, [1, 1, 3]> decoder_N_conv_weight_to_fp16 = const()[name = string("decoder_N_conv_weight_to_fp16"), val = tensor<fp16, [1, 1, 3]>([[[0x1.1d4p-5, -0x1.71p-1, -0x1.89cp-1]]])];
+            tensor<fp16, [1]> decoder_N_conv_bias_to_fp16 = const()[name = string("decoder_N_conv_bias_to_fp16"), val = tensor<fp16, [1]>([0x1.44cp-1])];
+            tensor<fp16, [1, 1, ?]> N_cast_fp16 = conv(bias = decoder_N_conv_bias_to_fp16, dilations = N_dilations_0, groups = N_groups_0, pad = N_pad_0, pad_type = N_pad_type_0, strides = N_strides_0, weight = decoder_N_conv_weight_to_fp16, x = input_3_cast_fp16)[name = string("N_cast_fp16")];
+            int32 var_54 = const()[name = string("op_54"), val = int32(1)];
+            bool input_7_interleave_0 = const()[name = string("input_7_interleave_0"), val = bool(false)];
+            string asr_to_fp16_dtype_0 = const()[name = string("asr_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 512, ?]> asr_to_fp16 = cast(dtype = asr_to_fp16_dtype_0, x = asr)[name = string("cast_22")];
+            tensor<fp16, [1, 514, ?]> input_7_cast_fp16 = concat(axis = var_54, interleave = input_7_interleave_0, values = (asr_to_fp16, F0_cast_fp16, N_cast_fp16))[name = string("input_7_cast_fp16")];
+            string ref_to_fp16_dtype_0 = const()[name = string("ref_to_fp16_dtype_0"), val = string("fp16")];
+            fp32 var_61 = const()[name = string("op_61"), val = fp32(0x1.99999ap-3)];
+            tensor<fp16, [1028, 128]> decoder_encode_norm1_fc_weight_to_fp16 = const()[name = string("decoder_encode_norm1_fc_weight_to_fp16"), val = tensor<fp16, [1028, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [1028]> decoder_encode_norm1_fc_bias_to_fp16 = const()[name = string("decoder_encode_norm1_fc_bias_to_fp16"), val = tensor<fp16, [1028]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(263296)))];
+            tensor<fp16, [1, 128]> ref_to_fp16 = cast(dtype = ref_to_fp16_dtype_0, x = ref)[name = string("cast_21")];
+            tensor<fp16, [1, 1028]> linear_0_cast_fp16 = linear(bias = decoder_encode_norm1_fc_bias_to_fp16, weight = decoder_encode_norm1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_0_cast_fp16")];
+            tensor<int32, [3]> var_79 = const()[name = string("op_79"), val = tensor<int32, [3]>([1, 1028, 1])];
+            tensor<fp16, [1, 1028, 1]> h_3_cast_fp16 = reshape(shape = var_79, x = linear_0_cast_fp16)[name = string("h_3_cast_fp16")];
+            tensor<int32, [2]> var_81_split_sizes_0 = const()[name = string("op_81_split_sizes_0"), val = tensor<int32, [2]>([514, 514])];
+            int32 var_81_axis_0 = const()[name = string("op_81_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 514, 1]> var_81_cast_fp16_0, tensor<fp16, [1, 514, 1]> var_81_cast_fp16_1 = split(axis = var_81_axis_0, split_sizes = var_81_split_sizes_0, x = h_3_cast_fp16)[name = string("op_81_cast_fp16")];
+            fp16 var_83_promoted_to_fp16 = const()[name = string("op_83_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 514, 1]> var_84_cast_fp16 = add(x = var_81_cast_fp16_0, y = var_83_promoted_to_fp16)[name = string("op_84_cast_fp16")];
+            fp16 var_64_to_fp16 = const()[name = string("op_64_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 514, ?]> var_85_cast_fp16 = instance_norm(epsilon = var_64_to_fp16, x = input_7_cast_fp16)[name = string("op_85_cast_fp16")];
+            tensor<fp16, [1, 514, ?]> var_86_cast_fp16 = mul(x = var_84_cast_fp16, y = var_85_cast_fp16)[name = string("op_86_cast_fp16")];
+            tensor<fp16, [1, 514, ?]> input_9_cast_fp16 = add(x = var_86_cast_fp16, y = var_81_cast_fp16_1)[name = string("input_9_cast_fp16")];
+            tensor<fp16, [1, 514, ?]> input_11_cast_fp16 = leaky_relu(alpha = var_61, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")];
+            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 514, 3]> decoder_encode_conv1_weight_to_fp16 = const()[name = string("decoder_encode_conv1_weight_to_fp16"), val = tensor<fp16, [1024, 514, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265472)))];
+            tensor<fp16, [1024]> decoder_encode_conv1_bias_to_fp16 = const()[name = string("decoder_encode_conv1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3423552)))];
+            tensor<fp16, [1, 1024, ?]> input_13_cast_fp16 = conv(bias = decoder_encode_conv1_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = decoder_encode_conv1_weight_to_fp16, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")];
+            tensor<fp16, [2048, 128]> decoder_encode_norm2_fc_weight_to_fp16 = const()[name = string("decoder_encode_norm2_fc_weight_to_fp16"), val = tensor<fp16, [2048, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3425664)))];
+            tensor<fp16, [2048]> decoder_encode_norm2_fc_bias_to_fp16 = const()[name = string("decoder_encode_norm2_fc_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3950016)))];
+            tensor<fp16, [1, 2048]> linear_1_cast_fp16 = linear(bias = decoder_encode_norm2_fc_bias_to_fp16, weight = decoder_encode_norm2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_1_cast_fp16")];
+            tensor<int32, [3]> var_102 = const()[name = string("op_102"), val = tensor<int32, [3]>([1, 2048, 1])];
+            tensor<fp16, [1, 2048, 1]> h_7_cast_fp16 = reshape(shape = var_102, x = linear_1_cast_fp16)[name = string("h_7_cast_fp16")];
+            tensor<int32, [2]> var_104_split_sizes_0 = const()[name = string("op_104_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
+            int32 var_104_axis_0 = const()[name = string("op_104_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 1]> var_104_cast_fp16_0, tensor<fp16, [1, 1024, 1]> var_104_cast_fp16_1 = split(axis = var_104_axis_0, split_sizes = var_104_split_sizes_0, x = h_7_cast_fp16)[name = string("op_104_cast_fp16")];
+            fp16 var_106_promoted_to_fp16 = const()[name = string("op_106_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1024, 1]> var_107_cast_fp16 = add(x = var_104_cast_fp16_0, y = var_106_promoted_to_fp16)[name = string("op_107_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_108_cast_fp16 = instance_norm(epsilon = var_64_to_fp16, x = input_13_cast_fp16)[name = string("op_108_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_109_cast_fp16 = mul(x = var_107_cast_fp16, y = var_108_cast_fp16)[name = string("op_109_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_15_cast_fp16 = add(x = var_109_cast_fp16, y = var_104_cast_fp16_1)[name = string("input_15_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_17_cast_fp16 = leaky_relu(alpha = var_61, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")];
+            string out_1_pad_type_0 = const()[name = string("out_1_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> out_1_pad_0 = const()[name = string("out_1_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_1_strides_0 = const()[name = string("out_1_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_1_dilations_0 = const()[name = string("out_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 out_1_groups_0 = const()[name = string("out_1_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 3]> decoder_encode_conv2_weight_to_fp16 = const()[name = string("decoder_encode_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3954176)))];
+            tensor<fp16, [1024]> decoder_encode_conv2_bias_to_fp16 = const()[name = string("decoder_encode_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10245696)))];
+            tensor<fp16, [1, 1024, ?]> out_1_cast_fp16 = conv(bias = decoder_encode_conv2_bias_to_fp16, dilations = out_1_dilations_0, groups = out_1_groups_0, pad = out_1_pad_0, pad_type = out_1_pad_type_0, strides = out_1_strides_0, weight = decoder_encode_conv2_weight_to_fp16, x = input_17_cast_fp16)[name = string("out_1_cast_fp16")];
+            string var_124_pad_type_0 = const()[name = string("op_124_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_124_strides_0 = const()[name = string("op_124_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_124_pad_0 = const()[name = string("op_124_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_124_dilations_0 = const()[name = string("op_124_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_124_groups_0 = const()[name = string("op_124_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 514, 1]> decoder_encode_conv1x1_weight_to_fp16 = const()[name = string("decoder_encode_conv1x1_weight_to_fp16"), val = tensor<fp16, [1024, 514, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10247808)))];
+            tensor<fp16, [1, 1024, ?]> var_124_cast_fp16 = conv(dilations = var_124_dilations_0, groups = var_124_groups_0, pad = var_124_pad_0, pad_type = var_124_pad_type_0, strides = var_124_strides_0, weight = decoder_encode_conv1x1_weight_to_fp16, x = input_7_cast_fp16)[name = string("op_124_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_125_cast_fp16 = add(x = out_1_cast_fp16, y = var_124_cast_fp16)[name = string("op_125_cast_fp16")];
+            fp16 _inversed_x_1_y_0_to_fp16 = const()[name = string("_inversed_x_1_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
+            tensor<fp16, [1, 1024, ?]> _inversed_x_1_cast_fp16 = mul(x = var_125_cast_fp16, y = _inversed_x_1_y_0_to_fp16)[name = string("_inversed_x_1_cast_fp16")];
+            string asr_res_1_pad_type_0 = const()[name = string("asr_res_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> asr_res_1_strides_0 = const()[name = string("asr_res_1_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> asr_res_1_pad_0 = const()[name = string("asr_res_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> asr_res_1_dilations_0 = const()[name = string("asr_res_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 asr_res_1_groups_0 = const()[name = string("asr_res_1_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 512, 1]> decoder_asr_res_0_weight_to_fp16 = const()[name = string("decoder_asr_res_0_weight_to_fp16"), val = tensor<fp16, [64, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11300544)))];
+            tensor<fp16, [64]> decoder_asr_res_0_bias_to_fp16 = const()[name = string("decoder_asr_res_0_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11366144)))];
+            tensor<fp16, [1, 64, ?]> asr_res_1_cast_fp16 = conv(bias = decoder_asr_res_0_bias_to_fp16, dilations = asr_res_1_dilations_0, groups = asr_res_1_groups_0, pad = asr_res_1_pad_0, pad_type = asr_res_1_pad_type_0, strides = asr_res_1_strides_0, weight = decoder_asr_res_0_weight_to_fp16, x = asr_to_fp16)[name = string("asr_res_1_cast_fp16")];
+            int32 var_141 = const()[name = string("op_141"), val = int32(1)];
+            bool input_19_interleave_0 = const()[name = string("input_19_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 1090, ?]> input_19_cast_fp16 = concat(axis = var_141, interleave = input_19_interleave_0, values = (_inversed_x_1_cast_fp16, asr_res_1_cast_fp16, F0_cast_fp16, N_cast_fp16))[name = string("input_19_cast_fp16")];
+            fp32 var_144 = const()[name = string("op_144"), val = fp32(0x1.99999ap-3)];
+            tensor<fp16, [2180, 128]> decoder_decode_0_norm1_fc_weight_to_fp16 = const()[name = string("decoder_decode_0_norm1_fc_weight_to_fp16"), val = tensor<fp16, [2180, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11366336)))];
+            tensor<fp16, [2180]> decoder_decode_0_norm1_fc_bias_to_fp16 = const()[name = string("decoder_decode_0_norm1_fc_bias_to_fp16"), val = tensor<fp16, [2180]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11924480)))];
+            tensor<fp16, [1, 2180]> linear_2_cast_fp16 = linear(bias = decoder_decode_0_norm1_fc_bias_to_fp16, weight = decoder_decode_0_norm1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_2_cast_fp16")];
+            tensor<int32, [3]> var_162 = const()[name = string("op_162"), val = tensor<int32, [3]>([1, 2180, 1])];
+            tensor<fp16, [1, 2180, 1]> h_11_cast_fp16 = reshape(shape = var_162, x = linear_2_cast_fp16)[name = string("h_11_cast_fp16")];
+            tensor<int32, [2]> var_164_split_sizes_0 = const()[name = string("op_164_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
+            int32 var_164_axis_0 = const()[name = string("op_164_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1090, 1]> var_164_cast_fp16_0, tensor<fp16, [1, 1090, 1]> var_164_cast_fp16_1 = split(axis = var_164_axis_0, split_sizes = var_164_split_sizes_0, x = h_11_cast_fp16)[name = string("op_164_cast_fp16")];
+            fp16 var_166_promoted_to_fp16 = const()[name = string("op_166_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1090, 1]> var_167_cast_fp16 = add(x = var_164_cast_fp16_0, y = var_166_promoted_to_fp16)[name = string("op_167_cast_fp16")];
+            fp16 var_147_to_fp16 = const()[name = string("op_147_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 1090, ?]> var_168_cast_fp16 = instance_norm(epsilon = var_147_to_fp16, x = input_19_cast_fp16)[name = string("op_168_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> var_169_cast_fp16 = mul(x = var_167_cast_fp16, y = var_168_cast_fp16)[name = string("op_169_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_21_cast_fp16 = add(x = var_169_cast_fp16, y = var_164_cast_fp16_1)[name = string("input_21_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_23_cast_fp16 = leaky_relu(alpha = var_144, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")];
+            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1090, 3]> decoder_decode_0_conv1_weight_to_fp16 = const()[name = string("decoder_decode_0_conv1_weight_to_fp16"), val = tensor<fp16, [1024, 1090, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11928960)))];
+            tensor<fp16, [1024]> decoder_decode_0_conv1_bias_to_fp16 = const()[name = string("decoder_decode_0_conv1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18625984)))];
+            tensor<fp16, [1, 1024, ?]> input_25_cast_fp16 = conv(bias = decoder_decode_0_conv1_bias_to_fp16, 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 = decoder_decode_0_conv1_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")];
+            tensor<fp16, [2048, 128]> decoder_decode_0_norm2_fc_weight_to_fp16 = const()[name = string("decoder_decode_0_norm2_fc_weight_to_fp16"), val = tensor<fp16, [2048, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18628096)))];
+            tensor<fp16, [2048]> decoder_decode_0_norm2_fc_bias_to_fp16 = const()[name = string("decoder_decode_0_norm2_fc_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19152448)))];
+            tensor<fp16, [1, 2048]> linear_3_cast_fp16 = linear(bias = decoder_decode_0_norm2_fc_bias_to_fp16, weight = decoder_decode_0_norm2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_3_cast_fp16")];
+            tensor<int32, [3]> var_185 = const()[name = string("op_185"), val = tensor<int32, [3]>([1, 2048, 1])];
+            tensor<fp16, [1, 2048, 1]> h_15_cast_fp16 = reshape(shape = var_185, x = linear_3_cast_fp16)[name = string("h_15_cast_fp16")];
+            tensor<int32, [2]> var_187_split_sizes_0 = const()[name = string("op_187_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
+            int32 var_187_axis_0 = const()[name = string("op_187_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 1]> var_187_cast_fp16_0, tensor<fp16, [1, 1024, 1]> var_187_cast_fp16_1 = split(axis = var_187_axis_0, split_sizes = var_187_split_sizes_0, x = h_15_cast_fp16)[name = string("op_187_cast_fp16")];
+            fp16 var_189_promoted_to_fp16 = const()[name = string("op_189_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1024, 1]> var_190_cast_fp16 = add(x = var_187_cast_fp16_0, y = var_189_promoted_to_fp16)[name = string("op_190_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_191_cast_fp16 = instance_norm(epsilon = var_147_to_fp16, x = input_25_cast_fp16)[name = string("op_191_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_192_cast_fp16 = mul(x = var_190_cast_fp16, y = var_191_cast_fp16)[name = string("op_192_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_27_cast_fp16 = add(x = var_192_cast_fp16, y = var_187_cast_fp16_1)[name = string("input_27_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_29_cast_fp16 = leaky_relu(alpha = var_144, x = input_27_cast_fp16)[name = string("input_29_cast_fp16")];
+            string out_3_pad_type_0 = const()[name = string("out_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> out_3_pad_0 = const()[name = string("out_3_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_3_strides_0 = const()[name = string("out_3_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_3_dilations_0 = const()[name = string("out_3_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 out_3_groups_0 = const()[name = string("out_3_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 3]> decoder_decode_0_conv2_weight_to_fp16 = const()[name = string("decoder_decode_0_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19156608)))];
+            tensor<fp16, [1024]> decoder_decode_0_conv2_bias_to_fp16 = const()[name = string("decoder_decode_0_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25448128)))];
+            tensor<fp16, [1, 1024, ?]> out_3_cast_fp16 = conv(bias = decoder_decode_0_conv2_bias_to_fp16, dilations = out_3_dilations_0, groups = out_3_groups_0, pad = out_3_pad_0, pad_type = out_3_pad_type_0, strides = out_3_strides_0, weight = decoder_decode_0_conv2_weight_to_fp16, x = input_29_cast_fp16)[name = string("out_3_cast_fp16")];
+            string var_207_pad_type_0 = const()[name = string("op_207_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_207_strides_0 = const()[name = string("op_207_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_207_pad_0 = const()[name = string("op_207_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_207_dilations_0 = const()[name = string("op_207_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_207_groups_0 = const()[name = string("op_207_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1090, 1]> decoder_decode_0_conv1x1_weight_to_fp16 = const()[name = string("decoder_decode_0_conv1x1_weight_to_fp16"), val = tensor<fp16, [1024, 1090, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25450240)))];
+            tensor<fp16, [1, 1024, ?]> var_207_cast_fp16 = conv(dilations = var_207_dilations_0, groups = var_207_groups_0, pad = var_207_pad_0, pad_type = var_207_pad_type_0, strides = var_207_strides_0, weight = decoder_decode_0_conv1x1_weight_to_fp16, x = input_19_cast_fp16)[name = string("op_207_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_208_cast_fp16 = add(x = out_3_cast_fp16, y = var_207_cast_fp16)[name = string("op_208_cast_fp16")];
+            fp16 _inversed_x_3_y_0_to_fp16 = const()[name = string("_inversed_x_3_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
+            tensor<fp16, [1, 1024, ?]> _inversed_x_3_cast_fp16 = mul(x = var_208_cast_fp16, y = _inversed_x_3_y_0_to_fp16)[name = string("_inversed_x_3_cast_fp16")];
+            int32 var_212 = const()[name = string("op_212"), val = int32(1)];
+            bool input_31_interleave_0 = const()[name = string("input_31_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 1090, ?]> input_31_cast_fp16 = concat(axis = var_212, interleave = input_31_interleave_0, values = (_inversed_x_3_cast_fp16, asr_res_1_cast_fp16, F0_cast_fp16, N_cast_fp16))[name = string("input_31_cast_fp16")];
+            fp32 var_215 = const()[name = string("op_215"), val = fp32(0x1.99999ap-3)];
+            tensor<fp16, [2180, 128]> decoder_decode_1_norm1_fc_weight_to_fp16 = const()[name = string("decoder_decode_1_norm1_fc_weight_to_fp16"), val = tensor<fp16, [2180, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27682624)))];
+            tensor<fp16, [2180]> decoder_decode_1_norm1_fc_bias_to_fp16 = const()[name = string("decoder_decode_1_norm1_fc_bias_to_fp16"), val = tensor<fp16, [2180]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28240768)))];
+            tensor<fp16, [1, 2180]> linear_4_cast_fp16 = linear(bias = decoder_decode_1_norm1_fc_bias_to_fp16, weight = decoder_decode_1_norm1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_4_cast_fp16")];
+            tensor<int32, [3]> var_233 = const()[name = string("op_233"), val = tensor<int32, [3]>([1, 2180, 1])];
+            tensor<fp16, [1, 2180, 1]> h_19_cast_fp16 = reshape(shape = var_233, x = linear_4_cast_fp16)[name = string("h_19_cast_fp16")];
+            tensor<int32, [2]> var_235_split_sizes_0 = const()[name = string("op_235_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
+            int32 var_235_axis_0 = const()[name = string("op_235_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1090, 1]> var_235_cast_fp16_0, tensor<fp16, [1, 1090, 1]> var_235_cast_fp16_1 = split(axis = var_235_axis_0, split_sizes = var_235_split_sizes_0, x = h_19_cast_fp16)[name = string("op_235_cast_fp16")];
+            fp16 var_237_promoted_to_fp16 = const()[name = string("op_237_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1090, 1]> var_238_cast_fp16 = add(x = var_235_cast_fp16_0, y = var_237_promoted_to_fp16)[name = string("op_238_cast_fp16")];
+            fp16 var_218_to_fp16 = const()[name = string("op_218_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 1090, ?]> var_239_cast_fp16 = instance_norm(epsilon = var_218_to_fp16, x = input_31_cast_fp16)[name = string("op_239_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> var_240_cast_fp16 = mul(x = var_238_cast_fp16, y = var_239_cast_fp16)[name = string("op_240_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_33_cast_fp16 = add(x = var_240_cast_fp16, y = var_235_cast_fp16_1)[name = string("input_33_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_35_cast_fp16 = leaky_relu(alpha = var_215, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")];
+            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1090, 3]> decoder_decode_1_conv1_weight_to_fp16 = const()[name = string("decoder_decode_1_conv1_weight_to_fp16"), val = tensor<fp16, [1024, 1090, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28245248)))];
+            tensor<fp16, [1024]> decoder_decode_1_conv1_bias_to_fp16 = const()[name = string("decoder_decode_1_conv1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34942272)))];
+            tensor<fp16, [1, 1024, ?]> input_37_cast_fp16 = conv(bias = decoder_decode_1_conv1_bias_to_fp16, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = decoder_decode_1_conv1_weight_to_fp16, x = input_35_cast_fp16)[name = string("input_37_cast_fp16")];
+            tensor<fp16, [2048, 128]> decoder_decode_1_norm2_fc_weight_to_fp16 = const()[name = string("decoder_decode_1_norm2_fc_weight_to_fp16"), val = tensor<fp16, [2048, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34944384)))];
+            tensor<fp16, [2048]> decoder_decode_1_norm2_fc_bias_to_fp16 = const()[name = string("decoder_decode_1_norm2_fc_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35468736)))];
+            tensor<fp16, [1, 2048]> linear_5_cast_fp16 = linear(bias = decoder_decode_1_norm2_fc_bias_to_fp16, weight = decoder_decode_1_norm2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_5_cast_fp16")];
+            tensor<int32, [3]> var_256 = const()[name = string("op_256"), val = tensor<int32, [3]>([1, 2048, 1])];
+            tensor<fp16, [1, 2048, 1]> h_23_cast_fp16 = reshape(shape = var_256, x = linear_5_cast_fp16)[name = string("h_23_cast_fp16")];
+            tensor<int32, [2]> var_258_split_sizes_0 = const()[name = string("op_258_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
+            int32 var_258_axis_0 = const()[name = string("op_258_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 1]> var_258_cast_fp16_0, tensor<fp16, [1, 1024, 1]> var_258_cast_fp16_1 = split(axis = var_258_axis_0, split_sizes = var_258_split_sizes_0, x = h_23_cast_fp16)[name = string("op_258_cast_fp16")];
+            fp16 var_260_promoted_to_fp16 = const()[name = string("op_260_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1024, 1]> var_261_cast_fp16 = add(x = var_258_cast_fp16_0, y = var_260_promoted_to_fp16)[name = string("op_261_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_262_cast_fp16 = instance_norm(epsilon = var_218_to_fp16, x = input_37_cast_fp16)[name = string("op_262_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_263_cast_fp16 = mul(x = var_261_cast_fp16, y = var_262_cast_fp16)[name = string("op_263_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_39_cast_fp16 = add(x = var_263_cast_fp16, y = var_258_cast_fp16_1)[name = string("input_39_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_41_cast_fp16 = leaky_relu(alpha = var_215, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")];
+            string out_5_pad_type_0 = const()[name = string("out_5_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> out_5_pad_0 = const()[name = string("out_5_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_5_strides_0 = const()[name = string("out_5_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_5_dilations_0 = const()[name = string("out_5_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 out_5_groups_0 = const()[name = string("out_5_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 3]> decoder_decode_1_conv2_weight_to_fp16 = const()[name = string("decoder_decode_1_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35472896)))];
+            tensor<fp16, [1024]> decoder_decode_1_conv2_bias_to_fp16 = const()[name = string("decoder_decode_1_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41764416)))];
+            tensor<fp16, [1, 1024, ?]> out_5_cast_fp16 = conv(bias = decoder_decode_1_conv2_bias_to_fp16, dilations = out_5_dilations_0, groups = out_5_groups_0, pad = out_5_pad_0, pad_type = out_5_pad_type_0, strides = out_5_strides_0, weight = decoder_decode_1_conv2_weight_to_fp16, x = input_41_cast_fp16)[name = string("out_5_cast_fp16")];
+            string var_278_pad_type_0 = const()[name = string("op_278_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_278_strides_0 = const()[name = string("op_278_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_278_pad_0 = const()[name = string("op_278_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_278_dilations_0 = const()[name = string("op_278_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_278_groups_0 = const()[name = string("op_278_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1090, 1]> decoder_decode_1_conv1x1_weight_to_fp16 = const()[name = string("decoder_decode_1_conv1x1_weight_to_fp16"), val = tensor<fp16, [1024, 1090, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41766528)))];
+            tensor<fp16, [1, 1024, ?]> var_278_cast_fp16 = conv(dilations = var_278_dilations_0, groups = var_278_groups_0, pad = var_278_pad_0, pad_type = var_278_pad_type_0, strides = var_278_strides_0, weight = decoder_decode_1_conv1x1_weight_to_fp16, x = input_31_cast_fp16)[name = string("op_278_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_279_cast_fp16 = add(x = out_5_cast_fp16, y = var_278_cast_fp16)[name = string("op_279_cast_fp16")];
+            fp16 _inversed_x_5_y_0_to_fp16 = const()[name = string("_inversed_x_5_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
+            tensor<fp16, [1, 1024, ?]> _inversed_x_5_cast_fp16 = mul(x = var_279_cast_fp16, y = _inversed_x_5_y_0_to_fp16)[name = string("_inversed_x_5_cast_fp16")];
+            int32 var_283 = const()[name = string("op_283"), val = int32(1)];
+            bool input_43_interleave_0 = const()[name = string("input_43_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 1090, ?]> input_43_cast_fp16 = concat(axis = var_283, interleave = input_43_interleave_0, values = (_inversed_x_5_cast_fp16, asr_res_1_cast_fp16, F0_cast_fp16, N_cast_fp16))[name = string("input_43_cast_fp16")];
+            fp32 var_286 = const()[name = string("op_286"), val = fp32(0x1.99999ap-3)];
+            tensor<fp16, [2180, 128]> decoder_decode_2_norm1_fc_weight_to_fp16 = const()[name = string("decoder_decode_2_norm1_fc_weight_to_fp16"), val = tensor<fp16, [2180, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43998912)))];
+            tensor<fp16, [2180]> decoder_decode_2_norm1_fc_bias_to_fp16 = const()[name = string("decoder_decode_2_norm1_fc_bias_to_fp16"), val = tensor<fp16, [2180]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44557056)))];
+            tensor<fp16, [1, 2180]> linear_6_cast_fp16 = linear(bias = decoder_decode_2_norm1_fc_bias_to_fp16, weight = decoder_decode_2_norm1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_6_cast_fp16")];
+            tensor<int32, [3]> var_304 = const()[name = string("op_304"), val = tensor<int32, [3]>([1, 2180, 1])];
+            tensor<fp16, [1, 2180, 1]> h_27_cast_fp16 = reshape(shape = var_304, x = linear_6_cast_fp16)[name = string("h_27_cast_fp16")];
+            tensor<int32, [2]> var_306_split_sizes_0 = const()[name = string("op_306_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
+            int32 var_306_axis_0 = const()[name = string("op_306_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1090, 1]> var_306_cast_fp16_0, tensor<fp16, [1, 1090, 1]> var_306_cast_fp16_1 = split(axis = var_306_axis_0, split_sizes = var_306_split_sizes_0, x = h_27_cast_fp16)[name = string("op_306_cast_fp16")];
+            fp16 var_308_promoted_to_fp16 = const()[name = string("op_308_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1090, 1]> var_309_cast_fp16 = add(x = var_306_cast_fp16_0, y = var_308_promoted_to_fp16)[name = string("op_309_cast_fp16")];
+            fp16 var_289_to_fp16 = const()[name = string("op_289_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 1090, ?]> var_310_cast_fp16 = instance_norm(epsilon = var_289_to_fp16, x = input_43_cast_fp16)[name = string("op_310_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> var_311_cast_fp16 = mul(x = var_309_cast_fp16, y = var_310_cast_fp16)[name = string("op_311_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_45_cast_fp16 = add(x = var_311_cast_fp16, y = var_306_cast_fp16_1)[name = string("input_45_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_47_cast_fp16 = leaky_relu(alpha = var_286, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")];
+            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1090, 3]> decoder_decode_2_conv1_weight_to_fp16 = const()[name = string("decoder_decode_2_conv1_weight_to_fp16"), val = tensor<fp16, [1024, 1090, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44561536)))];
+            tensor<fp16, [1024]> decoder_decode_2_conv1_bias_to_fp16 = const()[name = string("decoder_decode_2_conv1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51258560)))];
+            tensor<fp16, [1, 1024, ?]> input_49_cast_fp16 = conv(bias = decoder_decode_2_conv1_bias_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = decoder_decode_2_conv1_weight_to_fp16, x = input_47_cast_fp16)[name = string("input_49_cast_fp16")];
+            tensor<fp16, [2048, 128]> decoder_decode_2_norm2_fc_weight_to_fp16 = const()[name = string("decoder_decode_2_norm2_fc_weight_to_fp16"), val = tensor<fp16, [2048, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51260672)))];
+            tensor<fp16, [2048]> decoder_decode_2_norm2_fc_bias_to_fp16 = const()[name = string("decoder_decode_2_norm2_fc_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51785024)))];
+            tensor<fp16, [1, 2048]> linear_7_cast_fp16 = linear(bias = decoder_decode_2_norm2_fc_bias_to_fp16, weight = decoder_decode_2_norm2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_7_cast_fp16")];
+            tensor<int32, [3]> var_327 = const()[name = string("op_327"), val = tensor<int32, [3]>([1, 2048, 1])];
+            tensor<fp16, [1, 2048, 1]> h_31_cast_fp16 = reshape(shape = var_327, x = linear_7_cast_fp16)[name = string("h_31_cast_fp16")];
+            tensor<int32, [2]> var_329_split_sizes_0 = const()[name = string("op_329_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
+            int32 var_329_axis_0 = const()[name = string("op_329_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1024, 1]> var_329_cast_fp16_0, tensor<fp16, [1, 1024, 1]> var_329_cast_fp16_1 = split(axis = var_329_axis_0, split_sizes = var_329_split_sizes_0, x = h_31_cast_fp16)[name = string("op_329_cast_fp16")];
+            fp16 var_331_promoted_to_fp16 = const()[name = string("op_331_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1024, 1]> var_332_cast_fp16 = add(x = var_329_cast_fp16_0, y = var_331_promoted_to_fp16)[name = string("op_332_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_333_cast_fp16 = instance_norm(epsilon = var_289_to_fp16, x = input_49_cast_fp16)[name = string("op_333_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_334_cast_fp16 = mul(x = var_332_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_51_cast_fp16 = add(x = var_334_cast_fp16, y = var_329_cast_fp16_1)[name = string("input_51_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_53_cast_fp16 = leaky_relu(alpha = var_286, x = input_51_cast_fp16)[name = string("input_53_cast_fp16")];
+            string out_7_pad_type_0 = const()[name = string("out_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> out_7_pad_0 = const()[name = string("out_7_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_7_strides_0 = const()[name = string("out_7_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_7_dilations_0 = const()[name = string("out_7_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 out_7_groups_0 = const()[name = string("out_7_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1024, 3]> decoder_decode_2_conv2_weight_to_fp16 = const()[name = string("decoder_decode_2_conv2_weight_to_fp16"), val = tensor<fp16, [1024, 1024, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51789184)))];
+            tensor<fp16, [1024]> decoder_decode_2_conv2_bias_to_fp16 = const()[name = string("decoder_decode_2_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58080704)))];
+            tensor<fp16, [1, 1024, ?]> out_7_cast_fp16 = conv(bias = decoder_decode_2_conv2_bias_to_fp16, dilations = out_7_dilations_0, groups = out_7_groups_0, pad = out_7_pad_0, pad_type = out_7_pad_type_0, strides = out_7_strides_0, weight = decoder_decode_2_conv2_weight_to_fp16, x = input_53_cast_fp16)[name = string("out_7_cast_fp16")];
+            string var_349_pad_type_0 = const()[name = string("op_349_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_349_strides_0 = const()[name = string("op_349_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_349_pad_0 = const()[name = string("op_349_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_349_dilations_0 = const()[name = string("op_349_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_349_groups_0 = const()[name = string("op_349_groups_0"), val = int32(1)];
+            tensor<fp16, [1024, 1090, 1]> decoder_decode_2_conv1x1_weight_to_fp16 = const()[name = string("decoder_decode_2_conv1x1_weight_to_fp16"), val = tensor<fp16, [1024, 1090, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58082816)))];
+            tensor<fp16, [1, 1024, ?]> var_349_cast_fp16 = conv(dilations = var_349_dilations_0, groups = var_349_groups_0, pad = var_349_pad_0, pad_type = var_349_pad_type_0, strides = var_349_strides_0, weight = decoder_decode_2_conv1x1_weight_to_fp16, x = input_43_cast_fp16)[name = string("op_349_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_350_cast_fp16 = add(x = out_7_cast_fp16, y = var_349_cast_fp16)[name = string("op_350_cast_fp16")];
+            fp16 _inversed_x_y_0_to_fp16 = const()[name = string("_inversed_x_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
+            tensor<fp16, [1, 1024, ?]> _inversed_x_cast_fp16 = mul(x = var_350_cast_fp16, y = _inversed_x_y_0_to_fp16)[name = string("_inversed_x_cast_fp16")];
+            int32 var_354 = const()[name = string("op_354"), val = int32(1)];
+            bool input_55_interleave_0 = const()[name = string("input_55_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 1090, ?]> input_55_cast_fp16 = concat(axis = var_354, interleave = input_55_interleave_0, values = (_inversed_x_cast_fp16, asr_res_1_cast_fp16, F0_cast_fp16, N_cast_fp16))[name = string("input_55_cast_fp16")];
+            fp32 var_359 = const()[name = string("op_359"), val = fp32(0x1.99999ap-3)];
+            tensor<fp16, [2180, 128]> decoder_decode_3_norm1_fc_weight_to_fp16 = const()[name = string("decoder_decode_3_norm1_fc_weight_to_fp16"), val = tensor<fp16, [2180, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60315200)))];
+            tensor<fp16, [2180]> decoder_decode_3_norm1_fc_bias_to_fp16 = const()[name = string("decoder_decode_3_norm1_fc_bias_to_fp16"), val = tensor<fp16, [2180]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60873344)))];
+            tensor<fp16, [1, 2180]> linear_8_cast_fp16 = linear(bias = decoder_decode_3_norm1_fc_bias_to_fp16, weight = decoder_decode_3_norm1_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_8_cast_fp16")];
+            tensor<int32, [3]> var_379 = const()[name = string("op_379"), val = tensor<int32, [3]>([1, 2180, 1])];
+            tensor<fp16, [1, 2180, 1]> h_35_cast_fp16 = reshape(shape = var_379, x = linear_8_cast_fp16)[name = string("h_35_cast_fp16")];
+            tensor<int32, [2]> var_381_split_sizes_0 = const()[name = string("op_381_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
+            int32 var_381_axis_0 = const()[name = string("op_381_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 1090, 1]> var_381_cast_fp16_0, tensor<fp16, [1, 1090, 1]> var_381_cast_fp16_1 = split(axis = var_381_axis_0, split_sizes = var_381_split_sizes_0, x = h_35_cast_fp16)[name = string("op_381_cast_fp16")];
+            fp16 var_383_promoted_to_fp16 = const()[name = string("op_383_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1090, 1]> var_384_cast_fp16 = add(x = var_381_cast_fp16_0, y = var_383_promoted_to_fp16)[name = string("op_384_cast_fp16")];
+            fp16 var_363_to_fp16 = const()[name = string("op_363_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 1090, ?]> var_385_cast_fp16 = instance_norm(epsilon = var_363_to_fp16, x = input_55_cast_fp16)[name = string("op_385_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> var_386_cast_fp16 = mul(x = var_384_cast_fp16, y = var_385_cast_fp16)[name = string("op_386_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_57_cast_fp16 = add(x = var_386_cast_fp16, y = var_381_cast_fp16_1)[name = string("input_57_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_59_cast_fp16 = leaky_relu(alpha = var_359, x = input_57_cast_fp16)[name = string("input_59_cast_fp16")];
+            string conv_transpose_0_pad_type_0 = const()[name = string("conv_transpose_0_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> conv_transpose_0_pad_0 = const()[name = string("conv_transpose_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_transpose_0_strides_0 = const()[name = string("conv_transpose_0_strides_0"), val = tensor<int32, [1]>([2])];
+            int32 conv_transpose_0_groups_0 = const()[name = string("conv_transpose_0_groups_0"), val = int32(1090)];
+            tensor<int32, [1]> conv_transpose_0_dilations_0 = const()[name = string("conv_transpose_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1090, 1, 3]> decoder_decode_3_pool_weight_to_fp16 = const()[name = string("decoder_decode_3_pool_weight_to_fp16"), val = tensor<fp16, [1090, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60877824)))];
+            tensor<fp16, [1090]> decoder_decode_3_pool_bias_to_fp16 = const()[name = string("decoder_decode_3_pool_bias_to_fp16"), val = tensor<fp16, [1090]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60884480)))];
+            tensor<fp16, [1, 1090, ?]> conv_transpose_0_cast_fp16 = conv_transpose(bias = decoder_decode_3_pool_bias_to_fp16, dilations = conv_transpose_0_dilations_0, groups = conv_transpose_0_groups_0, pad = conv_transpose_0_pad_0, pad_type = conv_transpose_0_pad_type_0, strides = conv_transpose_0_strides_0, weight = decoder_decode_3_pool_weight_to_fp16, x = input_59_cast_fp16)[name = string("conv_transpose_0_cast_fp16")];
+            tensor<int32, [3]> input_61_begin_0 = const()[name = string("input_61_begin_0"), val = tensor<int32, [3]>([0, 0, 1])];
+            tensor<int32, [3]> input_61_end_0 = const()[name = string("input_61_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<bool, [3]> input_61_begin_mask_0 = const()[name = string("input_61_begin_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<bool, [3]> input_61_end_mask_0 = const()[name = string("input_61_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            tensor<fp16, [1, 1090, ?]> input_61_cast_fp16 = slice_by_index(begin = input_61_begin_0, begin_mask = input_61_begin_mask_0, end = input_61_end_0, end_mask = input_61_end_mask_0, x = conv_transpose_0_cast_fp16)[name = string("input_61_cast_fp16")];
+            string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 1090, 3]> decoder_decode_3_conv1_weight_to_fp16 = const()[name = string("decoder_decode_3_conv1_weight_to_fp16"), val = tensor<fp16, [512, 1090, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60886784)))];
+            tensor<fp16, [512]> decoder_decode_3_conv1_bias_to_fp16 = const()[name = string("decoder_decode_3_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64235328)))];
+            tensor<fp16, [1, 512, ?]> input_63_cast_fp16 = conv(bias = decoder_decode_3_conv1_bias_to_fp16, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = decoder_decode_3_conv1_weight_to_fp16, x = input_61_cast_fp16)[name = string("input_63_cast_fp16")];
+            tensor<fp16, [1024, 128]> decoder_decode_3_norm2_fc_weight_to_fp16 = const()[name = string("decoder_decode_3_norm2_fc_weight_to_fp16"), val = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64236416)))];
+            tensor<fp16, [1024]> decoder_decode_3_norm2_fc_bias_to_fp16 = const()[name = string("decoder_decode_3_norm2_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64498624)))];
+            tensor<fp16, [1, 1024]> linear_9_cast_fp16 = linear(bias = decoder_decode_3_norm2_fc_bias_to_fp16, weight = decoder_decode_3_norm2_fc_weight_to_fp16, x = ref_to_fp16)[name = string("linear_9_cast_fp16")];
+            tensor<int32, [3]> var_409 = const()[name = string("op_409"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp16, [1, 1024, 1]> h_cast_fp16 = reshape(shape = var_409, x = linear_9_cast_fp16)[name = string("h_cast_fp16")];
+            tensor<int32, [2]> var_411_split_sizes_0 = const()[name = string("op_411_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            int32 var_411_axis_0 = const()[name = string("op_411_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 512, 1]> var_411_cast_fp16_0, tensor<fp16, [1, 512, 1]> var_411_cast_fp16_1 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = h_cast_fp16)[name = string("op_411_cast_fp16")];
+            fp16 var_413_promoted_to_fp16 = const()[name = string("op_413_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 512, 1]> var_414_cast_fp16 = add(x = var_411_cast_fp16_0, y = var_413_promoted_to_fp16)[name = string("op_414_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> var_415_cast_fp16 = instance_norm(epsilon = var_363_to_fp16, x = input_63_cast_fp16)[name = string("op_415_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> var_416_cast_fp16 = mul(x = var_414_cast_fp16, y = var_415_cast_fp16)[name = string("op_416_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_65_cast_fp16 = add(x = var_416_cast_fp16, y = var_411_cast_fp16_1)[name = string("input_65_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_67_cast_fp16 = leaky_relu(alpha = var_359, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")];
+            string out_pad_type_0 = const()[name = string("out_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> out_pad_0 = const()[name = string("out_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_strides_0 = const()[name = string("out_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_dilations_0 = const()[name = string("out_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 out_groups_0 = const()[name = string("out_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 512, 3]> decoder_decode_3_conv2_weight_to_fp16 = const()[name = string("decoder_decode_3_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64500736)))];
+            tensor<fp16, [512]> decoder_decode_3_conv2_bias_to_fp16 = const()[name = string("decoder_decode_3_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66073664)))];
+            tensor<fp16, [1, 512, ?]> out_cast_fp16 = conv(bias = decoder_decode_3_conv2_bias_to_fp16, dilations = out_dilations_0, groups = out_groups_0, pad = out_pad_0, pad_type = out_pad_type_0, strides = out_strides_0, weight = decoder_decode_3_conv2_weight_to_fp16, x = input_67_cast_fp16)[name = string("out_cast_fp16")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [1, 1090, ?, 1]> expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = input_55_cast_fp16)[name = string("expand_dims_0_cast_fp16")];
+            int32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = int32(2)];
+            int32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = int32(1)];
+            tensor<fp16, [1, 1090, ?, 1]> upsample_nearest_neighbor_0_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0_cast_fp16)[name = string("upsample_nearest_neighbor_0_cast_fp16")];
+            tensor<int32, [1]> input_axes_0 = const()[name = string("input_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [1, 1090, ?]> input_cast_fp16 = squeeze(axes = input_axes_0, x = upsample_nearest_neighbor_0_cast_fp16)[name = string("input_cast_fp16")];
+            string var_433_pad_type_0 = const()[name = string("op_433_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_433_strides_0 = const()[name = string("op_433_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_433_pad_0 = const()[name = string("op_433_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_433_dilations_0 = const()[name = string("op_433_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_433_groups_0 = const()[name = string("op_433_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 1090, 1]> decoder_decode_3_conv1x1_weight_to_fp16 = const()[name = string("decoder_decode_3_conv1x1_weight_to_fp16"), val = tensor<fp16, [512, 1090, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66074752)))];
+            tensor<fp16, [1, 512, ?]> var_433_cast_fp16 = conv(dilations = var_433_dilations_0, groups = var_433_groups_0, pad = var_433_pad_0, pad_type = var_433_pad_type_0, strides = var_433_strides_0, weight = decoder_decode_3_conv1x1_weight_to_fp16, x = input_cast_fp16)[name = string("op_433_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> var_434_cast_fp16 = add(x = out_cast_fp16, y = var_433_cast_fp16)[name = string("op_434_cast_fp16")];
+            fp16 _inversed_436_y_0_to_fp16 = const()[name = string("_inversed_436_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
+            tensor<fp16, [1, 512, ?]> var_436 = mul(x = var_434_cast_fp16, y = _inversed_436_y_0_to_fp16)[name = string("_inversed_436_cast_fp16")];
+        } -> (var_436);
+}

iteration_3/compiled/decoder_pre_fp16.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db81849a38ce1959ea345219332051947f22f00dc2445cb9b7a119673ca4bf93
+size 67190976

iteration_3/compiled/decoder_upsample_fp16.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c3674b167f9873214552daa4c56f6068fe8cb2850436a49232fe3b86ddbf8bc
+size 243

iteration_3/compiled/decoder_upsample_fp16.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c164a7d827bd1d9d73f88cd116cb84658c30bd848a9654f658206849e9bc4336
+size 467

iteration_3/compiled/decoder_upsample_fp16.mlmodelc/metadata.json

@@ -0,0 +1,97 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "var_3711",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.linear" : 96,
+      "Ios18.convTranspose" : 4,
+      "Ios18.conv" : 101,
+      "Ios18.mul" : 302,
+      "Ios18.sin" : 101,
+      "Ios18.add" : 353,
+      "Ios18.tanh" : 1,
+      "Ios18.pow" : 101,
+      "Ios18.sliceByIndex" : 2,
+      "Ios18.cast" : 3,
+      "Ios18.reshape" : 96,
+      "Split" : 96,
+      "Ios18.instanceNorm" : 88
+    },
+    "computePrecision" : "Mixed (Float16, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-05-08",
+      "com.github.apple.coremltools.source" : "torch==2.11.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "inputSchema" : [
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 512 × 2...4096",
+        "shapeRange" : "[[1, 1], [512, 512], [2, 4096]]",
+        "formattedType" : "MultiArray (Float32 1 × 512 × 294)",
+        "type" : "MultiArray",
+        "shape" : "[1, 512, 294]",
+        "name" : "x_pre",
+        "shortDescription" : ""
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 128)",
+        "shortDescription" : "",
+        "shape" : "[1, 128]",
+        "name" : "ref",
+        "type" : "MultiArray"
+      },
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 1 × 600...1228800",
+        "shapeRange" : "[[1, 1], [1, 1], [600, 1228800]]",
+        "formattedType" : "MultiArray (Float32 1 × 1 × 88200)",
+        "type" : "MultiArray",
+        "shape" : "[1, 1, 88200]",
+        "name" : "har_source",
+        "shortDescription" : ""
+      }
+    ],
+    "generatedClassName" : "decoder_upsample_fp16",
+    "method" : "predict"
+  }
+]

iteration_3/compiled/decoder_upsample_fp16.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:43161151f001bb951c34952465adfc3c4f5fb8ab2845f31903be09ea9f1a6bc5
+size 41400320

iteration_3/compiled/duration_predictor_fp16.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ba0441a674538a9fcf54834f20487d807286cb7b9b26b0d5187d4fab9abcd03
+size 243

iteration_3/compiled/duration_predictor_fp16.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2b1de14b8aeaf322f76418d3455ffe54d926f74f638130431b68a42b109b5f9c
+size 470

iteration_3/compiled/duration_predictor_fp16.mlmodelc/metadata.json

@@ -0,0 +1,112 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "input_13",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "var_308",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.linear" : 4,
+      "Ios18.sub" : 1,
+      "Select" : 1,
+      "Ios18.expandDims" : 2,
+      "Ios18.gather" : 1,
+      "Ios18.concat" : 5,
+      "Shape" : 1,
+      "Ios18.lstm" : 4,
+      "Ios18.realDiv" : 1,
+      "Ios18.add" : 6,
+      "Ios18.transpose" : 18,
+      "Ios18.cast" : 5,
+      "Tile" : 1,
+      "Ios18.layerNorm" : 3,
+      "Ios18.equal" : 1,
+      "Ios18.reshape" : 3,
+      "Split" : 3,
+      "Ios18.mul" : 7
+    },
+    "computePrecision" : "Mixed (Float16, Int16, Int32, UInt16)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-05-08",
+      "com.github.apple.coremltools.source" : "torch==2.11.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "inputSchema" : [
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 512 × 1...512",
+        "shapeRange" : "[[1, 1], [512, 512], [1, 512]]",
+        "formattedType" : "MultiArray (Float32 1 × 512 × 57)",
+        "type" : "MultiArray",
+        "shape" : "[1, 512, 57]",
+        "name" : "d_en",
+        "shortDescription" : ""
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 128)",
+        "shortDescription" : "",
+        "shape" : "[1, 128]",
+        "name" : "s",
+        "type" : "MultiArray"
+      },
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 1...512",
+        "shapeRange" : "[[1, 1], [1, 512]]",
+        "formattedType" : "MultiArray (Float32 1 × 57)",
+        "type" : "MultiArray",
+        "shape" : "[1, 57]",
+        "name" : "text_mask",
+        "shortDescription" : ""
+      }
+    ],
+    "generatedClassName" : "duration_predictor_fp16",
+    "method" : "predict"
+  }
+]

iteration_3/compiled/duration_predictor_fp16.mlmodelc/model.mil

@@ -0,0 +1,188 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 512, ?]> d_en, tensor<fp32, [1, 128]> s, tensor<fp32, [1, ?]> text_mask) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"d_en", [1, 512, 57]}, {"text_mask", [1, 57]}}), ("RangeDims", {{"d_en", [[1, 1], [512, 512], [1, 512]]}, {"text_mask", [[1, 1], [1, 512]]}})))] {
+            string d_en_to_fp16_dtype_0 = const()[name = string("d_en_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 512, ?]> d_en_to_fp16 = cast(dtype = d_en_to_fp16_dtype_0, x = d_en)[name = string("cast_10")];
+            tensor<int32, [3]> var_25_shape_cast_fp16 = shape(x = d_en_to_fp16)[name = string("op_25_shape_cast_fp16")];
+            int32 gather_0_axis_0 = const()[name = string("gather_0_axis_0"), val = int32(0)];
+            int32 gather_0_batch_dims_0 = const()[name = string("gather_0_batch_dims_0"), val = int32(0)];
+            bool gather_0_validate_indices_0 = const()[name = string("gather_0_validate_indices_0"), val = bool(false)];
+            string var_25_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_25_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")];
+            uint16 gather_0_indices_0_to_uint16 = const()[name = string("gather_0_indices_0_to_uint16"), val = uint16(2)];
+            tensor<int16, [3]> var_25_shape_cast_fp16_to_int16 = cast(dtype = var_25_shape_cast_fp16_to_int16_dtype_0, x = var_25_shape_cast_fp16)[name = string("cast_9")];
+            int16 gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = gather_0_indices_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_25_shape_cast_fp16_to_int16)[name = string("gather_0_cast_uint16")];
+            string gather_0_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_0_cast_uint16_to_int32_dtype_0"), val = string("int32")];
+            fp16 var_33_to_fp16 = const()[name = string("op_33_to_fp16"), val = fp16(0x1p+0)];
+            string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, ?]> text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_7")];
+            tensor<fp16, [1, ?]> var_35_cast_fp16 = sub(x = var_33_to_fp16, y = text_mask_to_fp16)[name = string("op_35_cast_fp16")];
+            tensor<int32, [1]> keep_axes_0 = const()[name = string("keep_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, ?]> keep_cast_fp16 = expand_dims(axes = keep_axes_0, x = var_35_cast_fp16)[name = string("keep_cast_fp16")];
+            tensor<int32, [1]> var_39_axes_0 = const()[name = string("op_39_axes_0"), val = tensor<int32, [1]>([-1])];
+            string s_to_fp16_dtype_0 = const()[name = string("s_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 128]> s_to_fp16 = cast(dtype = s_to_fp16_dtype_0, x = s)[name = string("cast_6")];
+            tensor<fp16, [1, 128, 1]> var_39_cast_fp16 = expand_dims(axes = var_39_axes_0, x = s_to_fp16)[name = string("op_39_cast_fp16")];
+            int32 var_43 = const()[name = string("op_43"), val = int32(128)];
+            int32 var_44 = const()[name = string("op_44"), val = int32(-1)];
+            int32 concat_0_axis_0 = const()[name = string("concat_0_axis_0"), val = int32(0)];
+            bool concat_0_interleave_0 = const()[name = string("concat_0_interleave_0"), val = bool(false)];
+            int32 gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = string("cast_8")];
+            tensor<int32, [3]> concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (var_44, var_43, gather_0_cast_uint16_to_int32))[name = string("concat_0")];
+            tensor<int32, [3]> shape_0 = const()[name = string("shape_0"), val = tensor<int32, [3]>([1, 128, 1])];
+            int32 equal_0_y_0 = const()[name = string("equal_0_y_0"), val = int32(-1)];
+            tensor<bool, [3]> equal_0 = equal(x = concat_0, y = equal_0_y_0)[name = string("equal_0")];
+            tensor<int32, [3]> select_0 = select(a = shape_0, b = concat_0, cond = equal_0)[name = string("select_0")];
+            tensor<int32, [3]> real_div_0 = real_div(x = select_0, y = shape_0)[name = string("real_div_0")];
+            tensor<fp16, [?, ?, ?]> s_exp_cast_fp16 = tile(reps = real_div_0, x = var_39_cast_fp16)[name = string("s_exp_cast_fp16")];
+            int32 var_49 = const()[name = string("op_49"), val = int32(1)];
+            bool var_50_interleave_0 = const()[name = string("op_50_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, ?, ?]> var_50_cast_fp16 = concat(axis = var_49, interleave = var_50_interleave_0, values = (d_en_to_fp16, s_exp_cast_fp16))[name = string("op_50_cast_fp16")];
+            tensor<fp16, [1, ?, ?]> x_1_cast_fp16 = mul(x = var_50_cast_fp16, y = keep_cast_fp16)[name = string("x_1_cast_fp16")];
+            tensor<int32, [3]> transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])];
+            string x_t_1_batch_first_direction_0 = const()[name = string("x_t_1_batch_first_direction_0"), val = string("bidirectional")];
+            bool x_t_1_batch_first_output_sequence_0 = const()[name = string("x_t_1_batch_first_output_sequence_0"), val = bool(true)];
+            string x_t_1_batch_first_recurrent_activation_0 = const()[name = string("x_t_1_batch_first_recurrent_activation_0"), val = string("sigmoid")];
+            string x_t_1_batch_first_cell_activation_0 = const()[name = string("x_t_1_batch_first_cell_activation_0"), val = string("tanh")];
+            string x_t_1_batch_first_activation_0 = const()[name = string("x_t_1_batch_first_activation_0"), val = string("tanh")];
+            tensor<fp16, [1, 512]> x_t_1_batch_first_lstm_h0_reshaped_to_fp16 = const()[name = string("x_t_1_batch_first_lstm_h0_reshaped_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [1024, 640]> concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152)))];
+            tensor<fp16, [1024, 256]> concat_6_to_fp16 = const()[name = string("concat_6_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))];
+            tensor<fp16, [1024]> add_0_to_fp16 = const()[name = string("add_0_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1836288)))];
+            tensor<fp16, [1024, 640]> concat_7_to_fp16 = const()[name = string("concat_7_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1838400)))];
+            tensor<fp16, [1024, 256]> concat_8_to_fp16 = const()[name = string("concat_8_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3149184)))];
+            tensor<fp16, [1024]> add_1_to_fp16 = const()[name = string("add_1_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3673536)))];
+            tensor<fp16, [?, 1, ?]> transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = x_1_cast_fp16)[name = string("transpose_26")];
+            tensor<fp16, [?, 1, 512]> x_t_1_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> x_t_1_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> x_t_1_batch_first_cast_fp16_2 = lstm(activation = x_t_1_batch_first_activation_0, bias = add_0_to_fp16, bias_back = add_1_to_fp16, cell_activation = x_t_1_batch_first_cell_activation_0, direction = x_t_1_batch_first_direction_0, initial_c = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = x_t_1_batch_first_output_sequence_0, recurrent_activation = x_t_1_batch_first_recurrent_activation_0, weight_hh = concat_6_to_fp16, weight_hh_back = concat_8_to_fp16, weight_ih = concat_5_to_fp16, weight_ih_back = concat_7_to_fp16, x = transpose_0_cast_fp16)[name = string("x_t_1_batch_first_cast_fp16")];
+            tensor<int32, [3]> transpose_6_perm_0 = const()[name = string("transpose_6_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp16, [1024, 128]> text_encoder_lstms_1_fc_weight_to_fp16 = const()[name = string("text_encoder_lstms_1_fc_weight_to_fp16"), val = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3675648)))];
+            tensor<fp16, [1024]> text_encoder_lstms_1_fc_bias_to_fp16 = const()[name = string("text_encoder_lstms_1_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3937856)))];
+            tensor<fp16, [1, 1024]> linear_0_cast_fp16 = linear(bias = text_encoder_lstms_1_fc_bias_to_fp16, weight = text_encoder_lstms_1_fc_weight_to_fp16, x = s_to_fp16)[name = string("linear_0_cast_fp16")];
+            tensor<int32, [3]> var_105 = const()[name = string("op_105"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp16, [1, 1024, 1]> h_3_cast_fp16 = reshape(shape = var_105, x = linear_0_cast_fp16)[name = string("h_3_cast_fp16")];
+            tensor<int32, [2]> var_107_split_sizes_0 = const()[name = string("op_107_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            int32 var_107_axis_0 = const()[name = string("op_107_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 512, 1]> var_107_cast_fp16_0, tensor<fp16, [1, 512, 1]> var_107_cast_fp16_1 = split(axis = var_107_axis_0, split_sizes = var_107_split_sizes_0, x = h_3_cast_fp16)[name = string("op_107_cast_fp16")];
+            tensor<int32, [3]> gamma_3_perm_0 = const()[name = string("gamma_3_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [3]> beta_3_perm_0 = const()[name = string("beta_3_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [1]> x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor<int32, [1]>([-1])];
+            fp16 var_90_to_fp16 = const()[name = string("op_90_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> transpose_6_cast_fp16 = transpose(perm = transpose_6_perm_0, x = x_t_1_batch_first_cast_fp16_0)[name = string("transpose_25")];
+            tensor<fp16, [1, ?, 512]> x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_90_to_fp16, x = transpose_6_cast_fp16)[name = string("x_9_cast_fp16")];
+            fp16 var_113_promoted_to_fp16 = const()[name = string("op_113_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1, 512]> gamma_3_cast_fp16 = transpose(perm = gamma_3_perm_0, x = var_107_cast_fp16_0)[name = string("transpose_24")];
+            tensor<fp16, [1, 1, 512]> var_114_cast_fp16 = add(x = gamma_3_cast_fp16, y = var_113_promoted_to_fp16)[name = string("op_114_cast_fp16")];
+            tensor<fp16, [1, ?, 512]> var_115_cast_fp16 = mul(x = var_114_cast_fp16, y = x_9_cast_fp16)[name = string("op_115_cast_fp16")];
+            tensor<fp16, [1, 1, 512]> beta_3_cast_fp16 = transpose(perm = beta_3_perm_0, x = var_107_cast_fp16_1)[name = string("transpose_23")];
+            tensor<fp16, [1, ?, 512]> x_11_cast_fp16 = add(x = var_115_cast_fp16, y = beta_3_cast_fp16)[name = string("x_11_cast_fp16")];
+            tensor<int32, [3]> x_13_perm_0 = const()[name = string("x_13_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
+            int32 var_123 = const()[name = string("op_123"), val = int32(1)];
+            bool var_124_interleave_0 = const()[name = string("op_124_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 512, ?]> x_13_cast_fp16 = transpose(perm = x_13_perm_0, x = x_11_cast_fp16)[name = string("transpose_22")];
+            tensor<fp16, [1, ?, ?]> var_124_cast_fp16 = concat(axis = var_123, interleave = var_124_interleave_0, values = (x_13_cast_fp16, s_exp_cast_fp16))[name = string("op_124_cast_fp16")];
+            tensor<fp16, [1, ?, ?]> x_15_cast_fp16 = mul(x = var_124_cast_fp16, y = keep_cast_fp16)[name = string("x_15_cast_fp16")];
+            tensor<int32, [3]> transpose_2_perm_0 = const()[name = string("transpose_2_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])];
+            string x_t_3_batch_first_direction_0 = const()[name = string("x_t_3_batch_first_direction_0"), val = string("bidirectional")];
+            bool x_t_3_batch_first_output_sequence_0 = const()[name = string("x_t_3_batch_first_output_sequence_0"), val = bool(true)];
+            string x_t_3_batch_first_recurrent_activation_0 = const()[name = string("x_t_3_batch_first_recurrent_activation_0"), val = string("sigmoid")];
+            string x_t_3_batch_first_cell_activation_0 = const()[name = string("x_t_3_batch_first_cell_activation_0"), val = string("tanh")];
+            string x_t_3_batch_first_activation_0 = const()[name = string("x_t_3_batch_first_activation_0"), val = string("tanh")];
+            tensor<fp16, [1024, 640]> concat_15_to_fp16 = const()[name = string("concat_15_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3939968)))];
+            tensor<fp16, [1024, 256]> concat_16_to_fp16 = const()[name = string("concat_16_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5250752)))];
+            tensor<fp16, [1024]> add_2_to_fp16 = const()[name = string("add_2_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5775104)))];
+            tensor<fp16, [1024, 640]> concat_17_to_fp16 = const()[name = string("concat_17_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5777216)))];
+            tensor<fp16, [1024, 256]> concat_18_to_fp16 = const()[name = string("concat_18_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7088000)))];
+            tensor<fp16, [1024]> add_3_to_fp16 = const()[name = string("add_3_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7612352)))];
+            tensor<fp16, [?, 1, ?]> transpose_2_cast_fp16 = transpose(perm = transpose_2_perm_0, x = x_15_cast_fp16)[name = string("transpose_21")];
+            tensor<fp16, [?, 1, 512]> x_t_3_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> x_t_3_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> x_t_3_batch_first_cast_fp16_2 = lstm(activation = x_t_3_batch_first_activation_0, bias = add_2_to_fp16, bias_back = add_3_to_fp16, cell_activation = x_t_3_batch_first_cell_activation_0, direction = x_t_3_batch_first_direction_0, initial_c = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = x_t_3_batch_first_output_sequence_0, recurrent_activation = x_t_3_batch_first_recurrent_activation_0, weight_hh = concat_16_to_fp16, weight_hh_back = concat_18_to_fp16, weight_ih = concat_15_to_fp16, weight_ih_back = concat_17_to_fp16, x = transpose_2_cast_fp16)[name = string("x_t_3_batch_first_cast_fp16")];
+            tensor<int32, [3]> transpose_7_perm_0 = const()[name = string("transpose_7_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp16, [1024, 128]> text_encoder_lstms_3_fc_weight_to_fp16 = const()[name = string("text_encoder_lstms_3_fc_weight_to_fp16"), val = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7614464)))];
+            tensor<fp16, [1024]> text_encoder_lstms_3_fc_bias_to_fp16 = const()[name = string("text_encoder_lstms_3_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7876672)))];
+            tensor<fp16, [1, 1024]> linear_1_cast_fp16 = linear(bias = text_encoder_lstms_3_fc_bias_to_fp16, weight = text_encoder_lstms_3_fc_weight_to_fp16, x = s_to_fp16)[name = string("linear_1_cast_fp16")];
+            tensor<int32, [3]> var_179 = const()[name = string("op_179"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp16, [1, 1024, 1]> h_7_cast_fp16 = reshape(shape = var_179, x = linear_1_cast_fp16)[name = string("h_7_cast_fp16")];
+            tensor<int32, [2]> var_181_split_sizes_0 = const()[name = string("op_181_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            int32 var_181_axis_0 = const()[name = string("op_181_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 512, 1]> var_181_cast_fp16_0, tensor<fp16, [1, 512, 1]> var_181_cast_fp16_1 = split(axis = var_181_axis_0, split_sizes = var_181_split_sizes_0, x = h_7_cast_fp16)[name = string("op_181_cast_fp16")];
+            tensor<int32, [3]> gamma_7_perm_0 = const()[name = string("gamma_7_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [3]> beta_7_perm_0 = const()[name = string("beta_7_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [1]> x_23_axes_0 = const()[name = string("x_23_axes_0"), val = tensor<int32, [1]>([-1])];
+            fp16 var_164_to_fp16 = const()[name = string("op_164_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> transpose_7_cast_fp16 = transpose(perm = transpose_7_perm_0, x = x_t_3_batch_first_cast_fp16_0)[name = string("transpose_20")];
+            tensor<fp16, [1, ?, 512]> x_23_cast_fp16 = layer_norm(axes = x_23_axes_0, epsilon = var_164_to_fp16, x = transpose_7_cast_fp16)[name = string("x_23_cast_fp16")];
+            fp16 var_187_promoted_to_fp16 = const()[name = string("op_187_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1, 512]> gamma_7_cast_fp16 = transpose(perm = gamma_7_perm_0, x = var_181_cast_fp16_0)[name = string("transpose_19")];
+            tensor<fp16, [1, 1, 512]> var_188_cast_fp16 = add(x = gamma_7_cast_fp16, y = var_187_promoted_to_fp16)[name = string("op_188_cast_fp16")];
+            tensor<fp16, [1, ?, 512]> var_189_cast_fp16 = mul(x = var_188_cast_fp16, y = x_23_cast_fp16)[name = string("op_189_cast_fp16")];
+            tensor<fp16, [1, 1, 512]> beta_7_cast_fp16 = transpose(perm = beta_7_perm_0, x = var_181_cast_fp16_1)[name = string("transpose_18")];
+            tensor<fp16, [1, ?, 512]> x_25_cast_fp16 = add(x = var_189_cast_fp16, y = beta_7_cast_fp16)[name = string("x_25_cast_fp16")];
+            tensor<int32, [3]> x_27_perm_0 = const()[name = string("x_27_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
+            int32 var_197 = const()[name = string("op_197"), val = int32(1)];
+            bool var_198_interleave_0 = const()[name = string("op_198_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 512, ?]> x_27_cast_fp16 = transpose(perm = x_27_perm_0, x = x_25_cast_fp16)[name = string("transpose_17")];
+            tensor<fp16, [1, ?, ?]> var_198_cast_fp16 = concat(axis = var_197, interleave = var_198_interleave_0, values = (x_27_cast_fp16, s_exp_cast_fp16))[name = string("op_198_cast_fp16")];
+            tensor<fp16, [1, ?, ?]> x_29_cast_fp16 = mul(x = var_198_cast_fp16, y = keep_cast_fp16)[name = string("x_29_cast_fp16")];
+            tensor<int32, [3]> transpose_4_perm_0 = const()[name = string("transpose_4_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])];
+            string x_t_batch_first_direction_0 = const()[name = string("x_t_batch_first_direction_0"), val = string("bidirectional")];
+            bool x_t_batch_first_output_sequence_0 = const()[name = string("x_t_batch_first_output_sequence_0"), val = bool(true)];
+            string x_t_batch_first_recurrent_activation_0 = const()[name = string("x_t_batch_first_recurrent_activation_0"), val = string("sigmoid")];
+            string x_t_batch_first_cell_activation_0 = const()[name = string("x_t_batch_first_cell_activation_0"), val = string("tanh")];
+            string x_t_batch_first_activation_0 = const()[name = string("x_t_batch_first_activation_0"), val = string("tanh")];
+            tensor<fp16, [1024, 640]> concat_25_to_fp16 = const()[name = string("concat_25_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7878784)))];
+            tensor<fp16, [1024, 256]> concat_26_to_fp16 = const()[name = string("concat_26_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9189568)))];
+            tensor<fp16, [1024]> add_4_to_fp16 = const()[name = string("add_4_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9713920)))];
+            tensor<fp16, [1024, 640]> concat_27_to_fp16 = const()[name = string("concat_27_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9716032)))];
+            tensor<fp16, [1024, 256]> concat_28_to_fp16 = const()[name = string("concat_28_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11026816)))];
+            tensor<fp16, [1024]> add_5_to_fp16 = const()[name = string("add_5_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11551168)))];
+            tensor<fp16, [?, 1, ?]> transpose_4_cast_fp16 = transpose(perm = transpose_4_perm_0, x = x_29_cast_fp16)[name = string("transpose_16")];
+            tensor<fp16, [?, 1, 512]> x_t_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> x_t_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> x_t_batch_first_cast_fp16_2 = lstm(activation = x_t_batch_first_activation_0, bias = add_4_to_fp16, bias_back = add_5_to_fp16, cell_activation = x_t_batch_first_cell_activation_0, direction = x_t_batch_first_direction_0, initial_c = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = x_t_batch_first_output_sequence_0, recurrent_activation = x_t_batch_first_recurrent_activation_0, weight_hh = concat_26_to_fp16, weight_hh_back = concat_28_to_fp16, weight_ih = concat_25_to_fp16, weight_ih_back = concat_27_to_fp16, x = transpose_4_cast_fp16)[name = string("x_t_batch_first_cast_fp16")];
+            tensor<int32, [3]> transpose_8_perm_0 = const()[name = string("transpose_8_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp16, [1024, 128]> text_encoder_lstms_5_fc_weight_to_fp16 = const()[name = string("text_encoder_lstms_5_fc_weight_to_fp16"), val = tensor<fp16, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11553280)))];
+            tensor<fp16, [1024]> text_encoder_lstms_5_fc_bias_to_fp16 = const()[name = string("text_encoder_lstms_5_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11815488)))];
+            tensor<fp16, [1, 1024]> linear_2_cast_fp16 = linear(bias = text_encoder_lstms_5_fc_bias_to_fp16, weight = text_encoder_lstms_5_fc_weight_to_fp16, x = s_to_fp16)[name = string("linear_2_cast_fp16")];
+            tensor<int32, [3]> var_253 = const()[name = string("op_253"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp16, [1, 1024, 1]> h_cast_fp16 = reshape(shape = var_253, x = linear_2_cast_fp16)[name = string("h_cast_fp16")];
+            tensor<int32, [2]> var_255_split_sizes_0 = const()[name = string("op_255_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            int32 var_255_axis_0 = const()[name = string("op_255_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 512, 1]> var_255_cast_fp16_0, tensor<fp16, [1, 512, 1]> var_255_cast_fp16_1 = split(axis = var_255_axis_0, split_sizes = var_255_split_sizes_0, x = h_cast_fp16)[name = string("op_255_cast_fp16")];
+            tensor<int32, [3]> gamma_perm_0 = const()[name = string("gamma_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [3]> beta_perm_0 = const()[name = string("beta_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [1]> x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor<int32, [1]>([-1])];
+            fp16 var_238_to_fp16 = const()[name = string("op_238_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> transpose_8_cast_fp16 = transpose(perm = transpose_8_perm_0, x = x_t_batch_first_cast_fp16_0)[name = string("transpose_15")];
+            tensor<fp16, [1, ?, 512]> x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_238_to_fp16, x = transpose_8_cast_fp16)[name = string("x_37_cast_fp16")];
+            fp16 var_261_promoted_to_fp16 = const()[name = string("op_261_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1, 512]> gamma_cast_fp16 = transpose(perm = gamma_perm_0, x = var_255_cast_fp16_0)[name = string("transpose_14")];
+            tensor<fp16, [1, 1, 512]> var_262_cast_fp16 = add(x = gamma_cast_fp16, y = var_261_promoted_to_fp16)[name = string("op_262_cast_fp16")];
+            tensor<fp16, [1, ?, 512]> var_263_cast_fp16 = mul(x = var_262_cast_fp16, y = x_37_cast_fp16)[name = string("op_263_cast_fp16")];
+            tensor<fp16, [1, 1, 512]> beta_cast_fp16 = transpose(perm = beta_perm_0, x = var_255_cast_fp16_1)[name = string("transpose_13")];
+            tensor<fp16, [1, ?, 512]> x_39_cast_fp16 = add(x = var_263_cast_fp16, y = beta_cast_fp16)[name = string("x_39_cast_fp16")];
+            tensor<int32, [3]> x_41_perm_0 = const()[name = string("x_41_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
+            int32 var_271 = const()[name = string("op_271"), val = int32(1)];
+            bool var_272_interleave_0 = const()[name = string("op_272_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 512, ?]> x_41_cast_fp16 = transpose(perm = x_41_perm_0, x = x_39_cast_fp16)[name = string("transpose_12")];
+            tensor<fp16, [1, ?, ?]> var_272_cast_fp16 = concat(axis = var_271, interleave = var_272_interleave_0, values = (x_41_cast_fp16, s_exp_cast_fp16))[name = string("op_272_cast_fp16")];
+            tensor<fp16, [1, ?, ?]> x_cast_fp16 = mul(x = var_272_cast_fp16, y = keep_cast_fp16)[name = string("x_cast_fp16")];
+            tensor<int32, [3]> input_13_perm_0 = const()[name = string("input_13_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
+            tensor<int32, [3]> input_13_batch_first_transpose_perm_0 = const()[name = string("input_13_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            string input_batch_first_direction_0 = const()[name = string("input_batch_first_direction_0"), val = string("bidirectional")];
+            bool input_batch_first_output_sequence_0 = const()[name = string("input_batch_first_output_sequence_0"), val = bool(true)];
+            string input_batch_first_recurrent_activation_0 = const()[name = string("input_batch_first_recurrent_activation_0"), val = string("sigmoid")];
+            string input_batch_first_cell_activation_0 = const()[name = string("input_batch_first_cell_activation_0"), val = string("tanh")];
+            string input_batch_first_activation_0 = const()[name = string("input_batch_first_activation_0"), val = string("tanh")];
+            tensor<fp16, [1024, 640]> concat_35_to_fp16 = const()[name = string("concat_35_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11817600)))];
+            tensor<fp16, [1024, 256]> concat_36_to_fp16 = const()[name = string("concat_36_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13128384)))];
+            tensor<fp16, [1024]> add_6_to_fp16 = const()[name = string("add_6_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13652736)))];
+            tensor<fp16, [1024, 640]> concat_37_to_fp16 = const()[name = string("concat_37_to_fp16"), val = tensor<fp16, [1024, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13654848)))];
+            tensor<fp16, [1024, 256]> concat_38_to_fp16 = const()[name = string("concat_38_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14965632)))];
+            tensor<fp16, [1024]> add_7_to_fp16 = const()[name = string("add_7_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15489984)))];
+            tensor<fp16, [1, ?, ?]> input_13 = transpose(perm = input_13_perm_0, x = x_cast_fp16)[name = string("transpose_11")];
+            tensor<fp16, [?, 1, ?]> input_13_batch_first_transpose_cast_fp16 = transpose(perm = input_13_batch_first_transpose_perm_0, x = input_13)[name = string("transpose_10")];
+            tensor<fp16, [?, 1, 512]> input_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> input_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> input_batch_first_cast_fp16_2 = lstm(activation = input_batch_first_activation_0, bias = add_6_to_fp16, bias_back = add_7_to_fp16, cell_activation = input_batch_first_cell_activation_0, direction = input_batch_first_direction_0, initial_c = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_t_1_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = input_batch_first_output_sequence_0, recurrent_activation = input_batch_first_recurrent_activation_0, weight_hh = concat_36_to_fp16, weight_hh_back = concat_38_to_fp16, weight_ih = concat_35_to_fp16, weight_ih_back = concat_37_to_fp16, x = input_13_batch_first_transpose_cast_fp16)[name = string("input_batch_first_cast_fp16")];
+            tensor<int32, [3]> input_perm_0 = const()[name = string("input_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp16, [50, 512]> duration_proj_linear_layer_weight_to_fp16 = const()[name = string("duration_proj_linear_layer_weight_to_fp16"), val = tensor<fp16, [50, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15492096)))];
+            tensor<fp16, [50]> duration_proj_linear_layer_bias_to_fp16 = const()[name = string("duration_proj_linear_layer_bias_to_fp16"), val = tensor<fp16, [50]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15543360)))];
+            tensor<fp16, [1, ?, 512]> input_cast_fp16 = transpose(perm = input_perm_0, x = input_batch_first_cast_fp16_0)[name = string("transpose_9")];
+            tensor<fp16, [1, ?, 50]> var_308 = linear(bias = duration_proj_linear_layer_bias_to_fp16, weight = duration_proj_linear_layer_weight_to_fp16, x = input_cast_fp16)[name = string("linear_3_cast_fp16")];
+        } -> (input_13, var_308);
+}

iteration_3/compiled/duration_predictor_fp16.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:75ba0b7b2f7dc6a687e9ec01d226c300b09f07832d8e4aac2705a16b5079910c
+size 15543524

iteration_3/compiled/fused_diffusion_sampler_fp16.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e3e3712822e90082f61de1ef08f1c17995ac7a95a2fc9cf8c2fa9f61fb91db83
+size 243

iteration_3/compiled/fused_diffusion_sampler_fp16.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba4800873024c9a666cabcbfbead42618196837c35e99959bf3f87a4a5ac85c9
+size 461

iteration_3/compiled/fused_diffusion_sampler_fp16.mlmodelc/metadata.json

@@ -0,0 +1,110 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 1 × 256)",
+        "shortDescription" : "",
+        "shape" : "[1, 1, 256]",
+        "name" : "var_6189",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.expandDims" : 16,
+      "Ios18.softmax" : 24,
+      "Ios18.mul" : 117,
+      "Ios18.matmul" : 48,
+      "Ios16.reduceMean" : 8,
+      "Split" : 72,
+      "Tile" : 16,
+      "Ios18.add" : 188,
+      "Ios16.reduceSum" : 8,
+      "Ios18.layerNorm" : 24,
+      "Ios18.reshape" : 102,
+      "Ios18.linear" : 143,
+      "Ios18.conv" : 8,
+      "Ios18.gelu" : 41,
+      "Ios18.sub" : 8,
+      "Ios18.concat" : 8,
+      "Stack" : 8,
+      "Ios18.transpose" : 216,
+      "Ios18.cast" : 4,
+      "Ios18.sliceByIndex" : 4
+    },
+    "computePrecision" : "Mixed (Float16, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-05-08",
+      "com.github.apple.coremltools.source" : "torch==2.11.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 1 × 256)",
+        "shortDescription" : "",
+        "shape" : "[1, 1, 256]",
+        "name" : "noise_init",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 4 × 1 × 1 × 256)",
+        "shortDescription" : "",
+        "shape" : "[4, 1, 1, 256]",
+        "name" : "noises_aux",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 57 × 768)",
+        "shortDescription" : "",
+        "shape" : "[1, 57, 768]",
+        "name" : "embedding",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 256)",
+        "shortDescription" : "",
+        "shape" : "[1, 256]",
+        "name" : "features",
+        "type" : "MultiArray"
+      }
+    ],
+    "generatedClassName" : "fused_diffusion_sampler_fp16",
+    "method" : "predict"
+  }
+]

iteration_3/compiled/fused_diffusion_sampler_fp16.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f823b5c638d2eb2fd91bf8e4efe4a90b2e1d3d9e2f5ab40e7e93cb03cd212aca
+size 49361856

iteration_3/compiled/fused_f0n_har_source.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9290278bda868d2249e00b18d124cf5db6d5e0d9ceb8ab4c5a7fd76e9f0479f8
+size 243

iteration_3/compiled/fused_f0n_har_source.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:40447481ecde8b79cac8749014144cce35c81fd53a57add0f3b1e459e58ff6a7
+size 441

iteration_3/compiled/fused_f0n_har_source.mlmodelc/metadata.json

@@ -0,0 +1,113 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float32",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "f0",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "var_496",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "var_537",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.expandDims" : 5,
+      "Ios18.mul" : 24,
+      "UpsampleNearestNeighbor" : 3,
+      "Ios18.sin" : 1,
+      "Split" : 12,
+      "Ios18.greater" : 1,
+      "Ios18.add" : 30,
+      "Ios18.reshape" : 12,
+      "Ios18.instanceNorm" : 11,
+      "Ios16.upsampleBilinear" : 1,
+      "Ios18.leakyRelu" : 12,
+      "Ios18.linear" : 13,
+      "Ios18.conv" : 16,
+      "Ios18.lstm" : 1,
+      "Ios18.transpose" : 7,
+      "Ios18.cast" : 1,
+      "Ios18.tanh" : 1,
+      "Ios18.convTranspose" : 2,
+      "Ios18.sliceByIndex" : 2,
+      "Ios16.cumsum" : 1,
+      "Ios18.squeeze" : 6
+    },
+    "computePrecision" : "Mixed (Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-05-08",
+      "com.github.apple.coremltools.source" : "torch==2.11.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "inputSchema" : [
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 640 × 1...2048",
+        "shapeRange" : "[[1, 1], [640, 640], [1, 2048]]",
+        "formattedType" : "MultiArray (Float32 1 × 640 × 147)",
+        "type" : "MultiArray",
+        "shape" : "[1, 640, 147]",
+        "name" : "en",
+        "shortDescription" : ""
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 128)",
+        "shortDescription" : "",
+        "shape" : "[1, 128]",
+        "name" : "s",
+        "type" : "MultiArray"
+      }
+    ],
+    "generatedClassName" : "fused_f0n_har_source",
+    "method" : "predict"
+  }
+]

iteration_3/compiled/fused_f0n_har_source.mlmodelc/model.mil

@@ -0,0 +1,431 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 640, ?]> en, tensor<fp32, [1, 128]> s) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"en", [1, 640, 147]}}), ("RangeDims", {{"en", [[1, 1], [640, 640], [1, 2048]]}})))] {
+            tensor<fp32, [1024]> f0n_wrap_predictor_F0_0_norm1_fc_bias = const()[name = string("f0n_wrap_predictor_F0_0_norm1_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [1024, 128]> f0n_wrap_predictor_F0_0_norm1_fc_weight = const()[name = string("f0n_wrap_predictor_F0_0_norm1_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4224)))];
+            tensor<fp32, [512]> f0n_wrap_predictor_F0_0_conv1_bias = const()[name = string("f0n_wrap_predictor_F0_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(528576)))];
+            tensor<fp32, [1024]> f0n_wrap_predictor_F0_0_norm2_fc_bias = const()[name = string("f0n_wrap_predictor_F0_0_norm2_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530688)))];
+            tensor<fp32, [1024, 128]> f0n_wrap_predictor_F0_0_norm2_fc_weight = const()[name = string("f0n_wrap_predictor_F0_0_norm2_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(534848)))];
+            tensor<fp32, [512]> f0n_wrap_predictor_F0_0_conv2_bias = const()[name = string("f0n_wrap_predictor_F0_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1059200)))];
+            tensor<fp32, [1024]> f0n_wrap_predictor_F0_1_norm1_fc_bias = const()[name = string("f0n_wrap_predictor_F0_1_norm1_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1061312)))];
+            tensor<fp32, [1024, 128]> f0n_wrap_predictor_F0_1_norm1_fc_weight = const()[name = string("f0n_wrap_predictor_F0_1_norm1_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1065472)))];
+            tensor<fp32, [512]> f0n_wrap_predictor_F0_1_pool_bias = const()[name = string("f0n_wrap_predictor_F0_1_pool_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1589824)))];
+            tensor<fp32, [256]> f0n_wrap_predictor_F0_1_conv1_bias = const()[name = string("f0n_wrap_predictor_F0_1_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1591936)))];
+            tensor<fp32, [512]> f0n_wrap_predictor_F0_1_norm2_fc_bias = const()[name = string("f0n_wrap_predictor_F0_1_norm2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1593024)))];
+            tensor<fp32, [512, 128]> f0n_wrap_predictor_F0_1_norm2_fc_weight = const()[name = string("f0n_wrap_predictor_F0_1_norm2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1595136)))];
+            tensor<fp32, [256]> f0n_wrap_predictor_F0_1_conv2_bias = const()[name = string("f0n_wrap_predictor_F0_1_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1857344)))];
+            tensor<fp32, [512]> f0n_wrap_predictor_F0_2_norm1_fc_bias = const()[name = string("f0n_wrap_predictor_F0_2_norm1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1858432)))];
+            tensor<fp32, [512, 128]> f0n_wrap_predictor_F0_2_norm1_fc_weight = const()[name = string("f0n_wrap_predictor_F0_2_norm1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1860544)))];
+            tensor<fp32, [256]> f0n_wrap_predictor_F0_2_conv1_bias = const()[name = string("f0n_wrap_predictor_F0_2_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2122752)))];
+            tensor<fp32, [512]> f0n_wrap_predictor_F0_2_norm2_fc_bias = const()[name = string("f0n_wrap_predictor_F0_2_norm2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2123840)))];
+            tensor<fp32, [512, 128]> f0n_wrap_predictor_F0_2_norm2_fc_weight = const()[name = string("f0n_wrap_predictor_F0_2_norm2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2125952)))];
+            tensor<fp32, [256]> f0n_wrap_predictor_F0_2_conv2_bias = const()[name = string("f0n_wrap_predictor_F0_2_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2388160)))];
+            tensor<fp32, [1]> f0n_wrap_predictor_F0_proj_bias = const()[name = string("f0n_wrap_predictor_F0_proj_bias"), val = tensor<fp32, [1]>([0x1.ad0bfcp-4])];
+            tensor<fp32, [1, 256, 1]> f0n_wrap_predictor_F0_proj_weight = const()[name = string("f0n_wrap_predictor_F0_proj_weight"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2389248)))];
+            tensor<fp32, [1024]> f0n_wrap_predictor_N_0_norm1_fc_bias = const()[name = string("f0n_wrap_predictor_N_0_norm1_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2390336)))];
+            tensor<fp32, [1024, 128]> f0n_wrap_predictor_N_0_norm1_fc_weight = const()[name = string("f0n_wrap_predictor_N_0_norm1_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2394496)))];
+            tensor<fp32, [512]> f0n_wrap_predictor_N_0_conv1_bias = const()[name = string("f0n_wrap_predictor_N_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2918848)))];
+            tensor<fp32, [1024]> f0n_wrap_predictor_N_0_norm2_fc_bias = const()[name = string("f0n_wrap_predictor_N_0_norm2_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2920960)))];
+            tensor<fp32, [1024, 128]> f0n_wrap_predictor_N_0_norm2_fc_weight = const()[name = string("f0n_wrap_predictor_N_0_norm2_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2925120)))];
+            tensor<fp32, [512]> f0n_wrap_predictor_N_0_conv2_bias = const()[name = string("f0n_wrap_predictor_N_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3449472)))];
+            tensor<fp32, [1024]> f0n_wrap_predictor_N_1_norm1_fc_bias = const()[name = string("f0n_wrap_predictor_N_1_norm1_fc_bias"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3451584)))];
+            tensor<fp32, [1024, 128]> f0n_wrap_predictor_N_1_norm1_fc_weight = const()[name = string("f0n_wrap_predictor_N_1_norm1_fc_weight"), val = tensor<fp32, [1024, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455744)))];
+            tensor<fp32, [512]> f0n_wrap_predictor_N_1_pool_bias = const()[name = string("f0n_wrap_predictor_N_1_pool_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3980096)))];
+            tensor<fp32, [256]> f0n_wrap_predictor_N_1_conv1_bias = const()[name = string("f0n_wrap_predictor_N_1_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3982208)))];
+            tensor<fp32, [512]> f0n_wrap_predictor_N_1_norm2_fc_bias = const()[name = string("f0n_wrap_predictor_N_1_norm2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3983296)))];
+            tensor<fp32, [512, 128]> f0n_wrap_predictor_N_1_norm2_fc_weight = const()[name = string("f0n_wrap_predictor_N_1_norm2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3985408)))];
+            tensor<fp32, [256]> f0n_wrap_predictor_N_1_conv2_bias = const()[name = string("f0n_wrap_predictor_N_1_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4247616)))];
+            tensor<fp32, [512]> f0n_wrap_predictor_N_2_norm1_fc_bias = const()[name = string("f0n_wrap_predictor_N_2_norm1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4248704)))];
+            tensor<fp32, [512, 128]> f0n_wrap_predictor_N_2_norm1_fc_weight = const()[name = string("f0n_wrap_predictor_N_2_norm1_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4250816)))];
+            tensor<fp32, [256]> f0n_wrap_predictor_N_2_conv1_bias = const()[name = string("f0n_wrap_predictor_N_2_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4513024)))];
+            tensor<fp32, [512]> f0n_wrap_predictor_N_2_norm2_fc_bias = const()[name = string("f0n_wrap_predictor_N_2_norm2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4514112)))];
+            tensor<fp32, [512, 128]> f0n_wrap_predictor_N_2_norm2_fc_weight = const()[name = string("f0n_wrap_predictor_N_2_norm2_fc_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4516224)))];
+            tensor<fp32, [256]> f0n_wrap_predictor_N_2_conv2_bias = const()[name = string("f0n_wrap_predictor_N_2_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4778432)))];
+            tensor<fp32, [1]> f0n_wrap_predictor_N_proj_bias = const()[name = string("f0n_wrap_predictor_N_proj_bias"), val = tensor<fp32, [1]>([0x1.6de2cap-4])];
+            tensor<fp32, [1, 256, 1]> f0n_wrap_predictor_N_proj_weight = const()[name = string("f0n_wrap_predictor_N_proj_weight"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4779520)))];
+            tensor<fp32, [1, 1, 9]> har_wrap_harmonics = const()[name = string("har_wrap_harmonics"), val = tensor<fp32, [1, 1, 9]>([[[0x1p+0, 0x1p+1, 0x1.8p+1, 0x1p+2, 0x1.4p+2, 0x1.8p+2, 0x1.cp+2, 0x1p+3, 0x1.2p+3]]])];
+            tensor<fp32, [1]> har_wrap_l_linear_bias = const()[name = string("har_wrap_l_linear_bias"), val = tensor<fp32, [1]>([0x1.23e7f2p-6])];
+            tensor<fp32, [1, 9]> har_wrap_l_linear_weight = const()[name = string("har_wrap_l_linear_weight"), val = tensor<fp32, [1, 9]>([[-0x1.2aaee4p-12, 0x1.3872d6p-4, -0x1.e6ccbep-7, 0x1.e1debcp-8, -0x1.6ca714p-10, 0x1.8ba75p-10, 0x1.b33eecp-9, -0x1.cd2accp-8, 0x1.07f694p-7]])];
+            fp32 var_8 = const()[name = string("op_8"), val = fp32(0x1.4f8b58p-17)];
+            fp32 var_9 = const()[name = string("op_9"), val = fp32(0x1.99999ap-3)];
+            tensor<int32, [3]> transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])];
+            tensor<fp32, [1024]> add_0 = const()[name = string("add_0"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4780608)))];
+            tensor<fp32, [1024]> add_1 = const()[name = string("add_1"), val = tensor<fp32, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4784768)))];
+            tensor<fp32, [1024, 640]> concat_4 = const()[name = string("concat_4"), val = tensor<fp32, [1024, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4788928)))];
+            tensor<fp32, [1024, 256]> concat_5 = const()[name = string("concat_5"), val = tensor<fp32, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7410432)))];
+            tensor<fp32, [1024, 640]> concat_6 = const()[name = string("concat_6"), val = tensor<fp32, [1024, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8459072)))];
+            tensor<fp32, [1024, 256]> concat_7 = const()[name = string("concat_7"), val = tensor<fp32, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11080576)))];
+            tensor<fp32, [1, 512]> x_batch_first_lstm_h0_reshaped = const()[name = string("x_batch_first_lstm_h0_reshaped"), val = tensor<fp32, [1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12129216)))];
+            string x_batch_first_direction_0 = const()[name = string("x_batch_first_direction_0"), val = string("bidirectional")];
+            bool x_batch_first_output_sequence_0 = const()[name = string("x_batch_first_output_sequence_0"), val = bool(true)];
+            string x_batch_first_recurrent_activation_0 = const()[name = string("x_batch_first_recurrent_activation_0"), val = string("sigmoid")];
+            string x_batch_first_cell_activation_0 = const()[name = string("x_batch_first_cell_activation_0"), val = string("tanh")];
+            string x_batch_first_activation_0 = const()[name = string("x_batch_first_activation_0"), val = string("tanh")];
+            tensor<fp32, [?, 1, 640]> transpose_1 = transpose(perm = transpose_1_perm_0, x = en)[name = string("transpose_8")];
+            tensor<fp32, [?, 1, 512]> x_batch_first_0, tensor<fp32, [1, 512]> x_batch_first_1, tensor<fp32, [1, 512]> x_batch_first_2 = lstm(activation = x_batch_first_activation_0, bias = add_0, bias_back = add_1, cell_activation = x_batch_first_cell_activation_0, direction = x_batch_first_direction_0, initial_c = x_batch_first_lstm_h0_reshaped, initial_h = x_batch_first_lstm_h0_reshaped, output_sequence = x_batch_first_output_sequence_0, recurrent_activation = x_batch_first_recurrent_activation_0, weight_hh = concat_5, weight_hh_back = concat_7, weight_ih = concat_4, weight_ih_back = concat_6, x = transpose_1)[name = string("x_batch_first")];
+            tensor<int32, [3]> x_perm_0 = const()[name = string("x_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<int32, [3]> input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
+            tensor<fp32, [1, 1024]> h_1 = linear(bias = f0n_wrap_predictor_F0_0_norm1_fc_bias, weight = f0n_wrap_predictor_F0_0_norm1_fc_weight, x = s)[name = string("linear_0")];
+            tensor<int32, [3]> var_84 = const()[name = string("op_84"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp32, [1, 1024, 1]> h_3 = reshape(shape = var_84, x = h_1)[name = string("h_3")];
+            tensor<int32, [2]> var_86_split_sizes_0 = const()[name = string("op_86_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            int32 var_86_axis_0 = const()[name = string("op_86_axis_0"), val = int32(1)];
+            tensor<fp32, [1, 512, 1]> var_86_0, tensor<fp32, [1, 512, 1]> var_86_1 = split(axis = var_86_axis_0, split_sizes = var_86_split_sizes_0, x = h_3)[name = string("op_86")];
+            fp32 var_88_promoted = const()[name = string("op_88_promoted"), val = fp32(0x1p+0)];
+            tensor<fp32, [1, 512, 1]> var_89 = add(x = var_86_0, y = var_88_promoted)[name = string("op_89")];
+            tensor<fp32, [1, ?, 512]> x = transpose(perm = x_perm_0, x = x_batch_first_0)[name = string("transpose_7")];
+            tensor<fp32, [1, 512, ?]> input_3 = transpose(perm = input_3_perm_0, x = x)[name = string("transpose_6")];
+            tensor<fp32, [1, 512, ?]> var_90 = instance_norm(epsilon = var_8, x = input_3)[name = string("op_90")];
+            tensor<fp32, [1, 512, ?]> var_91 = mul(x = var_89, y = var_90)[name = string("op_91")];
+            tensor<fp32, [1, 512, ?]> input_5 = add(x = var_91, y = var_86_1)[name = string("input_5")];
+            tensor<fp32, [1, 512, ?]> input_7 = leaky_relu(alpha = var_9, x = input_5)[name = string("input_7")];
+            tensor<fp32, [512, 512, 3]> weight_1 = const()[name = string("weight_1"), val = tensor<fp32, [512, 512, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12131328)))];
+            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 512, ?]> input_9 = conv(bias = f0n_wrap_predictor_F0_0_conv1_bias, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = weight_1, x = input_7)[name = string("input_9")];
+            tensor<fp32, [1, 1024]> h_5 = linear(bias = f0n_wrap_predictor_F0_0_norm2_fc_bias, weight = f0n_wrap_predictor_F0_0_norm2_fc_weight, x = s)[name = string("linear_1")];
+            tensor<int32, [3]> var_107 = const()[name = string("op_107"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp32, [1, 1024, 1]> h_7 = reshape(shape = var_107, x = h_5)[name = string("h_7")];
+            tensor<int32, [2]> var_109_split_sizes_0 = const()[name = string("op_109_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            int32 var_109_axis_0 = const()[name = string("op_109_axis_0"), val = int32(1)];
+            tensor<fp32, [1, 512, 1]> var_109_0, tensor<fp32, [1, 512, 1]> var_109_1 = split(axis = var_109_axis_0, split_sizes = var_109_split_sizes_0, x = h_7)[name = string("op_109")];
+            fp32 var_111_promoted = const()[name = string("op_111_promoted"), val = fp32(0x1p+0)];
+            tensor<fp32, [1, 512, 1]> var_112 = add(x = var_109_0, y = var_111_promoted)[name = string("op_112")];
+            tensor<fp32, [1, 512, ?]> var_113 = instance_norm(epsilon = var_8, x = input_9)[name = string("op_113")];
+            tensor<fp32, [1, 512, ?]> var_114 = mul(x = var_112, y = var_113)[name = string("op_114")];
+            tensor<fp32, [1, 512, ?]> input_11 = add(x = var_114, y = var_109_1)[name = string("input_11")];
+            tensor<fp32, [1, 512, ?]> input_13 = leaky_relu(alpha = var_9, x = input_11)[name = string("input_13")];
+            tensor<fp32, [512, 512, 3]> weight_3 = const()[name = string("weight_3"), val = tensor<fp32, [512, 512, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15277120)))];
+            string out_1_pad_type_0 = const()[name = string("out_1_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> out_1_pad_0 = const()[name = string("out_1_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_1_strides_0 = const()[name = string("out_1_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_1_dilations_0 = const()[name = string("out_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 out_1_groups_0 = const()[name = string("out_1_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 512, ?]> out_1 = conv(bias = f0n_wrap_predictor_F0_0_conv2_bias, dilations = out_1_dilations_0, groups = out_1_groups_0, pad = out_1_pad_0, pad_type = out_1_pad_type_0, strides = out_1_strides_0, weight = weight_3, x = input_13)[name = string("out_1")];
+            tensor<fp32, [1, 512, ?]> var_124 = add(x = out_1, y = input_3)[name = string("op_124")];
+            fp32 _inversed_input_15_y_0 = const()[name = string("_inversed_input_15_y_0"), val = fp32(0x1.6a09e6p-1)];
+            tensor<fp32, [1, 512, ?]> _inversed_input_15 = mul(x = var_124, y = _inversed_input_15_y_0)[name = string("_inversed_input_15")];
+            tensor<fp32, [1, 1024]> h_9 = linear(bias = f0n_wrap_predictor_F0_1_norm1_fc_bias, weight = f0n_wrap_predictor_F0_1_norm1_fc_weight, x = s)[name = string("linear_2")];
+            tensor<int32, [3]> var_154 = const()[name = string("op_154"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp32, [1, 1024, 1]> h_11 = reshape(shape = var_154, x = h_9)[name = string("h_11")];
+            tensor<int32, [2]> var_156_split_sizes_0 = const()[name = string("op_156_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            int32 var_156_axis_0 = const()[name = string("op_156_axis_0"), val = int32(1)];
+            tensor<fp32, [1, 512, 1]> var_156_0, tensor<fp32, [1, 512, 1]> var_156_1 = split(axis = var_156_axis_0, split_sizes = var_156_split_sizes_0, x = h_11)[name = string("op_156")];
+            fp32 var_158_promoted = const()[name = string("op_158_promoted"), val = fp32(0x1p+0)];
+            tensor<fp32, [1, 512, 1]> var_159 = add(x = var_156_0, y = var_158_promoted)[name = string("op_159")];
+            tensor<fp32, [1, 512, ?]> var_160 = instance_norm(epsilon = var_8, x = _inversed_input_15)[name = string("op_160")];
+            tensor<fp32, [1, 512, ?]> var_161 = mul(x = var_159, y = var_160)[name = string("op_161")];
+            tensor<fp32, [1, 512, ?]> input_17 = add(x = var_161, y = var_156_1)[name = string("input_17")];
+            tensor<fp32, [1, 512, ?]> input_19 = leaky_relu(alpha = var_9, x = input_17)[name = string("input_19")];
+            tensor<fp32, [512, 1, 3]> var_164 = const()[name = string("op_164"), val = tensor<fp32, [512, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18422912)))];
+            string conv_transpose_0_pad_type_0 = const()[name = string("conv_transpose_0_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> conv_transpose_0_pad_0 = const()[name = string("conv_transpose_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_transpose_0_strides_0 = const()[name = string("conv_transpose_0_strides_0"), val = tensor<int32, [1]>([2])];
+            int32 conv_transpose_0_groups_0 = const()[name = string("conv_transpose_0_groups_0"), val = int32(512)];
+            tensor<int32, [1]> conv_transpose_0_dilations_0 = const()[name = string("conv_transpose_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 512, ?]> conv_transpose_0 = conv_transpose(bias = f0n_wrap_predictor_F0_1_pool_bias, dilations = conv_transpose_0_dilations_0, groups = conv_transpose_0_groups_0, pad = conv_transpose_0_pad_0, pad_type = conv_transpose_0_pad_type_0, strides = conv_transpose_0_strides_0, weight = var_164, x = input_19)[name = string("conv_transpose_0")];
+            tensor<int32, [3]> input_21_begin_0 = const()[name = string("input_21_begin_0"), val = tensor<int32, [3]>([0, 0, 1])];
+            tensor<int32, [3]> input_21_end_0 = const()[name = string("input_21_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<bool, [3]> input_21_begin_mask_0 = const()[name = string("input_21_begin_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<bool, [3]> input_21_end_mask_0 = const()[name = string("input_21_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            tensor<fp32, [1, 512, ?]> input_21 = slice_by_index(begin = input_21_begin_0, begin_mask = input_21_begin_mask_0, end = input_21_end_0, end_mask = input_21_end_mask_0, x = conv_transpose_0)[name = string("input_21")];
+            tensor<fp32, [256, 512, 3]> weight_5 = const()[name = string("weight_5"), val = tensor<fp32, [256, 512, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18429120)))];
+            string input_23_pad_type_0 = const()[name = string("input_23_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_23_strides_0 = const()[name = string("input_23_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_23_dilations_0 = const()[name = string("input_23_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_23_groups_0 = const()[name = string("input_23_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 256, ?]> input_23 = conv(bias = f0n_wrap_predictor_F0_1_conv1_bias, dilations = input_23_dilations_0, groups = input_23_groups_0, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = input_23_strides_0, weight = weight_5, x = input_21)[name = string("input_23")];
+            tensor<fp32, [1, 512]> h_13 = linear(bias = f0n_wrap_predictor_F0_1_norm2_fc_bias, weight = f0n_wrap_predictor_F0_1_norm2_fc_weight, x = s)[name = string("linear_3")];
+            tensor<int32, [3]> var_184 = const()[name = string("op_184"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp32, [1, 512, 1]> h_15 = reshape(shape = var_184, x = h_13)[name = string("h_15")];
+            tensor<int32, [2]> var_186_split_sizes_0 = const()[name = string("op_186_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            int32 var_186_axis_0 = const()[name = string("op_186_axis_0"), val = int32(1)];
+            tensor<fp32, [1, 256, 1]> var_186_0, tensor<fp32, [1, 256, 1]> var_186_1 = split(axis = var_186_axis_0, split_sizes = var_186_split_sizes_0, x = h_15)[name = string("op_186")];
+            fp32 var_188_promoted = const()[name = string("op_188_promoted"), val = fp32(0x1p+0)];
+            tensor<fp32, [1, 256, 1]> var_189 = add(x = var_186_0, y = var_188_promoted)[name = string("op_189")];
+            tensor<fp32, [1, 256, ?]> var_190 = instance_norm(epsilon = var_8, x = input_23)[name = string("op_190")];
+            tensor<fp32, [1, 256, ?]> var_191 = mul(x = var_189, y = var_190)[name = string("op_191")];
+            tensor<fp32, [1, 256, ?]> input_25 = add(x = var_191, y = var_186_1)[name = string("input_25")];
+            tensor<fp32, [1, 256, ?]> input_27 = leaky_relu(alpha = var_9, x = input_25)[name = string("input_27")];
+            tensor<fp32, [256, 256, 3]> weight_7 = const()[name = string("weight_7"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20002048)))];
+            string out_3_pad_type_0 = const()[name = string("out_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> out_3_pad_0 = const()[name = string("out_3_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_3_strides_0 = const()[name = string("out_3_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_3_dilations_0 = const()[name = string("out_3_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 out_3_groups_0 = const()[name = string("out_3_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 256, ?]> out_3 = conv(bias = f0n_wrap_predictor_F0_1_conv2_bias, dilations = out_3_dilations_0, groups = out_3_groups_0, pad = out_3_pad_0, pad_type = out_3_pad_type_0, strides = out_3_strides_0, weight = weight_7, x = input_27)[name = string("out_3")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [1, 512, ?, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = _inversed_input_15)[name = string("expand_dims_0")];
+            int32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = int32(2)];
+            int32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = int32(1)];
+            tensor<fp32, [1, 512, ?, 1]> upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")];
+            tensor<int32, [1]> input_29_axes_0 = const()[name = string("input_29_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [1, 512, ?]> input_29 = squeeze(axes = input_29_axes_0, x = upsample_nearest_neighbor_0)[name = string("input_29")];
+            tensor<fp32, [256, 512, 1]> weight_9 = const()[name = string("weight_9"), val = tensor<fp32, [256, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20788544)))];
+            string var_208_pad_type_0 = const()[name = string("op_208_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_208_strides_0 = const()[name = string("op_208_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_208_pad_0 = const()[name = string("op_208_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_208_dilations_0 = const()[name = string("op_208_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_208_groups_0 = const()[name = string("op_208_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 256, ?]> var_208 = conv(dilations = var_208_dilations_0, groups = var_208_groups_0, pad = var_208_pad_0, pad_type = var_208_pad_type_0, strides = var_208_strides_0, weight = weight_9, x = input_29)[name = string("op_208")];
+            tensor<fp32, [1, 256, ?]> var_209 = add(x = out_3, y = var_208)[name = string("op_209")];
+            fp32 _inversed_input_31_y_0 = const()[name = string("_inversed_input_31_y_0"), val = fp32(0x1.6a09e6p-1)];
+            tensor<fp32, [1, 256, ?]> _inversed_input_31 = mul(x = var_209, y = _inversed_input_31_y_0)[name = string("_inversed_input_31")];
+            tensor<fp32, [1, 512]> h_17 = linear(bias = f0n_wrap_predictor_F0_2_norm1_fc_bias, weight = f0n_wrap_predictor_F0_2_norm1_fc_weight, x = s)[name = string("linear_4")];
+            tensor<int32, [3]> var_230 = const()[name = string("op_230"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp32, [1, 512, 1]> h_19 = reshape(shape = var_230, x = h_17)[name = string("h_19")];
+            tensor<int32, [2]> var_232_split_sizes_0 = const()[name = string("op_232_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            int32 var_232_axis_0 = const()[name = string("op_232_axis_0"), val = int32(1)];
+            tensor<fp32, [1, 256, 1]> var_232_0, tensor<fp32, [1, 256, 1]> var_232_1 = split(axis = var_232_axis_0, split_sizes = var_232_split_sizes_0, x = h_19)[name = string("op_232")];
+            fp32 var_234_promoted = const()[name = string("op_234_promoted"), val = fp32(0x1p+0)];
+            tensor<fp32, [1, 256, 1]> var_235 = add(x = var_232_0, y = var_234_promoted)[name = string("op_235")];
+            tensor<fp32, [1, 256, ?]> var_236 = instance_norm(epsilon = var_8, x = _inversed_input_31)[name = string("op_236")];
+            tensor<fp32, [1, 256, ?]> var_237 = mul(x = var_235, y = var_236)[name = string("op_237")];
+            tensor<fp32, [1, 256, ?]> input_33 = add(x = var_237, y = var_232_1)[name = string("input_33")];
+            tensor<fp32, [1, 256, ?]> input_35 = leaky_relu(alpha = var_9, x = input_33)[name = string("input_35")];
+            tensor<fp32, [256, 256, 3]> weight_11 = const()[name = string("weight_11"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21312896)))];
+            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 256, ?]> input_37 = conv(bias = f0n_wrap_predictor_F0_2_conv1_bias, dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = weight_11, x = input_35)[name = string("input_37")];
+            tensor<fp32, [1, 512]> h_21 = linear(bias = f0n_wrap_predictor_F0_2_norm2_fc_bias, weight = f0n_wrap_predictor_F0_2_norm2_fc_weight, x = s)[name = string("linear_5")];
+            tensor<int32, [3]> var_253 = const()[name = string("op_253"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp32, [1, 512, 1]> h_23 = reshape(shape = var_253, x = h_21)[name = string("h_23")];
+            tensor<int32, [2]> var_255_split_sizes_0 = const()[name = string("op_255_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            int32 var_255_axis_0 = const()[name = string("op_255_axis_0"), val = int32(1)];
+            tensor<fp32, [1, 256, 1]> var_255_0, tensor<fp32, [1, 256, 1]> var_255_1 = split(axis = var_255_axis_0, split_sizes = var_255_split_sizes_0, x = h_23)[name = string("op_255")];
+            fp32 var_257_promoted = const()[name = string("op_257_promoted"), val = fp32(0x1p+0)];
+            tensor<fp32, [1, 256, 1]> var_258 = add(x = var_255_0, y = var_257_promoted)[name = string("op_258")];
+            tensor<fp32, [1, 256, ?]> var_259 = instance_norm(epsilon = var_8, x = input_37)[name = string("op_259")];
+            tensor<fp32, [1, 256, ?]> var_260 = mul(x = var_258, y = var_259)[name = string("op_260")];
+            tensor<fp32, [1, 256, ?]> input_39 = add(x = var_260, y = var_255_1)[name = string("input_39")];
+            tensor<fp32, [1, 256, ?]> input_41 = leaky_relu(alpha = var_9, x = input_39)[name = string("input_41")];
+            tensor<fp32, [256, 256, 3]> weight_13 = const()[name = string("weight_13"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22099392)))];
+            string out_5_pad_type_0 = const()[name = string("out_5_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> out_5_pad_0 = const()[name = string("out_5_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_5_strides_0 = const()[name = string("out_5_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_5_dilations_0 = const()[name = string("out_5_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 out_5_groups_0 = const()[name = string("out_5_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 256, ?]> out_5 = conv(bias = f0n_wrap_predictor_F0_2_conv2_bias, dilations = out_5_dilations_0, groups = out_5_groups_0, pad = out_5_pad_0, pad_type = out_5_pad_type_0, strides = out_5_strides_0, weight = weight_13, x = input_41)[name = string("out_5")];
+            tensor<fp32, [1, 256, ?]> var_270 = add(x = out_5, y = _inversed_input_31)[name = string("op_270")];
+            fp32 _inversed_input_43_y_0 = const()[name = string("_inversed_input_43_y_0"), val = fp32(0x1.6a09e6p-1)];
+            tensor<fp32, [1, 256, ?]> _inversed_input_43 = mul(x = var_270, y = _inversed_input_43_y_0)[name = string("_inversed_input_43")];
+            string F0_1_pad_type_0 = const()[name = string("F0_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> F0_1_strides_0 = const()[name = string("F0_1_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> F0_1_pad_0 = const()[name = string("F0_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> F0_1_dilations_0 = const()[name = string("F0_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 F0_1_groups_0 = const()[name = string("F0_1_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 1, ?]> F0_1 = conv(bias = f0n_wrap_predictor_F0_proj_bias, dilations = F0_1_dilations_0, groups = F0_1_groups_0, pad = F0_1_pad_0, pad_type = F0_1_pad_type_0, strides = F0_1_strides_0, weight = f0n_wrap_predictor_F0_proj_weight, x = _inversed_input_43)[name = string("F0_1")];
+            tensor<fp32, [1, 1024]> h_25 = linear(bias = f0n_wrap_predictor_N_0_norm1_fc_bias, weight = f0n_wrap_predictor_N_0_norm1_fc_weight, x = s)[name = string("linear_6")];
+            tensor<int32, [3]> var_299 = const()[name = string("op_299"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp32, [1, 1024, 1]> h_27 = reshape(shape = var_299, x = h_25)[name = string("h_27")];
+            tensor<int32, [2]> var_301_split_sizes_0 = const()[name = string("op_301_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            int32 var_301_axis_0 = const()[name = string("op_301_axis_0"), val = int32(1)];
+            tensor<fp32, [1, 512, 1]> var_301_0, tensor<fp32, [1, 512, 1]> var_301_1 = split(axis = var_301_axis_0, split_sizes = var_301_split_sizes_0, x = h_27)[name = string("op_301")];
+            fp32 var_303_promoted = const()[name = string("op_303_promoted"), val = fp32(0x1p+0)];
+            tensor<fp32, [1, 512, 1]> var_304 = add(x = var_301_0, y = var_303_promoted)[name = string("op_304")];
+            tensor<fp32, [1, 512, ?]> var_306 = mul(x = var_304, y = var_90)[name = string("op_306")];
+            tensor<fp32, [1, 512, ?]> input_47 = add(x = var_306, y = var_301_1)[name = string("input_47")];
+            tensor<fp32, [1, 512, ?]> input_49 = leaky_relu(alpha = var_9, x = input_47)[name = string("input_49")];
+            tensor<fp32, [512, 512, 3]> weight_17 = const()[name = string("weight_17"), val = tensor<fp32, [512, 512, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22885888)))];
+            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 512, ?]> input_51 = conv(bias = f0n_wrap_predictor_N_0_conv1_bias, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = weight_17, x = input_49)[name = string("input_51")];
+            tensor<fp32, [1, 1024]> h_29 = linear(bias = f0n_wrap_predictor_N_0_norm2_fc_bias, weight = f0n_wrap_predictor_N_0_norm2_fc_weight, x = s)[name = string("linear_7")];
+            tensor<int32, [3]> var_322 = const()[name = string("op_322"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp32, [1, 1024, 1]> h_31 = reshape(shape = var_322, x = h_29)[name = string("h_31")];
+            tensor<int32, [2]> var_324_split_sizes_0 = const()[name = string("op_324_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            int32 var_324_axis_0 = const()[name = string("op_324_axis_0"), val = int32(1)];
+            tensor<fp32, [1, 512, 1]> var_324_0, tensor<fp32, [1, 512, 1]> var_324_1 = split(axis = var_324_axis_0, split_sizes = var_324_split_sizes_0, x = h_31)[name = string("op_324")];
+            fp32 var_326_promoted = const()[name = string("op_326_promoted"), val = fp32(0x1p+0)];
+            tensor<fp32, [1, 512, 1]> var_327 = add(x = var_324_0, y = var_326_promoted)[name = string("op_327")];
+            tensor<fp32, [1, 512, ?]> var_328 = instance_norm(epsilon = var_8, x = input_51)[name = string("op_328")];
+            tensor<fp32, [1, 512, ?]> var_329 = mul(x = var_327, y = var_328)[name = string("op_329")];
+            tensor<fp32, [1, 512, ?]> input_53 = add(x = var_329, y = var_324_1)[name = string("input_53")];
+            tensor<fp32, [1, 512, ?]> input_55 = leaky_relu(alpha = var_9, x = input_53)[name = string("input_55")];
+            tensor<fp32, [512, 512, 3]> weight_19 = const()[name = string("weight_19"), val = tensor<fp32, [512, 512, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26031680)))];
+            string out_7_pad_type_0 = const()[name = string("out_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> out_7_pad_0 = const()[name = string("out_7_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_7_strides_0 = const()[name = string("out_7_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_7_dilations_0 = const()[name = string("out_7_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 out_7_groups_0 = const()[name = string("out_7_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 512, ?]> out_7 = conv(bias = f0n_wrap_predictor_N_0_conv2_bias, dilations = out_7_dilations_0, groups = out_7_groups_0, pad = out_7_pad_0, pad_type = out_7_pad_type_0, strides = out_7_strides_0, weight = weight_19, x = input_55)[name = string("out_7")];
+            tensor<fp32, [1, 512, ?]> var_339 = add(x = out_7, y = input_3)[name = string("op_339")];
+            fp32 _inversed_input_57_y_0 = const()[name = string("_inversed_input_57_y_0"), val = fp32(0x1.6a09e6p-1)];
+            tensor<fp32, [1, 512, ?]> _inversed_input_57 = mul(x = var_339, y = _inversed_input_57_y_0)[name = string("_inversed_input_57")];
+            tensor<fp32, [1, 1024]> h_33 = linear(bias = f0n_wrap_predictor_N_1_norm1_fc_bias, weight = f0n_wrap_predictor_N_1_norm1_fc_weight, x = s)[name = string("linear_8")];
+            tensor<int32, [3]> var_369 = const()[name = string("op_369"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp32, [1, 1024, 1]> h_35 = reshape(shape = var_369, x = h_33)[name = string("h_35")];
+            tensor<int32, [2]> var_371_split_sizes_0 = const()[name = string("op_371_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            int32 var_371_axis_0 = const()[name = string("op_371_axis_0"), val = int32(1)];
+            tensor<fp32, [1, 512, 1]> var_371_0, tensor<fp32, [1, 512, 1]> var_371_1 = split(axis = var_371_axis_0, split_sizes = var_371_split_sizes_0, x = h_35)[name = string("op_371")];
+            fp32 var_373_promoted = const()[name = string("op_373_promoted"), val = fp32(0x1p+0)];
+            tensor<fp32, [1, 512, 1]> var_374 = add(x = var_371_0, y = var_373_promoted)[name = string("op_374")];
+            tensor<fp32, [1, 512, ?]> var_375 = instance_norm(epsilon = var_8, x = _inversed_input_57)[name = string("op_375")];
+            tensor<fp32, [1, 512, ?]> var_376 = mul(x = var_374, y = var_375)[name = string("op_376")];
+            tensor<fp32, [1, 512, ?]> input_59 = add(x = var_376, y = var_371_1)[name = string("input_59")];
+            tensor<fp32, [1, 512, ?]> input_61 = leaky_relu(alpha = var_9, x = input_59)[name = string("input_61")];
+            tensor<fp32, [512, 1, 3]> var_379 = const()[name = string("op_379"), val = tensor<fp32, [512, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29177472)))];
+            string conv_transpose_1_pad_type_0 = const()[name = string("conv_transpose_1_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> conv_transpose_1_pad_0 = const()[name = string("conv_transpose_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_transpose_1_strides_0 = const()[name = string("conv_transpose_1_strides_0"), val = tensor<int32, [1]>([2])];
+            int32 conv_transpose_1_groups_0 = const()[name = string("conv_transpose_1_groups_0"), val = int32(512)];
+            tensor<int32, [1]> conv_transpose_1_dilations_0 = const()[name = string("conv_transpose_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 512, ?]> conv_transpose_1 = conv_transpose(bias = f0n_wrap_predictor_N_1_pool_bias, dilations = conv_transpose_1_dilations_0, groups = conv_transpose_1_groups_0, pad = conv_transpose_1_pad_0, pad_type = conv_transpose_1_pad_type_0, strides = conv_transpose_1_strides_0, weight = var_379, x = input_61)[name = string("conv_transpose_1")];
+            tensor<int32, [3]> input_63_begin_0 = const()[name = string("input_63_begin_0"), val = tensor<int32, [3]>([0, 0, 1])];
+            tensor<int32, [3]> input_63_end_0 = const()[name = string("input_63_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<bool, [3]> input_63_begin_mask_0 = const()[name = string("input_63_begin_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<bool, [3]> input_63_end_mask_0 = const()[name = string("input_63_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            tensor<fp32, [1, 512, ?]> input_63 = slice_by_index(begin = input_63_begin_0, begin_mask = input_63_begin_mask_0, end = input_63_end_0, end_mask = input_63_end_mask_0, x = conv_transpose_1)[name = string("input_63")];
+            tensor<fp32, [256, 512, 3]> weight_21 = const()[name = string("weight_21"), val = tensor<fp32, [256, 512, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29183680)))];
+            string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 256, ?]> input_65 = conv(bias = f0n_wrap_predictor_N_1_conv1_bias, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = weight_21, x = input_63)[name = string("input_65")];
+            tensor<fp32, [1, 512]> h_37 = linear(bias = f0n_wrap_predictor_N_1_norm2_fc_bias, weight = f0n_wrap_predictor_N_1_norm2_fc_weight, x = s)[name = string("linear_9")];
+            tensor<int32, [3]> var_399 = const()[name = string("op_399"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp32, [1, 512, 1]> h_39 = reshape(shape = var_399, x = h_37)[name = string("h_39")];
+            tensor<int32, [2]> var_401_split_sizes_0 = const()[name = string("op_401_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            int32 var_401_axis_0 = const()[name = string("op_401_axis_0"), val = int32(1)];
+            tensor<fp32, [1, 256, 1]> var_401_0, tensor<fp32, [1, 256, 1]> var_401_1 = split(axis = var_401_axis_0, split_sizes = var_401_split_sizes_0, x = h_39)[name = string("op_401")];
+            fp32 var_403_promoted = const()[name = string("op_403_promoted"), val = fp32(0x1p+0)];
+            tensor<fp32, [1, 256, 1]> var_404 = add(x = var_401_0, y = var_403_promoted)[name = string("op_404")];
+            tensor<fp32, [1, 256, ?]> var_405 = instance_norm(epsilon = var_8, x = input_65)[name = string("op_405")];
+            tensor<fp32, [1, 256, ?]> var_406 = mul(x = var_404, y = var_405)[name = string("op_406")];
+            tensor<fp32, [1, 256, ?]> input_67 = add(x = var_406, y = var_401_1)[name = string("input_67")];
+            tensor<fp32, [1, 256, ?]> input_69 = leaky_relu(alpha = var_9, x = input_67)[name = string("input_69")];
+            tensor<fp32, [256, 256, 3]> weight_23 = const()[name = string("weight_23"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30756608)))];
+            string out_9_pad_type_0 = const()[name = string("out_9_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> out_9_pad_0 = const()[name = string("out_9_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_9_strides_0 = const()[name = string("out_9_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_9_dilations_0 = const()[name = string("out_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 out_9_groups_0 = const()[name = string("out_9_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 256, ?]> out_9 = conv(bias = f0n_wrap_predictor_N_1_conv2_bias, dilations = out_9_dilations_0, groups = out_9_groups_0, pad = out_9_pad_0, pad_type = out_9_pad_type_0, strides = out_9_strides_0, weight = weight_23, x = input_69)[name = string("out_9")];
+            tensor<int32, [1]> expand_dims_1_axes_0 = const()[name = string("expand_dims_1_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [1, 512, ?, 1]> expand_dims_1 = expand_dims(axes = expand_dims_1_axes_0, x = _inversed_input_57)[name = string("expand_dims_1")];
+            int32 upsample_nearest_neighbor_1_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_1_scale_factor_height_0"), val = int32(2)];
+            int32 upsample_nearest_neighbor_1_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_1_scale_factor_width_0"), val = int32(1)];
+            tensor<fp32, [1, 512, ?, 1]> upsample_nearest_neighbor_1 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_1_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_1_scale_factor_width_0, x = expand_dims_1)[name = string("upsample_nearest_neighbor_1")];
+            tensor<int32, [1]> input_71_axes_0 = const()[name = string("input_71_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [1, 512, ?]> input_71 = squeeze(axes = input_71_axes_0, x = upsample_nearest_neighbor_1)[name = string("input_71")];
+            tensor<fp32, [256, 512, 1]> weight_25 = const()[name = string("weight_25"), val = tensor<fp32, [256, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31543104)))];
+            string var_423_pad_type_0 = const()[name = string("op_423_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_423_strides_0 = const()[name = string("op_423_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_423_pad_0 = const()[name = string("op_423_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_423_dilations_0 = const()[name = string("op_423_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_423_groups_0 = const()[name = string("op_423_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 256, ?]> var_423 = conv(dilations = var_423_dilations_0, groups = var_423_groups_0, pad = var_423_pad_0, pad_type = var_423_pad_type_0, strides = var_423_strides_0, weight = weight_25, x = input_71)[name = string("op_423")];
+            tensor<fp32, [1, 256, ?]> var_424 = add(x = out_9, y = var_423)[name = string("op_424")];
+            fp32 _inversed_input_73_y_0 = const()[name = string("_inversed_input_73_y_0"), val = fp32(0x1.6a09e6p-1)];
+            tensor<fp32, [1, 256, ?]> _inversed_input_73 = mul(x = var_424, y = _inversed_input_73_y_0)[name = string("_inversed_input_73")];
+            tensor<fp32, [1, 512]> h_41 = linear(bias = f0n_wrap_predictor_N_2_norm1_fc_bias, weight = f0n_wrap_predictor_N_2_norm1_fc_weight, x = s)[name = string("linear_10")];
+            tensor<int32, [3]> var_445 = const()[name = string("op_445"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp32, [1, 512, 1]> h_43 = reshape(shape = var_445, x = h_41)[name = string("h_43")];
+            tensor<int32, [2]> var_447_split_sizes_0 = const()[name = string("op_447_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            int32 var_447_axis_0 = const()[name = string("op_447_axis_0"), val = int32(1)];
+            tensor<fp32, [1, 256, 1]> var_447_0, tensor<fp32, [1, 256, 1]> var_447_1 = split(axis = var_447_axis_0, split_sizes = var_447_split_sizes_0, x = h_43)[name = string("op_447")];
+            fp32 var_449_promoted = const()[name = string("op_449_promoted"), val = fp32(0x1p+0)];
+            tensor<fp32, [1, 256, 1]> var_450 = add(x = var_447_0, y = var_449_promoted)[name = string("op_450")];
+            tensor<fp32, [1, 256, ?]> var_451 = instance_norm(epsilon = var_8, x = _inversed_input_73)[name = string("op_451")];
+            tensor<fp32, [1, 256, ?]> var_452 = mul(x = var_450, y = var_451)[name = string("op_452")];
+            tensor<fp32, [1, 256, ?]> input_75 = add(x = var_452, y = var_447_1)[name = string("input_75")];
+            tensor<fp32, [1, 256, ?]> input_77 = leaky_relu(alpha = var_9, x = input_75)[name = string("input_77")];
+            tensor<fp32, [256, 256, 3]> weight_27 = const()[name = string("weight_27"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32067456)))];
+            string input_79_pad_type_0 = const()[name = string("input_79_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> input_79_pad_0 = const()[name = string("input_79_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_79_strides_0 = const()[name = string("input_79_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_79_dilations_0 = const()[name = string("input_79_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_79_groups_0 = const()[name = string("input_79_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 256, ?]> input_79 = conv(bias = f0n_wrap_predictor_N_2_conv1_bias, dilations = input_79_dilations_0, groups = input_79_groups_0, pad = input_79_pad_0, pad_type = input_79_pad_type_0, strides = input_79_strides_0, weight = weight_27, x = input_77)[name = string("input_79")];
+            tensor<fp32, [1, 512]> h_45 = linear(bias = f0n_wrap_predictor_N_2_norm2_fc_bias, weight = f0n_wrap_predictor_N_2_norm2_fc_weight, x = s)[name = string("linear_11")];
+            tensor<int32, [3]> var_468 = const()[name = string("op_468"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp32, [1, 512, 1]> h = reshape(shape = var_468, x = h_45)[name = string("h")];
+            tensor<int32, [2]> var_470_split_sizes_0 = const()[name = string("op_470_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            int32 var_470_axis_0 = const()[name = string("op_470_axis_0"), val = int32(1)];
+            tensor<fp32, [1, 256, 1]> var_470_0, tensor<fp32, [1, 256, 1]> var_470_1 = split(axis = var_470_axis_0, split_sizes = var_470_split_sizes_0, x = h)[name = string("op_470")];
+            fp32 var_472_promoted = const()[name = string("op_472_promoted"), val = fp32(0x1p+0)];
+            tensor<fp32, [1, 256, 1]> var_473 = add(x = var_470_0, y = var_472_promoted)[name = string("op_473")];
+            tensor<fp32, [1, 256, ?]> var_474 = instance_norm(epsilon = var_8, x = input_79)[name = string("op_474")];
+            tensor<fp32, [1, 256, ?]> var_475 = mul(x = var_473, y = var_474)[name = string("op_475")];
+            tensor<fp32, [1, 256, ?]> input_81 = add(x = var_475, y = var_470_1)[name = string("input_81")];
+            tensor<fp32, [1, 256, ?]> input_83 = leaky_relu(alpha = var_9, x = input_81)[name = string("input_83")];
+            tensor<fp32, [256, 256, 3]> weight_29 = const()[name = string("weight_29"), val = tensor<fp32, [256, 256, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32853952)))];
+            string out_pad_type_0 = const()[name = string("out_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> out_pad_0 = const()[name = string("out_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_strides_0 = const()[name = string("out_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_dilations_0 = const()[name = string("out_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 out_groups_0 = const()[name = string("out_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 256, ?]> out = conv(bias = f0n_wrap_predictor_N_2_conv2_bias, dilations = out_dilations_0, groups = out_groups_0, pad = out_pad_0, pad_type = out_pad_type_0, strides = out_strides_0, weight = weight_29, x = input_83)[name = string("out")];
+            tensor<fp32, [1, 256, ?]> var_485 = add(x = out, y = _inversed_input_73)[name = string("op_485")];
+            fp32 _inversed_input_85_y_0 = const()[name = string("_inversed_input_85_y_0"), val = fp32(0x1.6a09e6p-1)];
+            tensor<fp32, [1, 256, ?]> _inversed_input_85 = mul(x = var_485, y = _inversed_input_85_y_0)[name = string("_inversed_input_85")];
+            string N_1_pad_type_0 = const()[name = string("N_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> N_1_strides_0 = const()[name = string("N_1_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> N_1_pad_0 = const()[name = string("N_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> N_1_dilations_0 = const()[name = string("N_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 N_1_groups_0 = const()[name = string("N_1_groups_0"), val = int32(1)];
+            tensor<fp32, [1, 1, ?]> N_1 = conv(bias = f0n_wrap_predictor_N_proj_bias, dilations = N_1_dilations_0, groups = N_1_groups_0, pad = N_1_pad_0, pad_type = N_1_pad_type_0, strides = N_1_strides_0, weight = f0n_wrap_predictor_N_proj_weight, x = _inversed_input_85)[name = string("N_1")];
+            tensor<int32, [1]> f0_axes_0 = const()[name = string("f0_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, ?]> f0 = squeeze(axes = f0_axes_0, x = F0_1)[name = string("f0")];
+            tensor<int32, [1]> var_496_axes_0 = const()[name = string("op_496_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, ?]> var_496 = squeeze(axes = var_496_axes_0, x = N_1)[name = string("op_496")];
+            fp32 var_501 = const()[name = string("op_501"), val = fp32(0x1.4p+3)];
+            int32 var_506 = const()[name = string("op_506"), val = int32(1)];
+            tensor<int32, [1]> f0_lo_axes_0 = const()[name = string("f0_lo_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp32, [1, ?, 1]> f0_lo = expand_dims(axes = f0_lo_axes_0, x = f0)[name = string("f0_lo")];
+            tensor<fp32, [1, ?, 9]> fn_lo = mul(x = f0_lo, y = har_wrap_harmonics)[name = string("fn_lo")];
+            fp32 _inversed_rad_lo_y_0 = const()[name = string("_inversed_rad_lo_y_0"), val = fp32(0x1.5d867cp-15)];
+            tensor<fp32, [1, ?, 9]> _inversed_rad_lo = mul(x = fn_lo, y = _inversed_rad_lo_y_0)[name = string("_inversed_rad_lo")];
+            bool var_514_exclusive_0 = const()[name = string("op_514_exclusive_0"), val = bool(false)];
+            bool var_514_reverse_0 = const()[name = string("op_514_reverse_0"), val = bool(false)];
+            tensor<fp32, [1, ?, 9]> var_514 = cumsum(axis = var_506, exclusive = var_514_exclusive_0, reverse = var_514_reverse_0, x = _inversed_rad_lo)[name = string("op_514")];
+            fp32 var_515 = const()[name = string("op_515"), val = fp32(0x1.921fb6p+2)];
+            tensor<fp32, [1, ?, 9]> phase_lo = mul(x = var_514, y = var_515)[name = string("phase_lo")];
+            fp32 var_517_promoted = const()[name = string("op_517_promoted"), val = fp32(0x1.2cp+8)];
+            tensor<fp32, [1, ?, 9]> var_518 = mul(x = phase_lo, y = var_517_promoted)[name = string("op_518")];
+            tensor<int32, [3]> input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> expand_dims_2_axes_0 = const()[name = string("expand_dims_2_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [1, 9, ?]> input_87 = transpose(perm = input_87_perm_0, x = var_518)[name = string("transpose_5")];
+            tensor<fp32, [1, 9, ?, 1]> expand_dims_2 = expand_dims(axes = expand_dims_2_axes_0, x = input_87)[name = string("expand_dims_2")];
+            int32 upsample_bilinear_0_scale_factor_height_0 = const()[name = string("upsample_bilinear_0_scale_factor_height_0"), val = int32(300)];
+            bool upsample_bilinear_0_align_corners_0 = const()[name = string("upsample_bilinear_0_align_corners_0"), val = bool(false)];
+            int32 upsample_bilinear_0_scale_factor_width_0 = const()[name = string("upsample_bilinear_0_scale_factor_width_0"), val = int32(1)];
+            tensor<fp32, [1, 9, ?, 1]> upsample_bilinear_0 = upsample_bilinear(align_corners = upsample_bilinear_0_align_corners_0, scale_factor_height = upsample_bilinear_0_scale_factor_height_0, scale_factor_width = upsample_bilinear_0_scale_factor_width_0, x = expand_dims_2)[name = string("upsample_bilinear_0")];
+            tensor<int32, [1]> var_521_axes_0 = const()[name = string("op_521_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [1, 9, ?]> var_521 = squeeze(axes = var_521_axes_0, x = upsample_bilinear_0)[name = string("op_521")];
+            tensor<fp32, [1, 9, ?]> var_523 = sin(x = var_521)[name = string("op_523")];
+            fp32 var_524 = const()[name = string("op_524"), val = fp32(0x1.99999ap-4)];
+            tensor<fp32, [1, 9, ?]> sines = mul(x = var_523, y = var_524)[name = string("sines")];
+            tensor<bool, [1, ?, 9]> var_526 = greater(x = fn_lo, y = var_501)[name = string("op_526")];
+            string uv_lo_dtype_0 = const()[name = string("uv_lo_dtype_0"), val = string("fp32")];
+            tensor<int32, [3]> input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> expand_dims_3_axes_0 = const()[name = string("expand_dims_3_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [1, ?, 9]> uv_lo = cast(dtype = uv_lo_dtype_0, x = var_526)[name = string("cast_26")];
+            tensor<fp32, [1, 9, ?]> input_89 = transpose(perm = input_89_perm_0, x = uv_lo)[name = string("transpose_4")];
+            tensor<fp32, [1, 9, ?, 1]> expand_dims_3 = expand_dims(axes = expand_dims_3_axes_0, x = input_89)[name = string("expand_dims_3")];
+            int32 upsample_nearest_neighbor_2_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_2_scale_factor_height_0"), val = int32(300)];
+            int32 upsample_nearest_neighbor_2_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_2_scale_factor_width_0"), val = int32(1)];
+            tensor<fp32, [1, 9, ?, 1]> upsample_nearest_neighbor_2 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_2_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_2_scale_factor_width_0, x = expand_dims_3)[name = string("upsample_nearest_neighbor_2")];
+            tensor<int32, [1]> var_530_axes_0 = const()[name = string("op_530_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [1, 9, ?]> var_530 = squeeze(axes = var_530_axes_0, x = upsample_nearest_neighbor_2)[name = string("op_530")];
+            tensor<fp32, [1, 9, ?]> input_91 = mul(x = sines, y = var_530)[name = string("input_91")];
+            tensor<int32, [3]> transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp32, [1, ?, 9]> transpose_0 = transpose(perm = transpose_0_perm_0, x = input_91)[name = string("transpose_3")];
+            tensor<fp32, [1, ?, 1]> input = linear(bias = har_wrap_l_linear_bias, weight = har_wrap_l_linear_weight, x = transpose_0)[name = string("linear_12")];
+            tensor<fp32, [1, ?, 1]> sine_merge = tanh(x = input)[name = string("sine_merge")];
+            tensor<int32, [3]> var_537_perm_0 = const()[name = string("op_537_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp32, [1, 1, ?]> var_537 = transpose(perm = var_537_perm_0, x = sine_merge)[name = string("transpose_2")];
+        } -> (f0, var_496, var_537);
+}

iteration_3/compiled/fused_f0n_har_source.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce01b0523d8e925108b5f9ba113d5ff1721bb3e5e25f3eb6d8a2dfaa56876c59
+size 33640448

iteration_3/compiled/ref_encoder_fp16.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dfe5cf2e8252bfc82f19da3cd60f265c31abde5d69c982233a46244585677ad1
+size 243

iteration_3/compiled/ref_encoder_fp16.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9d2f56f7354ae06e94e716169c3d8e1ff4c35b5dca1de8a548bbcbd6feb876b
+size 372

iteration_3/compiled/ref_encoder_fp16.mlmodelc/metadata.json

@@ -0,0 +1,72 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 256)",
+        "shortDescription" : "",
+        "shape" : "[1, 256]",
+        "name" : "var_794",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.linear" : 2,
+      "Ios18.conv" : 34,
+      "Ios18.leakyRelu" : 20,
+      "Ios18.expandDims" : 4,
+      "Ios18.concat" : 5,
+      "Ios18.add" : 8,
+      "Ios16.reduceMean" : 2,
+      "Ios18.avgPool" : 8,
+      "Ios18.sliceByIndex" : 4,
+      "Ios18.cast" : 1,
+      "Ios18.reshape" : 2,
+      "Ios18.mul" : 8
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-05-08",
+      "com.github.apple.coremltools.source" : "torch==2.11.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 1 × 80 × 231)",
+        "shortDescription" : "",
+        "shape" : "[1, 1, 80, 231]",
+        "name" : "mel",
+        "type" : "MultiArray"
+      }
+    ],
+    "generatedClassName" : "ref_encoder_fp16",
+    "method" : "predict"
+  }
+]

iteration_3/compiled/ref_encoder_fp16.mlmodelc/model.mil

@@ -0,0 +1,433 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 1, 80, 231]> mel) {
+            int32 var_5 = const()[name = string("op_5"), val = int32(-1)];
+            fp32 var_10 = const()[name = string("op_10"), val = fp32(0x1.99999ap-3)];
+            string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_1_strides_0 = const()[name = string("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_1_dilations_0 = const()[name = string("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_1_groups_0 = const()[name = string("input_1_groups_0"), val = int32(1)];
+            string mel_to_fp16_dtype_0 = const()[name = string("mel_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [64, 1, 3, 3]> weight_3_to_fp16 = const()[name = string("weight_3_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [64]> style_encoder_shared_0_bias_to_fp16 = const()[name = string("style_encoder_shared_0_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1280)))];
+            tensor<fp16, [1, 1, 80, 231]> mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = string("cast_121")];
+            tensor<fp16, [1, 64, 80, 231]> input_1_cast_fp16 = conv(bias = style_encoder_shared_0_bias_to_fp16, dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = weight_3_to_fp16, x = mel_to_fp16)[name = string("input_1_cast_fp16")];
+            string x_1_pad_type_0 = const()[name = string("x_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> x_1_strides_0 = const()[name = string("x_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> x_1_pad_0 = const()[name = string("x_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> x_1_dilations_0 = const()[name = string("x_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 x_1_groups_0 = const()[name = string("x_1_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 64, 1, 1]> weight_7_to_fp16 = const()[name = string("weight_7_to_fp16"), val = tensor<fp16, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1472)))];
+            tensor<fp16, [1, 128, 80, 231]> x_1_cast_fp16 = 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 = weight_7_to_fp16, x = input_1_cast_fp16)[name = string("x_1_cast_fp16")];
+            tensor<int32, [4]> var_81_begin_0 = const()[name = string("op_81_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
+            tensor<int32, [4]> var_81_end_0 = const()[name = string("op_81_end_0"), val = tensor<int32, [4]>([1, 128, 80, 231])];
+            tensor<bool, [4]> var_81_end_mask_0 = const()[name = string("op_81_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<bool, [4]> var_81_squeeze_mask_0 = const()[name = string("op_81_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
+            tensor<fp16, [1, 128, 80]> var_81_cast_fp16 = slice_by_index(begin = var_81_begin_0, end = var_81_end_0, end_mask = var_81_end_mask_0, squeeze_mask = var_81_squeeze_mask_0, x = x_1_cast_fp16)[name = string("op_81_cast_fp16")];
+            tensor<int32, [1]> var_82_axes_0 = const()[name = string("op_82_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 128, 80, 1]> var_82_cast_fp16 = expand_dims(axes = var_82_axes_0, x = var_81_cast_fp16)[name = string("op_82_cast_fp16")];
+            bool x_3_interleave_0 = const()[name = string("x_3_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 128, 80, 232]> x_3_cast_fp16 = concat(axis = var_5, interleave = x_3_interleave_0, values = (x_1_cast_fp16, var_82_cast_fp16))[name = string("x_3_cast_fp16")];
+            tensor<int32, [2]> var_85 = const()[name = string("op_85"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> var_86 = const()[name = string("op_86"), val = tensor<int32, [2]>([2, 2])];
+            string var_88_pad_type_0 = const()[name = string("op_88_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_88_pad_0 = const()[name = string("op_88_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            bool var_88_exclude_padding_from_average_0 = const()[name = string("op_88_exclude_padding_from_average_0"), val = bool(false)];
+            bool var_88_ceil_mode_0 = const()[name = string("op_88_ceil_mode_0"), val = bool(false)];
+            tensor<fp16, [1, 128, 40, 116]> var_88_cast_fp16 = avg_pool(ceil_mode = var_88_ceil_mode_0, exclude_padding_from_average = var_88_exclude_padding_from_average_0, kernel_sizes = var_85, pad = var_88_pad_0, pad_type = var_88_pad_type_0, strides = var_86, x = x_3_cast_fp16)[name = string("op_88_cast_fp16")];
+            tensor<fp16, [1, 64, 80, 231]> input_3_cast_fp16 = leaky_relu(alpha = var_10, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")];
+            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> weight_11_to_fp16 = const()[name = string("weight_11_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17920)))];
+            tensor<fp16, [64]> style_encoder_shared_1_conv1_bias_to_fp16 = const()[name = string("style_encoder_shared_1_conv1_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91712)))];
+            tensor<fp16, [1, 64, 80, 231]> input_5_cast_fp16 = conv(bias = style_encoder_shared_1_conv1_bias_to_fp16, dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = weight_11_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")];
+            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(64)];
+            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [64, 1, 3, 3]> weight_15_to_fp16 = const()[name = string("weight_15_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91904)))];
+            tensor<fp16, [64]> style_encoder_shared_1_downsample_res_conv_bias_to_fp16 = const()[name = string("style_encoder_shared_1_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93120)))];
+            tensor<fp16, [1, 64, 40, 116]> input_7_cast_fp16 = conv(bias = style_encoder_shared_1_downsample_res_conv_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = weight_15_to_fp16, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")];
+            tensor<fp16, [1, 64, 40, 116]> input_9_cast_fp16 = leaky_relu(alpha = var_10, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")];
+            string var_133_pad_type_0 = const()[name = string("op_133_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_133_pad_0 = const()[name = string("op_133_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> var_133_strides_0 = const()[name = string("op_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> var_133_dilations_0 = const()[name = string("op_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_133_groups_0 = const()[name = string("op_133_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 64, 3, 3]> weight_19_to_fp16 = const()[name = string("weight_19_to_fp16"), val = tensor<fp16, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93312)))];
+            tensor<fp16, [128]> style_encoder_shared_1_conv2_bias_to_fp16 = const()[name = string("style_encoder_shared_1_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240832)))];
+            tensor<fp16, [1, 128, 40, 116]> var_133_cast_fp16 = conv(bias = style_encoder_shared_1_conv2_bias_to_fp16, dilations = var_133_dilations_0, groups = var_133_groups_0, pad = var_133_pad_0, pad_type = var_133_pad_type_0, strides = var_133_strides_0, weight = weight_19_to_fp16, x = input_9_cast_fp16)[name = string("op_133_cast_fp16")];
+            tensor<fp16, [1, 128, 40, 116]> x_5_cast_fp16 = add(x = var_88_cast_fp16, y = var_133_cast_fp16)[name = string("x_5_cast_fp16")];
+            fp16 _inversed_input_11_y_0_to_fp16 = const()[name = string("_inversed_input_11_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
+            tensor<fp16, [1, 128, 40, 116]> _inversed_input_11_cast_fp16 = mul(x = x_5_cast_fp16, y = _inversed_input_11_y_0_to_fp16)[name = string("_inversed_input_11_cast_fp16")];
+            string x_7_pad_type_0 = const()[name = string("x_7_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> x_7_strides_0 = const()[name = string("x_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> x_7_pad_0 = const()[name = string("x_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> x_7_dilations_0 = const()[name = string("x_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 x_7_groups_0 = const()[name = string("x_7_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 128, 1, 1]> weight_23_to_fp16 = const()[name = string("weight_23_to_fp16"), val = tensor<fp16, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241152)))];
+            tensor<fp16, [1, 256, 40, 116]> x_7_cast_fp16 = conv(dilations = x_7_dilations_0, groups = x_7_groups_0, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = x_7_strides_0, weight = weight_23_to_fp16, x = _inversed_input_11_cast_fp16)[name = string("x_7_cast_fp16")];
+            tensor<int32, [2]> var_169 = const()[name = string("op_169"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> var_170 = const()[name = string("op_170"), val = tensor<int32, [2]>([2, 2])];
+            string var_172_pad_type_0 = const()[name = string("op_172_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_172_pad_0 = const()[name = string("op_172_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            bool var_172_exclude_padding_from_average_0 = const()[name = string("op_172_exclude_padding_from_average_0"), val = bool(false)];
+            bool var_172_ceil_mode_0 = const()[name = string("op_172_ceil_mode_0"), val = bool(false)];
+            tensor<fp16, [1, 256, 20, 58]> var_172_cast_fp16 = avg_pool(ceil_mode = var_172_ceil_mode_0, exclude_padding_from_average = var_172_exclude_padding_from_average_0, kernel_sizes = var_169, pad = var_172_pad_0, pad_type = var_172_pad_type_0, strides = var_170, x = x_7_cast_fp16)[name = string("op_172_cast_fp16")];
+            tensor<fp16, [1, 128, 40, 116]> input_13_cast_fp16 = leaky_relu(alpha = var_10, x = _inversed_input_11_cast_fp16)[name = string("input_13_cast_fp16")];
+            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> weight_27_to_fp16 = const()[name = string("weight_27_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306752)))];
+            tensor<fp16, [128]> style_encoder_shared_2_conv1_bias_to_fp16 = const()[name = string("style_encoder_shared_2_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601728)))];
+            tensor<fp16, [1, 128, 40, 116]> input_15_cast_fp16 = conv(bias = style_encoder_shared_2_conv1_bias_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = weight_27_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")];
+            string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(128)];
+            tensor<int32, [2]> input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [128, 1, 3, 3]> weight_31_to_fp16 = const()[name = string("weight_31_to_fp16"), val = tensor<fp16, [128, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(602048)))];
+            tensor<fp16, [128]> style_encoder_shared_2_downsample_res_conv_bias_to_fp16 = const()[name = string("style_encoder_shared_2_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(604416)))];
+            tensor<fp16, [1, 128, 20, 58]> input_17_cast_fp16 = conv(bias = style_encoder_shared_2_downsample_res_conv_bias_to_fp16, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = weight_31_to_fp16, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")];
+            tensor<fp16, [1, 128, 20, 58]> input_19_cast_fp16 = leaky_relu(alpha = var_10, x = input_17_cast_fp16)[name = string("input_19_cast_fp16")];
+            string var_217_pad_type_0 = const()[name = string("op_217_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_217_pad_0 = const()[name = string("op_217_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> var_217_strides_0 = const()[name = string("op_217_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> var_217_dilations_0 = const()[name = string("op_217_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_217_groups_0 = const()[name = string("op_217_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 128, 3, 3]> weight_35_to_fp16 = const()[name = string("weight_35_to_fp16"), val = tensor<fp16, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(604736)))];
+            tensor<fp16, [256]> style_encoder_shared_2_conv2_bias_to_fp16 = const()[name = string("style_encoder_shared_2_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1194624)))];
+            tensor<fp16, [1, 256, 20, 58]> var_217_cast_fp16 = conv(bias = style_encoder_shared_2_conv2_bias_to_fp16, dilations = var_217_dilations_0, groups = var_217_groups_0, pad = var_217_pad_0, pad_type = var_217_pad_type_0, strides = var_217_strides_0, weight = weight_35_to_fp16, x = input_19_cast_fp16)[name = string("op_217_cast_fp16")];
+            tensor<fp16, [1, 256, 20, 58]> x_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_217_cast_fp16)[name = string("x_9_cast_fp16")];
+            fp16 _inversed_input_21_y_0_to_fp16 = const()[name = string("_inversed_input_21_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
+            tensor<fp16, [1, 256, 20, 58]> _inversed_input_21_cast_fp16 = mul(x = x_9_cast_fp16, y = _inversed_input_21_y_0_to_fp16)[name = string("_inversed_input_21_cast_fp16")];
+            string x_11_pad_type_0 = const()[name = string("x_11_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> x_11_strides_0 = const()[name = string("x_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> x_11_pad_0 = const()[name = string("x_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> x_11_dilations_0 = const()[name = string("x_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 x_11_groups_0 = const()[name = string("x_11_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 256, 1, 1]> weight_39_to_fp16 = const()[name = string("weight_39_to_fp16"), val = tensor<fp16, [512, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1195200)))];
+            tensor<fp16, [1, 512, 20, 58]> x_11_cast_fp16 = conv(dilations = x_11_dilations_0, groups = x_11_groups_0, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = x_11_strides_0, weight = weight_39_to_fp16, x = _inversed_input_21_cast_fp16)[name = string("x_11_cast_fp16")];
+            tensor<int32, [2]> var_253 = const()[name = string("op_253"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> var_254 = const()[name = string("op_254"), val = tensor<int32, [2]>([2, 2])];
+            string var_256_pad_type_0 = const()[name = string("op_256_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_256_pad_0 = const()[name = string("op_256_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            bool var_256_exclude_padding_from_average_0 = const()[name = string("op_256_exclude_padding_from_average_0"), val = bool(false)];
+            bool var_256_ceil_mode_0 = const()[name = string("op_256_ceil_mode_0"), val = bool(false)];
+            tensor<fp16, [1, 512, 10, 29]> var_256_cast_fp16 = avg_pool(ceil_mode = var_256_ceil_mode_0, exclude_padding_from_average = var_256_exclude_padding_from_average_0, kernel_sizes = var_253, pad = var_256_pad_0, pad_type = var_256_pad_type_0, strides = var_254, x = x_11_cast_fp16)[name = string("op_256_cast_fp16")];
+            tensor<fp16, [1, 256, 20, 58]> input_23_cast_fp16 = leaky_relu(alpha = var_10, x = _inversed_input_21_cast_fp16)[name = string("input_23_cast_fp16")];
+            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> weight_43_to_fp16 = const()[name = string("weight_43_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1457408)))];
+            tensor<fp16, [256]> style_encoder_shared_3_conv1_bias_to_fp16 = const()[name = string("style_encoder_shared_3_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2637120)))];
+            tensor<fp16, [1, 256, 20, 58]> input_25_cast_fp16 = conv(bias = style_encoder_shared_3_conv1_bias_to_fp16, 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 = weight_43_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")];
+            string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(256)];
+            tensor<int32, [2]> input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> weight_47_to_fp16 = const()[name = string("weight_47_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2637696)))];
+            tensor<fp16, [256]> style_encoder_shared_3_downsample_res_conv_bias_to_fp16 = const()[name = string("style_encoder_shared_3_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2642368)))];
+            tensor<fp16, [1, 256, 10, 29]> input_27_cast_fp16 = conv(bias = style_encoder_shared_3_downsample_res_conv_bias_to_fp16, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = weight_47_to_fp16, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")];
+            tensor<fp16, [1, 256, 10, 29]> input_29_cast_fp16 = leaky_relu(alpha = var_10, x = input_27_cast_fp16)[name = string("input_29_cast_fp16")];
+            string var_301_pad_type_0 = const()[name = string("op_301_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_301_pad_0 = const()[name = string("op_301_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> var_301_strides_0 = const()[name = string("op_301_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> var_301_dilations_0 = const()[name = string("op_301_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_301_groups_0 = const()[name = string("op_301_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 256, 3, 3]> weight_51_to_fp16 = const()[name = string("weight_51_to_fp16"), val = tensor<fp16, [512, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2642944)))];
+            tensor<fp16, [512]> style_encoder_shared_3_conv2_bias_to_fp16 = const()[name = string("style_encoder_shared_3_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5002304)))];
+            tensor<fp16, [1, 512, 10, 29]> var_301_cast_fp16 = conv(bias = style_encoder_shared_3_conv2_bias_to_fp16, dilations = var_301_dilations_0, groups = var_301_groups_0, pad = var_301_pad_0, pad_type = var_301_pad_type_0, strides = var_301_strides_0, weight = weight_51_to_fp16, x = input_29_cast_fp16)[name = string("op_301_cast_fp16")];
+            tensor<fp16, [1, 512, 10, 29]> x_13_cast_fp16 = add(x = var_256_cast_fp16, y = var_301_cast_fp16)[name = string("x_13_cast_fp16")];
+            fp16 _inversed_x_15_y_0_to_fp16 = const()[name = string("_inversed_x_15_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
+            tensor<fp16, [1, 512, 10, 29]> _inversed_x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = _inversed_x_15_y_0_to_fp16)[name = string("_inversed_x_15_cast_fp16")];
+            tensor<int32, [4]> var_320_begin_0 = const()[name = string("op_320_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
+            tensor<int32, [4]> var_320_end_0 = const()[name = string("op_320_end_0"), val = tensor<int32, [4]>([1, 512, 10, 29])];
+            tensor<bool, [4]> var_320_end_mask_0 = const()[name = string("op_320_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<bool, [4]> var_320_squeeze_mask_0 = const()[name = string("op_320_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
+            tensor<fp16, [1, 512, 10]> var_320_cast_fp16 = slice_by_index(begin = var_320_begin_0, end = var_320_end_0, end_mask = var_320_end_mask_0, squeeze_mask = var_320_squeeze_mask_0, x = _inversed_x_15_cast_fp16)[name = string("op_320_cast_fp16")];
+            tensor<int32, [1]> var_321_axes_0 = const()[name = string("op_321_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 512, 10, 1]> var_321_cast_fp16 = expand_dims(axes = var_321_axes_0, x = var_320_cast_fp16)[name = string("op_321_cast_fp16")];
+            bool x_17_interleave_0 = const()[name = string("x_17_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 512, 10, 30]> x_17_cast_fp16 = concat(axis = var_5, interleave = x_17_interleave_0, values = (_inversed_x_15_cast_fp16, var_321_cast_fp16))[name = string("x_17_cast_fp16")];
+            tensor<int32, [2]> var_324 = const()[name = string("op_324"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> var_325 = const()[name = string("op_325"), val = tensor<int32, [2]>([2, 2])];
+            string var_327_pad_type_0 = const()[name = string("op_327_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_327_pad_0 = const()[name = string("op_327_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            bool var_327_exclude_padding_from_average_0 = const()[name = string("op_327_exclude_padding_from_average_0"), val = bool(false)];
+            bool var_327_ceil_mode_0 = const()[name = string("op_327_ceil_mode_0"), val = bool(false)];
+            tensor<fp16, [1, 512, 5, 15]> var_327_cast_fp16 = avg_pool(ceil_mode = var_327_ceil_mode_0, exclude_padding_from_average = var_327_exclude_padding_from_average_0, kernel_sizes = var_324, pad = var_327_pad_0, pad_type = var_327_pad_type_0, strides = var_325, x = x_17_cast_fp16)[name = string("op_327_cast_fp16")];
+            tensor<fp16, [1, 512, 10, 29]> input_31_cast_fp16 = leaky_relu(alpha = var_10, x = _inversed_x_15_cast_fp16)[name = string("input_31_cast_fp16")];
+            string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 512, 3, 3]> weight_55_to_fp16 = const()[name = string("weight_55_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5003392)))];
+            tensor<fp16, [512]> style_encoder_shared_4_conv1_bias_to_fp16 = const()[name = string("style_encoder_shared_4_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9722048)))];
+            tensor<fp16, [1, 512, 10, 29]> input_33_cast_fp16 = conv(bias = style_encoder_shared_4_conv1_bias_to_fp16, 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 = weight_55_to_fp16, x = input_31_cast_fp16)[name = string("input_33_cast_fp16")];
+            string input_35_pad_type_0 = const()[name = string("input_35_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_35_strides_0 = const()[name = string("input_35_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 input_35_groups_0 = const()[name = string("input_35_groups_0"), val = int32(512)];
+            tensor<int32, [2]> input_35_dilations_0 = const()[name = string("input_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [512, 1, 3, 3]> weight_59_to_fp16 = const()[name = string("weight_59_to_fp16"), val = tensor<fp16, [512, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9723136)))];
+            tensor<fp16, [512]> style_encoder_shared_4_downsample_res_conv_bias_to_fp16 = const()[name = string("style_encoder_shared_4_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9732416)))];
+            tensor<fp16, [1, 512, 5, 15]> input_35_cast_fp16 = conv(bias = style_encoder_shared_4_downsample_res_conv_bias_to_fp16, dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = weight_59_to_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")];
+            tensor<fp16, [1, 512, 5, 15]> input_37_cast_fp16 = leaky_relu(alpha = var_10, x = input_35_cast_fp16)[name = string("input_37_cast_fp16")];
+            string var_372_pad_type_0 = const()[name = string("op_372_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_372_pad_0 = const()[name = string("op_372_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> var_372_strides_0 = const()[name = string("op_372_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> var_372_dilations_0 = const()[name = string("op_372_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_372_groups_0 = const()[name = string("op_372_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 512, 3, 3]> weight_63_to_fp16 = const()[name = string("weight_63_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9733504)))];
+            tensor<fp16, [512]> style_encoder_shared_4_conv2_bias_to_fp16 = const()[name = string("style_encoder_shared_4_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14452160)))];
+            tensor<fp16, [1, 512, 5, 15]> var_372_cast_fp16 = conv(bias = style_encoder_shared_4_conv2_bias_to_fp16, dilations = var_372_dilations_0, groups = var_372_groups_0, pad = var_372_pad_0, pad_type = var_372_pad_type_0, strides = var_372_strides_0, weight = weight_63_to_fp16, x = input_37_cast_fp16)[name = string("op_372_cast_fp16")];
+            tensor<fp16, [1, 512, 5, 15]> x_19_cast_fp16 = add(x = var_327_cast_fp16, y = var_372_cast_fp16)[name = string("x_19_cast_fp16")];
+            fp16 _inversed_input_39_y_0_to_fp16 = const()[name = string("_inversed_input_39_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
+            tensor<fp16, [1, 512, 5, 15]> _inversed_input_39_cast_fp16 = mul(x = x_19_cast_fp16, y = _inversed_input_39_y_0_to_fp16)[name = string("_inversed_input_39_cast_fp16")];
+            tensor<fp16, [1, 512, 5, 15]> input_41_cast_fp16 = leaky_relu(alpha = var_10, x = _inversed_input_39_cast_fp16)[name = string("input_41_cast_fp16")];
+            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 512, 5, 5]> weight_67_to_fp16 = const()[name = string("weight_67_to_fp16"), val = tensor<fp16, [512, 512, 5, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14453248)))];
+            tensor<fp16, [512]> style_encoder_shared_6_bias_to_fp16 = const()[name = string("style_encoder_shared_6_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27560512)))];
+            tensor<fp16, [1, 512, 1, 11]> input_43_cast_fp16 = conv(bias = style_encoder_shared_6_bias_to_fp16, 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 = weight_67_to_fp16, x = input_41_cast_fp16)[name = string("input_43_cast_fp16")];
+            tensor<int32, [2]> input_45_axes_0 = const()[name = string("input_45_axes_0"), val = tensor<int32, [2]>([-2, -1])];
+            bool input_45_keep_dims_0 = const()[name = string("input_45_keep_dims_0"), val = bool(true)];
+            tensor<fp16, [1, 512, 1, 1]> input_45_cast_fp16 = reduce_mean(axes = input_45_axes_0, keep_dims = input_45_keep_dims_0, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")];
+            tensor<fp16, [1, 512, 1, 1]> h_1_cast_fp16 = leaky_relu(alpha = var_10, x = input_45_cast_fp16)[name = string("h_1_cast_fp16")];
+            tensor<int32, [2]> var_393 = const()[name = string("op_393"), val = tensor<int32, [2]>([1, -1])];
+            tensor<fp16, [1, 512]> input_47_cast_fp16 = reshape(shape = var_393, x = h_1_cast_fp16)[name = string("input_47_cast_fp16")];
+            tensor<fp16, [128, 512]> style_encoder_unshared_weight_to_fp16 = const()[name = string("style_encoder_unshared_weight_to_fp16"), val = tensor<fp16, [128, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27561600)))];
+            tensor<fp16, [128]> style_encoder_unshared_bias_to_fp16 = const()[name = string("style_encoder_unshared_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27692736)))];
+            tensor<fp16, [1, 128]> linear_0_cast_fp16 = linear(bias = style_encoder_unshared_bias_to_fp16, weight = style_encoder_unshared_weight_to_fp16, x = input_47_cast_fp16)[name = string("linear_0_cast_fp16")];
+            int32 var_399 = const()[name = string("op_399"), val = int32(-1)];
+            fp32 var_404 = const()[name = string("op_404"), val = fp32(0x1.99999ap-3)];
+            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 1, 3, 3]> weight_71_to_fp16 = const()[name = string("weight_71_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27693056)))];
+            tensor<fp16, [64]> predictor_encoder_shared_0_bias_to_fp16 = const()[name = string("predictor_encoder_shared_0_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27694272)))];
+            tensor<fp16, [1, 64, 80, 231]> input_49_cast_fp16 = conv(bias = predictor_encoder_shared_0_bias_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = weight_71_to_fp16, x = mel_to_fp16)[name = string("input_49_cast_fp16")];
+            string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 64, 1, 1]> weight_75_to_fp16 = const()[name = string("weight_75_to_fp16"), val = tensor<fp16, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27694464)))];
+            tensor<fp16, [1, 128, 80, 231]> x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = weight_75_to_fp16, x = input_49_cast_fp16)[name = string("x_21_cast_fp16")];
+            tensor<int32, [4]> var_475_begin_0 = const()[name = string("op_475_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
+            tensor<int32, [4]> var_475_end_0 = const()[name = string("op_475_end_0"), val = tensor<int32, [4]>([1, 128, 80, 231])];
+            tensor<bool, [4]> var_475_end_mask_0 = const()[name = string("op_475_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<bool, [4]> var_475_squeeze_mask_0 = const()[name = string("op_475_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
+            tensor<fp16, [1, 128, 80]> var_475_cast_fp16 = slice_by_index(begin = var_475_begin_0, end = var_475_end_0, end_mask = var_475_end_mask_0, squeeze_mask = var_475_squeeze_mask_0, x = x_21_cast_fp16)[name = string("op_475_cast_fp16")];
+            tensor<int32, [1]> var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 128, 80, 1]> var_476_cast_fp16 = expand_dims(axes = var_476_axes_0, x = var_475_cast_fp16)[name = string("op_476_cast_fp16")];
+            bool x_23_interleave_0 = const()[name = string("x_23_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 128, 80, 232]> x_23_cast_fp16 = concat(axis = var_399, interleave = x_23_interleave_0, values = (x_21_cast_fp16, var_476_cast_fp16))[name = string("x_23_cast_fp16")];
+            tensor<int32, [2]> var_479 = const()[name = string("op_479"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> var_480 = const()[name = string("op_480"), val = tensor<int32, [2]>([2, 2])];
+            string var_482_pad_type_0 = const()[name = string("op_482_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_482_pad_0 = const()[name = string("op_482_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            bool var_482_exclude_padding_from_average_0 = const()[name = string("op_482_exclude_padding_from_average_0"), val = bool(false)];
+            bool var_482_ceil_mode_0 = const()[name = string("op_482_ceil_mode_0"), val = bool(false)];
+            tensor<fp16, [1, 128, 40, 116]> var_482_cast_fp16 = avg_pool(ceil_mode = var_482_ceil_mode_0, exclude_padding_from_average = var_482_exclude_padding_from_average_0, kernel_sizes = var_479, pad = var_482_pad_0, pad_type = var_482_pad_type_0, strides = var_480, x = x_23_cast_fp16)[name = string("op_482_cast_fp16")];
+            tensor<fp16, [1, 64, 80, 231]> input_51_cast_fp16 = leaky_relu(alpha = var_404, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")];
+            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 3, 3]> weight_79_to_fp16 = const()[name = string("weight_79_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27710912)))];
+            tensor<fp16, [64]> predictor_encoder_shared_1_conv1_bias_to_fp16 = const()[name = string("predictor_encoder_shared_1_conv1_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27784704)))];
+            tensor<fp16, [1, 64, 80, 231]> input_53_cast_fp16 = conv(bias = predictor_encoder_shared_1_conv1_bias_to_fp16, 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 = weight_79_to_fp16, x = input_51_cast_fp16)[name = string("input_53_cast_fp16")];
+            string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_55_strides_0 = const()[name = string("input_55_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 input_55_groups_0 = const()[name = string("input_55_groups_0"), val = int32(64)];
+            tensor<int32, [2]> input_55_dilations_0 = const()[name = string("input_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [64, 1, 3, 3]> weight_83_to_fp16 = const()[name = string("weight_83_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27784896)))];
+            tensor<fp16, [64]> predictor_encoder_shared_1_downsample_res_conv_bias_to_fp16 = const()[name = string("predictor_encoder_shared_1_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27786112)))];
+            tensor<fp16, [1, 64, 40, 116]> input_55_cast_fp16 = conv(bias = predictor_encoder_shared_1_downsample_res_conv_bias_to_fp16, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = weight_83_to_fp16, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")];
+            tensor<fp16, [1, 64, 40, 116]> input_57_cast_fp16 = leaky_relu(alpha = var_404, x = input_55_cast_fp16)[name = string("input_57_cast_fp16")];
+            string var_527_pad_type_0 = const()[name = string("op_527_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_527_pad_0 = const()[name = string("op_527_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> var_527_strides_0 = const()[name = string("op_527_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> var_527_dilations_0 = const()[name = string("op_527_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_527_groups_0 = const()[name = string("op_527_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 64, 3, 3]> weight_87_to_fp16 = const()[name = string("weight_87_to_fp16"), val = tensor<fp16, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27786304)))];
+            tensor<fp16, [128]> predictor_encoder_shared_1_conv2_bias_to_fp16 = const()[name = string("predictor_encoder_shared_1_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27933824)))];
+            tensor<fp16, [1, 128, 40, 116]> var_527_cast_fp16 = conv(bias = predictor_encoder_shared_1_conv2_bias_to_fp16, dilations = var_527_dilations_0, groups = var_527_groups_0, pad = var_527_pad_0, pad_type = var_527_pad_type_0, strides = var_527_strides_0, weight = weight_87_to_fp16, x = input_57_cast_fp16)[name = string("op_527_cast_fp16")];
+            tensor<fp16, [1, 128, 40, 116]> x_25_cast_fp16 = add(x = var_482_cast_fp16, y = var_527_cast_fp16)[name = string("x_25_cast_fp16")];
+            fp16 _inversed_input_59_y_0_to_fp16 = const()[name = string("_inversed_input_59_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
+            tensor<fp16, [1, 128, 40, 116]> _inversed_input_59_cast_fp16 = mul(x = x_25_cast_fp16, y = _inversed_input_59_y_0_to_fp16)[name = string("_inversed_input_59_cast_fp16")];
+            string x_27_pad_type_0 = const()[name = string("x_27_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> x_27_strides_0 = const()[name = string("x_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> x_27_pad_0 = const()[name = string("x_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> x_27_dilations_0 = const()[name = string("x_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 x_27_groups_0 = const()[name = string("x_27_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 128, 1, 1]> weight_91_to_fp16 = const()[name = string("weight_91_to_fp16"), val = tensor<fp16, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27934144)))];
+            tensor<fp16, [1, 256, 40, 116]> x_27_cast_fp16 = conv(dilations = x_27_dilations_0, groups = x_27_groups_0, pad = x_27_pad_0, pad_type = x_27_pad_type_0, strides = x_27_strides_0, weight = weight_91_to_fp16, x = _inversed_input_59_cast_fp16)[name = string("x_27_cast_fp16")];
+            tensor<int32, [2]> var_563 = const()[name = string("op_563"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> var_564 = const()[name = string("op_564"), val = tensor<int32, [2]>([2, 2])];
+            string var_566_pad_type_0 = const()[name = string("op_566_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_566_pad_0 = const()[name = string("op_566_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            bool var_566_exclude_padding_from_average_0 = const()[name = string("op_566_exclude_padding_from_average_0"), val = bool(false)];
+            bool var_566_ceil_mode_0 = const()[name = string("op_566_ceil_mode_0"), val = bool(false)];
+            tensor<fp16, [1, 256, 20, 58]> var_566_cast_fp16 = avg_pool(ceil_mode = var_566_ceil_mode_0, exclude_padding_from_average = var_566_exclude_padding_from_average_0, kernel_sizes = var_563, pad = var_566_pad_0, pad_type = var_566_pad_type_0, strides = var_564, x = x_27_cast_fp16)[name = string("op_566_cast_fp16")];
+            tensor<fp16, [1, 128, 40, 116]> input_61_cast_fp16 = leaky_relu(alpha = var_404, x = _inversed_input_59_cast_fp16)[name = string("input_61_cast_fp16")];
+            string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)];
+            tensor<fp16, [128, 128, 3, 3]> weight_95_to_fp16 = const()[name = string("weight_95_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27999744)))];
+            tensor<fp16, [128]> predictor_encoder_shared_2_conv1_bias_to_fp16 = const()[name = string("predictor_encoder_shared_2_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28294720)))];
+            tensor<fp16, [1, 128, 40, 116]> input_63_cast_fp16 = conv(bias = predictor_encoder_shared_2_conv1_bias_to_fp16, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = weight_95_to_fp16, x = input_61_cast_fp16)[name = string("input_63_cast_fp16")];
+            string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(128)];
+            tensor<int32, [2]> input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [128, 1, 3, 3]> weight_99_to_fp16 = const()[name = string("weight_99_to_fp16"), val = tensor<fp16, [128, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28295040)))];
+            tensor<fp16, [128]> predictor_encoder_shared_2_downsample_res_conv_bias_to_fp16 = const()[name = string("predictor_encoder_shared_2_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28297408)))];
+            tensor<fp16, [1, 128, 20, 58]> input_65_cast_fp16 = conv(bias = predictor_encoder_shared_2_downsample_res_conv_bias_to_fp16, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = weight_99_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")];
+            tensor<fp16, [1, 128, 20, 58]> input_67_cast_fp16 = leaky_relu(alpha = var_404, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")];
+            string var_611_pad_type_0 = const()[name = string("op_611_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_611_pad_0 = const()[name = string("op_611_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> var_611_strides_0 = const()[name = string("op_611_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> var_611_dilations_0 = const()[name = string("op_611_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_611_groups_0 = const()[name = string("op_611_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 128, 3, 3]> weight_103_to_fp16 = const()[name = string("weight_103_to_fp16"), val = tensor<fp16, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28297728)))];
+            tensor<fp16, [256]> predictor_encoder_shared_2_conv2_bias_to_fp16 = const()[name = string("predictor_encoder_shared_2_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28887616)))];
+            tensor<fp16, [1, 256, 20, 58]> var_611_cast_fp16 = conv(bias = predictor_encoder_shared_2_conv2_bias_to_fp16, dilations = var_611_dilations_0, groups = var_611_groups_0, pad = var_611_pad_0, pad_type = var_611_pad_type_0, strides = var_611_strides_0, weight = weight_103_to_fp16, x = input_67_cast_fp16)[name = string("op_611_cast_fp16")];
+            tensor<fp16, [1, 256, 20, 58]> x_29_cast_fp16 = add(x = var_566_cast_fp16, y = var_611_cast_fp16)[name = string("x_29_cast_fp16")];
+            fp16 _inversed_input_69_y_0_to_fp16 = const()[name = string("_inversed_input_69_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
+            tensor<fp16, [1, 256, 20, 58]> _inversed_input_69_cast_fp16 = mul(x = x_29_cast_fp16, y = _inversed_input_69_y_0_to_fp16)[name = string("_inversed_input_69_cast_fp16")];
+            string x_31_pad_type_0 = const()[name = string("x_31_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> x_31_strides_0 = const()[name = string("x_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> x_31_pad_0 = const()[name = string("x_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> x_31_dilations_0 = const()[name = string("x_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 x_31_groups_0 = const()[name = string("x_31_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 256, 1, 1]> weight_107_to_fp16 = const()[name = string("weight_107_to_fp16"), val = tensor<fp16, [512, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28888192)))];
+            tensor<fp16, [1, 512, 20, 58]> x_31_cast_fp16 = conv(dilations = x_31_dilations_0, groups = x_31_groups_0, pad = x_31_pad_0, pad_type = x_31_pad_type_0, strides = x_31_strides_0, weight = weight_107_to_fp16, x = _inversed_input_69_cast_fp16)[name = string("x_31_cast_fp16")];
+            tensor<int32, [2]> var_647 = const()[name = string("op_647"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> var_648 = const()[name = string("op_648"), val = tensor<int32, [2]>([2, 2])];
+            string var_650_pad_type_0 = const()[name = string("op_650_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_650_pad_0 = const()[name = string("op_650_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            bool var_650_exclude_padding_from_average_0 = const()[name = string("op_650_exclude_padding_from_average_0"), val = bool(false)];
+            bool var_650_ceil_mode_0 = const()[name = string("op_650_ceil_mode_0"), val = bool(false)];
+            tensor<fp16, [1, 512, 10, 29]> var_650_cast_fp16 = avg_pool(ceil_mode = var_650_ceil_mode_0, exclude_padding_from_average = var_650_exclude_padding_from_average_0, kernel_sizes = var_647, pad = var_650_pad_0, pad_type = var_650_pad_type_0, strides = var_648, x = x_31_cast_fp16)[name = string("op_650_cast_fp16")];
+            tensor<fp16, [1, 256, 20, 58]> input_71_cast_fp16 = leaky_relu(alpha = var_404, x = _inversed_input_69_cast_fp16)[name = string("input_71_cast_fp16")];
+            string input_73_pad_type_0 = const()[name = string("input_73_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_73_pad_0 = const()[name = string("input_73_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_73_strides_0 = const()[name = string("input_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_73_dilations_0 = const()[name = string("input_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_73_groups_0 = const()[name = string("input_73_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 256, 3, 3]> weight_111_to_fp16 = const()[name = string("weight_111_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29150400)))];
+            tensor<fp16, [256]> predictor_encoder_shared_3_conv1_bias_to_fp16 = const()[name = string("predictor_encoder_shared_3_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30330112)))];
+            tensor<fp16, [1, 256, 20, 58]> input_73_cast_fp16 = conv(bias = predictor_encoder_shared_3_conv1_bias_to_fp16, dilations = input_73_dilations_0, groups = input_73_groups_0, pad = input_73_pad_0, pad_type = input_73_pad_type_0, strides = input_73_strides_0, weight = weight_111_to_fp16, x = input_71_cast_fp16)[name = string("input_73_cast_fp16")];
+            string input_75_pad_type_0 = const()[name = string("input_75_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_75_pad_0 = const()[name = string("input_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_75_strides_0 = const()[name = string("input_75_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 input_75_groups_0 = const()[name = string("input_75_groups_0"), val = int32(256)];
+            tensor<int32, [2]> input_75_dilations_0 = const()[name = string("input_75_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [256, 1, 3, 3]> weight_115_to_fp16 = const()[name = string("weight_115_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30330688)))];
+            tensor<fp16, [256]> predictor_encoder_shared_3_downsample_res_conv_bias_to_fp16 = const()[name = string("predictor_encoder_shared_3_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30335360)))];
+            tensor<fp16, [1, 256, 10, 29]> input_75_cast_fp16 = conv(bias = predictor_encoder_shared_3_downsample_res_conv_bias_to_fp16, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = weight_115_to_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")];
+            tensor<fp16, [1, 256, 10, 29]> input_77_cast_fp16 = leaky_relu(alpha = var_404, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")];
+            string var_695_pad_type_0 = const()[name = string("op_695_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_695_pad_0 = const()[name = string("op_695_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> var_695_strides_0 = const()[name = string("op_695_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> var_695_dilations_0 = const()[name = string("op_695_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_695_groups_0 = const()[name = string("op_695_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 256, 3, 3]> weight_119_to_fp16 = const()[name = string("weight_119_to_fp16"), val = tensor<fp16, [512, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30335936)))];
+            tensor<fp16, [512]> predictor_encoder_shared_3_conv2_bias_to_fp16 = const()[name = string("predictor_encoder_shared_3_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32695296)))];
+            tensor<fp16, [1, 512, 10, 29]> var_695_cast_fp16 = conv(bias = predictor_encoder_shared_3_conv2_bias_to_fp16, dilations = var_695_dilations_0, groups = var_695_groups_0, pad = var_695_pad_0, pad_type = var_695_pad_type_0, strides = var_695_strides_0, weight = weight_119_to_fp16, x = input_77_cast_fp16)[name = string("op_695_cast_fp16")];
+            tensor<fp16, [1, 512, 10, 29]> x_33_cast_fp16 = add(x = var_650_cast_fp16, y = var_695_cast_fp16)[name = string("x_33_cast_fp16")];
+            fp16 _inversed_x_35_y_0_to_fp16 = const()[name = string("_inversed_x_35_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
+            tensor<fp16, [1, 512, 10, 29]> _inversed_x_35_cast_fp16 = mul(x = x_33_cast_fp16, y = _inversed_x_35_y_0_to_fp16)[name = string("_inversed_x_35_cast_fp16")];
+            tensor<int32, [4]> var_714_begin_0 = const()[name = string("op_714_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
+            tensor<int32, [4]> var_714_end_0 = const()[name = string("op_714_end_0"), val = tensor<int32, [4]>([1, 512, 10, 29])];
+            tensor<bool, [4]> var_714_end_mask_0 = const()[name = string("op_714_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<bool, [4]> var_714_squeeze_mask_0 = const()[name = string("op_714_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
+            tensor<fp16, [1, 512, 10]> var_714_cast_fp16 = slice_by_index(begin = var_714_begin_0, end = var_714_end_0, end_mask = var_714_end_mask_0, squeeze_mask = var_714_squeeze_mask_0, x = _inversed_x_35_cast_fp16)[name = string("op_714_cast_fp16")];
+            tensor<int32, [1]> var_715_axes_0 = const()[name = string("op_715_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 512, 10, 1]> var_715_cast_fp16 = expand_dims(axes = var_715_axes_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")];
+            bool x_37_interleave_0 = const()[name = string("x_37_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 512, 10, 30]> x_37_cast_fp16 = concat(axis = var_399, interleave = x_37_interleave_0, values = (_inversed_x_35_cast_fp16, var_715_cast_fp16))[name = string("x_37_cast_fp16")];
+            tensor<int32, [2]> var_718 = const()[name = string("op_718"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> var_719 = const()[name = string("op_719"), val = tensor<int32, [2]>([2, 2])];
+            string var_721_pad_type_0 = const()[name = string("op_721_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_721_pad_0 = const()[name = string("op_721_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            bool var_721_exclude_padding_from_average_0 = const()[name = string("op_721_exclude_padding_from_average_0"), val = bool(false)];
+            bool var_721_ceil_mode_0 = const()[name = string("op_721_ceil_mode_0"), val = bool(false)];
+            tensor<fp16, [1, 512, 5, 15]> var_721_cast_fp16 = avg_pool(ceil_mode = var_721_ceil_mode_0, exclude_padding_from_average = var_721_exclude_padding_from_average_0, kernel_sizes = var_718, pad = var_721_pad_0, pad_type = var_721_pad_type_0, strides = var_719, x = x_37_cast_fp16)[name = string("op_721_cast_fp16")];
+            tensor<fp16, [1, 512, 10, 29]> input_79_cast_fp16 = leaky_relu(alpha = var_404, x = _inversed_x_35_cast_fp16)[name = string("input_79_cast_fp16")];
+            string input_81_pad_type_0 = const()[name = string("input_81_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_81_pad_0 = const()[name = string("input_81_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_81_strides_0 = const()[name = string("input_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_81_dilations_0 = const()[name = string("input_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_81_groups_0 = const()[name = string("input_81_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 512, 3, 3]> weight_123_to_fp16 = const()[name = string("weight_123_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32696384)))];
+            tensor<fp16, [512]> predictor_encoder_shared_4_conv1_bias_to_fp16 = const()[name = string("predictor_encoder_shared_4_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37415040)))];
+            tensor<fp16, [1, 512, 10, 29]> input_81_cast_fp16 = conv(bias = predictor_encoder_shared_4_conv1_bias_to_fp16, dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = weight_123_to_fp16, x = input_79_cast_fp16)[name = string("input_81_cast_fp16")];
+            string input_83_pad_type_0 = const()[name = string("input_83_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_83_pad_0 = const()[name = string("input_83_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_83_strides_0 = const()[name = string("input_83_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            int32 input_83_groups_0 = const()[name = string("input_83_groups_0"), val = int32(512)];
+            tensor<int32, [2]> input_83_dilations_0 = const()[name = string("input_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<fp16, [512, 1, 3, 3]> weight_127_to_fp16 = const()[name = string("weight_127_to_fp16"), val = tensor<fp16, [512, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37416128)))];
+            tensor<fp16, [512]> predictor_encoder_shared_4_downsample_res_conv_bias_to_fp16 = const()[name = string("predictor_encoder_shared_4_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37425408)))];
+            tensor<fp16, [1, 512, 5, 15]> input_83_cast_fp16 = conv(bias = predictor_encoder_shared_4_downsample_res_conv_bias_to_fp16, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = weight_127_to_fp16, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")];
+            tensor<fp16, [1, 512, 5, 15]> input_85_cast_fp16 = leaky_relu(alpha = var_404, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")];
+            string var_766_pad_type_0 = const()[name = string("op_766_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> var_766_pad_0 = const()[name = string("op_766_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> var_766_strides_0 = const()[name = string("op_766_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> var_766_dilations_0 = const()[name = string("op_766_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_766_groups_0 = const()[name = string("op_766_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 512, 3, 3]> weight_131_to_fp16 = const()[name = string("weight_131_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37426496)))];
+            tensor<fp16, [512]> predictor_encoder_shared_4_conv2_bias_to_fp16 = const()[name = string("predictor_encoder_shared_4_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42145152)))];
+            tensor<fp16, [1, 512, 5, 15]> var_766_cast_fp16 = conv(bias = predictor_encoder_shared_4_conv2_bias_to_fp16, dilations = var_766_dilations_0, groups = var_766_groups_0, pad = var_766_pad_0, pad_type = var_766_pad_type_0, strides = var_766_strides_0, weight = weight_131_to_fp16, x = input_85_cast_fp16)[name = string("op_766_cast_fp16")];
+            tensor<fp16, [1, 512, 5, 15]> x_cast_fp16 = add(x = var_721_cast_fp16, y = var_766_cast_fp16)[name = string("x_cast_fp16")];
+            fp16 _inversed_input_87_y_0_to_fp16 = const()[name = string("_inversed_input_87_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
+            tensor<fp16, [1, 512, 5, 15]> _inversed_input_87_cast_fp16 = mul(x = x_cast_fp16, y = _inversed_input_87_y_0_to_fp16)[name = string("_inversed_input_87_cast_fp16")];
+            tensor<fp16, [1, 512, 5, 15]> input_89_cast_fp16 = leaky_relu(alpha = var_404, x = _inversed_input_87_cast_fp16)[name = string("input_89_cast_fp16")];
+            string input_91_pad_type_0 = const()[name = string("input_91_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_91_strides_0 = const()[name = string("input_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> input_91_pad_0 = const()[name = string("input_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_91_dilations_0 = const()[name = string("input_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_91_groups_0 = const()[name = string("input_91_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 512, 5, 5]> weight_135_to_fp16 = const()[name = string("weight_135_to_fp16"), val = tensor<fp16, [512, 512, 5, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42146240)))];
+            tensor<fp16, [512]> predictor_encoder_shared_6_bias_to_fp16 = const()[name = string("predictor_encoder_shared_6_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55253504)))];
+            tensor<fp16, [1, 512, 1, 11]> input_91_cast_fp16 = conv(bias = predictor_encoder_shared_6_bias_to_fp16, dilations = input_91_dilations_0, groups = input_91_groups_0, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = input_91_strides_0, weight = weight_135_to_fp16, x = input_89_cast_fp16)[name = string("input_91_cast_fp16")];
+            tensor<int32, [2]> input_93_axes_0 = const()[name = string("input_93_axes_0"), val = tensor<int32, [2]>([-2, -1])];
+            bool input_93_keep_dims_0 = const()[name = string("input_93_keep_dims_0"), val = bool(true)];
+            tensor<fp16, [1, 512, 1, 1]> input_93_cast_fp16 = reduce_mean(axes = input_93_axes_0, keep_dims = input_93_keep_dims_0, x = input_91_cast_fp16)[name = string("input_93_cast_fp16")];
+            tensor<fp16, [1, 512, 1, 1]> h_cast_fp16 = leaky_relu(alpha = var_404, x = input_93_cast_fp16)[name = string("h_cast_fp16")];
+            tensor<int32, [2]> var_787 = const()[name = string("op_787"), val = tensor<int32, [2]>([1, -1])];
+            tensor<fp16, [1, 512]> input_cast_fp16 = reshape(shape = var_787, x = h_cast_fp16)[name = string("input_cast_fp16")];
+            tensor<fp16, [128, 512]> predictor_encoder_unshared_weight_to_fp16 = const()[name = string("predictor_encoder_unshared_weight_to_fp16"), val = tensor<fp16, [128, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55254592)))];
+            tensor<fp16, [128]> predictor_encoder_unshared_bias_to_fp16 = const()[name = string("predictor_encoder_unshared_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55385728)))];
+            tensor<fp16, [1, 128]> linear_1_cast_fp16 = linear(bias = predictor_encoder_unshared_bias_to_fp16, weight = predictor_encoder_unshared_weight_to_fp16, x = input_cast_fp16)[name = string("linear_1_cast_fp16")];
+            int32 var_793 = const()[name = string("op_793"), val = int32(1)];
+            bool var_794_interleave_0 = const()[name = string("op_794_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 256]> var_794 = concat(axis = var_793, interleave = var_794_interleave_0, values = (linear_0_cast_fp16, linear_1_cast_fp16))[name = string("op_794_cast_fp16")];
+        } -> (var_794);
+}

iteration_3/compiled/ref_encoder_fp16.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:011d14fdb46589dfb79efb619d63846430be4e4ac86372f8819f35f5e0157391
+size 55386048

iteration_3/compiled/text_encoder_fp16.mlmodelc/analytics/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:26345aacd8ebd5eebc32bed1e54ae89b92c120a01272ce75991b6ebfeba3d302
+size 243

iteration_3/compiled/text_encoder_fp16.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9aabdea8db6713d2e02aa4d97a57e9fbfad5c44de155f57dd2c0870c523e41fa
+size 452

iteration_3/compiled/text_encoder_fp16.mlmodelc/metadata.json

@@ -0,0 +1,98 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "var_159",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 9,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios18.conv" : 3,
+      "Ios18.sub" : 1,
+      "Select" : 1,
+      "Ios18.leakyRelu" : 3,
+      "Ios18.expandDims" : 1,
+      "Ios18.gather" : 1,
+      "Ios18.lstm" : 1,
+      "Ios18.add" : 1,
+      "Ios18.layerNorm" : 3,
+      "Ios18.transpose" : 9,
+      "Ios18.cast" : 4,
+      "Ios18.greaterEqual" : 1,
+      "Identity" : 1,
+      "Ios18.mul" : 5
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int16, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "15.0",
+      "tvOS" : "18.0",
+      "visionOS" : "2.0",
+      "watchOS" : "11.0",
+      "iOS" : "18.0",
+      "macCatalyst" : "18.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-05-08",
+      "com.github.apple.coremltools.source" : "torch==2.11.0",
+      "com.github.apple.coremltools.version" : "9.0",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "inputSchema" : [
+      {
+        "dataType" : "Int32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 1...512",
+        "shapeRange" : "[[1, 1], [1, 512]]",
+        "formattedType" : "MultiArray (Int32 1 × 57)",
+        "type" : "MultiArray",
+        "shape" : "[1, 57]",
+        "name" : "tokens",
+        "shortDescription" : ""
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "input_lengths",
+        "type" : "MultiArray"
+      },
+      {
+        "dataType" : "Float32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 1...512",
+        "shapeRange" : "[[1, 1], [1, 512]]",
+        "formattedType" : "MultiArray (Float32 1 × 57)",
+        "type" : "MultiArray",
+        "shape" : "[1, 57]",
+        "name" : "text_mask",
+        "shortDescription" : ""
+      }
+    ],
+    "generatedClassName" : "text_encoder_fp16",
+    "method" : "predict"
+  }
+]

iteration_3/compiled/text_encoder_fp16.mlmodelc/model.mil

@@ -0,0 +1,110 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios18>(tensor<int32, [1]> input_lengths, tensor<fp32, [1, ?]> text_mask, tensor<int32, [1, ?]> tokens) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"text_mask", [1, 57]}, {"tokens", [1, 57]}}), ("RangeDims", {{"text_mask", [[1, 1], [1, 512]]}, {"tokens", [[1, 1], [1, 512]]}})))] {
+            int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)];
+            bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)];
+            tensor<fp16, [178, 512]> text_encoder_embedding_weight_to_fp16 = const()[name = string("text_encoder_embedding_weight_to_fp16"), val = tensor<fp16, [178, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            string tokens_to_int16_dtype_0 = const()[name = string("tokens_to_int16_dtype_0"), val = string("int16")];
+            string cast_2_dtype_0 = const()[name = string("cast_2_dtype_0"), val = string("int32")];
+            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
+            tensor<int16, [1, ?]> tokens_to_int16 = cast(dtype = tokens_to_int16_dtype_0, x = tokens)[name = string("cast_6")];
+            tensor<int32, [1, ?]> cast_2 = cast(dtype = cast_2_dtype_0, x = tokens_to_int16)[name = string("cast_5")];
+            tensor<bool, [1, ?]> greater_equal_0 = greater_equal(x = cast_2, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
+            int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(178)];
+            tensor<int32, [1, ?]> add_2 = add(x = cast_2, y = slice_by_index_0)[name = string("add_2")];
+            tensor<int32, [1, ?]> select_0 = select(a = cast_2, b = add_2, cond = greater_equal_0)[name = string("select_0")];
+            int32 x_1_cast_fp16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_axis_0"), val = int32(0)];
+            string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")];
+            tensor<int16, [1, ?]> select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_4")];
+            tensor<fp16, [1, ?, 512]> x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_to_int16, validate_indices = x_1_validate_indices_0, x = text_encoder_embedding_weight_to_fp16)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")];
+            tensor<int32, [3]> x_3_perm_0 = const()[name = string("x_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_30_axes_0 = const()[name = string("op_30_axes_0"), val = tensor<int32, [1]>([1])];
+            string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, ?]> text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_3")];
+            tensor<fp16, [1, 1, ?]> var_30_cast_fp16 = expand_dims(axes = var_30_axes_0, x = text_mask_to_fp16)[name = string("op_30_cast_fp16")];
+            fp16 var_31_to_fp16 = const()[name = string("op_31_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1, ?]> keep_cast_fp16 = sub(x = var_31_to_fp16, y = var_30_cast_fp16)[name = string("keep_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> x_3_cast_fp16 = transpose(perm = x_3_perm_0, x = x_1_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_10")];
+            tensor<fp16, [1, 512, ?]> input_1_cast_fp16 = mul(x = x_3_cast_fp16, y = keep_cast_fp16)[name = string("input_1_cast_fp16")];
+            fp32 var_35 = const()[name = string("op_35"), val = fp32(0x1.99999ap-3)];
+            string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [1]> x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 512, 5]> weight_3_to_fp16 = const()[name = string("weight_3_to_fp16"), val = tensor<fp16, [512, 512, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182400)))];
+            tensor<fp16, [512]> text_encoder_cnn_0_0_bias_to_fp16 = const()[name = string("text_encoder_cnn_0_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2803904)))];
+            tensor<fp16, [1, 512, ?]> x_5_cast_fp16 = conv(bias = text_encoder_cnn_0_0_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = weight_3_to_fp16, x = input_1_cast_fp16)[name = string("x_5_cast_fp16")];
+            tensor<int32, [3]> input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [1]> x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> text_encoder_cnn_0_1_gamma_to_fp16 = const()[name = string("text_encoder_cnn_0_1_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2804992)))];
+            tensor<fp16, [512]> text_encoder_cnn_0_1_beta_to_fp16 = const()[name = string("text_encoder_cnn_0_1_beta_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2806080)))];
+            fp16 var_38_to_fp16 = const()[name = string("op_38_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = x_5_cast_fp16)[name = string("transpose_9")];
+            tensor<fp16, [1, ?, 512]> x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, beta = text_encoder_cnn_0_1_beta_to_fp16, epsilon = var_38_to_fp16, gamma = text_encoder_cnn_0_1_gamma_to_fp16, x = input_3_cast_fp16)[name = string("x_7_cast_fp16")];
+            tensor<int32, [3]> input_5_perm_0 = const()[name = string("input_5_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<fp16, [1, 512, ?]> input_5_cast_fp16 = transpose(perm = input_5_perm_0, x = x_7_cast_fp16)[name = string("transpose_8")];
+            tensor<fp16, [1, 512, ?]> x_9_cast_fp16 = leaky_relu(alpha = var_35, x = input_5_cast_fp16)[name = string("x_9_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_7_cast_fp16 = mul(x = x_9_cast_fp16, y = keep_cast_fp16)[name = string("input_7_cast_fp16")];
+            fp32 var_65 = const()[name = string("op_65"), val = fp32(0x1.99999ap-3)];
+            string x_11_pad_type_0 = const()[name = string("x_11_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> x_11_pad_0 = const()[name = string("x_11_pad_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [1]> x_11_strides_0 = const()[name = string("x_11_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> x_11_dilations_0 = const()[name = string("x_11_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_11_groups_0 = const()[name = string("x_11_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 512, 5]> weight_7_to_fp16 = const()[name = string("weight_7_to_fp16"), val = tensor<fp16, [512, 512, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2807168)))];
+            tensor<fp16, [512]> text_encoder_cnn_1_0_bias_to_fp16 = const()[name = string("text_encoder_cnn_1_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5428672)))];
+            tensor<fp16, [1, 512, ?]> x_11_cast_fp16 = conv(bias = text_encoder_cnn_1_0_bias_to_fp16, dilations = x_11_dilations_0, groups = x_11_groups_0, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = x_11_strides_0, weight = weight_7_to_fp16, x = input_7_cast_fp16)[name = string("x_11_cast_fp16")];
+            tensor<int32, [3]> input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [1]> x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> text_encoder_cnn_1_1_gamma_to_fp16 = const()[name = string("text_encoder_cnn_1_1_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5429760)))];
+            tensor<fp16, [512]> text_encoder_cnn_1_1_beta_to_fp16 = const()[name = string("text_encoder_cnn_1_1_beta_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430848)))];
+            fp16 var_68_to_fp16 = const()[name = string("op_68_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_11_cast_fp16)[name = string("transpose_7")];
+            tensor<fp16, [1, ?, 512]> x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, beta = text_encoder_cnn_1_1_beta_to_fp16, epsilon = var_68_to_fp16, gamma = text_encoder_cnn_1_1_gamma_to_fp16, x = input_9_cast_fp16)[name = string("x_13_cast_fp16")];
+            tensor<int32, [3]> input_11_perm_0 = const()[name = string("input_11_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<fp16, [1, 512, ?]> input_11_cast_fp16 = transpose(perm = input_11_perm_0, x = x_13_cast_fp16)[name = string("transpose_6")];
+            tensor<fp16, [1, 512, ?]> x_15_cast_fp16 = leaky_relu(alpha = var_65, x = input_11_cast_fp16)[name = string("x_15_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_13_cast_fp16 = mul(x = x_15_cast_fp16, y = keep_cast_fp16)[name = string("input_13_cast_fp16")];
+            fp32 var_95 = const()[name = string("op_95"), val = fp32(0x1.99999ap-3)];
+            string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("custom")];
+            tensor<int32, [2]> x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [1]> x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 512, 5]> weight_11_to_fp16 = const()[name = string("weight_11_to_fp16"), val = tensor<fp16, [512, 512, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431936)))];
+            tensor<fp16, [512]> text_encoder_cnn_2_0_bias_to_fp16 = const()[name = string("text_encoder_cnn_2_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8053440)))];
+            tensor<fp16, [1, 512, ?]> x_17_cast_fp16 = conv(bias = text_encoder_cnn_2_0_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = weight_11_to_fp16, x = input_13_cast_fp16)[name = string("x_17_cast_fp16")];
+            tensor<int32, [3]> input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [1]> x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> text_encoder_cnn_2_1_gamma_to_fp16 = const()[name = string("text_encoder_cnn_2_1_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054528)))];
+            tensor<fp16, [512]> text_encoder_cnn_2_1_beta_to_fp16 = const()[name = string("text_encoder_cnn_2_1_beta_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8055616)))];
+            fp16 var_98_to_fp16 = const()[name = string("op_98_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = x_17_cast_fp16)[name = string("transpose_5")];
+            tensor<fp16, [1, ?, 512]> x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, beta = text_encoder_cnn_2_1_beta_to_fp16, epsilon = var_98_to_fp16, gamma = text_encoder_cnn_2_1_gamma_to_fp16, x = input_15_cast_fp16)[name = string("x_19_cast_fp16")];
+            tensor<int32, [3]> input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<fp16, [1, 512, ?]> input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = x_19_cast_fp16)[name = string("transpose_4")];
+            tensor<fp16, [1, 512, ?]> x_21_cast_fp16 = leaky_relu(alpha = var_95, x = input_17_cast_fp16)[name = string("x_21_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> x_23_cast_fp16 = mul(x = x_21_cast_fp16, y = keep_cast_fp16)[name = string("x_23_cast_fp16")];
+            tensor<int32, [3]> transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor<int32, [3]>([2, 0, 1])];
+            string x_25_batch_first_direction_0 = const()[name = string("x_25_batch_first_direction_0"), val = string("bidirectional")];
+            bool x_25_batch_first_output_sequence_0 = const()[name = string("x_25_batch_first_output_sequence_0"), val = bool(true)];
+            string x_25_batch_first_recurrent_activation_0 = const()[name = string("x_25_batch_first_recurrent_activation_0"), val = string("sigmoid")];
+            string x_25_batch_first_cell_activation_0 = const()[name = string("x_25_batch_first_cell_activation_0"), val = string("tanh")];
+            string x_25_batch_first_activation_0 = const()[name = string("x_25_batch_first_activation_0"), val = string("tanh")];
+            tensor<fp16, [1, 512]> x_25_batch_first_lstm_h0_reshaped_to_fp16 = const()[name = string("x_25_batch_first_lstm_h0_reshaped_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8056704)))];
+            tensor<fp16, [1024, 512]> concat_4_to_fp16 = const()[name = string("concat_4_to_fp16"), val = tensor<fp16, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8057792)))];
+            tensor<fp16, [1024, 256]> concat_5_to_fp16 = const()[name = string("concat_5_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9106432)))];
+            tensor<fp16, [1024]> add_0_to_fp16 = const()[name = string("add_0_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9630784)))];
+            tensor<fp16, [1024, 512]> concat_6_to_fp16 = const()[name = string("concat_6_to_fp16"), val = tensor<fp16, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9632896)))];
+            tensor<fp16, [1024, 256]> concat_7_to_fp16 = const()[name = string("concat_7_to_fp16"), val = tensor<fp16, [1024, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10681536)))];
+            tensor<fp16, [1024]> add_1_to_fp16 = const()[name = string("add_1_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11205888)))];
+            tensor<fp16, [?, 1, 512]> transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = x_23_cast_fp16)[name = string("transpose_3")];
+            tensor<fp16, [?, 1, 512]> x_25_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> x_25_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> x_25_batch_first_cast_fp16_2 = lstm(activation = x_25_batch_first_activation_0, bias = add_0_to_fp16, bias_back = add_1_to_fp16, cell_activation = x_25_batch_first_cell_activation_0, direction = x_25_batch_first_direction_0, initial_c = x_25_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_25_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = x_25_batch_first_output_sequence_0, recurrent_activation = x_25_batch_first_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_hh_back = concat_7_to_fp16, weight_ih = concat_4_to_fp16, weight_ih_back = concat_6_to_fp16, x = transpose_0_cast_fp16)[name = string("x_25_batch_first_cast_fp16")];
+            tensor<int32, [3]> transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor<int32, [3]>([1, 2, 0])];
+            tensor<fp16, [1, 512, ?]> transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = x_25_batch_first_cast_fp16_0)[name = string("transpose_2")];
+            tensor<fp16, [1, 512, ?]> var_159 = mul(x = transpose_1_cast_fp16, y = keep_cast_fp16)[name = string("op_159_cast_fp16")];
+            tensor<int32, [1]> input_lengths_tmp = identity(x = input_lengths)[name = string("input_lengths_tmp")];
+        } -> (var_159);
+}

iteration_3/compiled/text_encoder_fp16.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d7f6e5869bb9d523956183e0facdff160c301d28113290efa329ae7bf72d3ce
+size 11208000

iteration_3/swift/.build/.lock

@@ -0,0 +1 @@
+71408

iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/Iter3Bench.d

@@ -0,0 +1,4 @@
+/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Iter3Bench.build/main.swift.o : /Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3Bench/main.swift /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_DarwinFoundation1.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_DarwinFoundation2.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_DarwinFoundation3.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/XPC.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/ObjectiveC.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreML.framework/Modules/CoreML.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Combine.framework/Modules/Combine.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_StringProcessing.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Dispatch.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Metal.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/System.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Darwin.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Foundation.framework/Modules/Foundation.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/CoreFoundation.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Observation.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Synchronization.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreVideo.framework/Modules/CoreVideo.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreGraphics.framework/Modules/CoreGraphics.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/UniformTypeIdentifiers.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_Builtin_float.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Swift.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/IOKit.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreText.framework/Modules/CoreText.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_Concurrency.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_DarwinFoundation1.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_DarwinFoundation2.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_DarwinFoundation3.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/XPC.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/ObjectiveC.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreML.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Combine.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_StringProcessing.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Dispatch.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Metal.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/System.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Darwin.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Foundation.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreFoundation.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Observation.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Synchronization.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreVideo.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreGraphics.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/UniformTypeIdentifiers.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_Builtin_float.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Swift.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/IOKit.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreText.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_Concurrency.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/_DarwinFoundation2.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/XPC.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/ObjectiveC.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreML.framework/Headers/CoreML.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/Dispatch.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Metal.framework/Headers/Metal.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Foundation.framework/Headers/Foundation.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreGraphics.framework/Headers/CoreGraphics.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/ApplicationServices.framework/Headers/ApplicationServices.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/UniformTypeIdentifiers.framework/Headers/UniformTypeIdentifiers.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreText.framework/Headers/CoreText.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Security.framework/Headers/Security.apinotes
+/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules/Iter3Bench.swiftmodule : /Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3Bench/main.swift /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_DarwinFoundation1.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_DarwinFoundation2.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_DarwinFoundation3.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/XPC.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/ObjectiveC.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreML.framework/Modules/CoreML.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Combine.framework/Modules/Combine.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_StringProcessing.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Dispatch.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Metal.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/System.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Darwin.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Foundation.framework/Modules/Foundation.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/CoreFoundation.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Observation.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Synchronization.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreVideo.framework/Modules/CoreVideo.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreGraphics.framework/Modules/CoreGraphics.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/UniformTypeIdentifiers.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_Builtin_float.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Swift.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/IOKit.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreText.framework/Modules/CoreText.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_Concurrency.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_DarwinFoundation1.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_DarwinFoundation2.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_DarwinFoundation3.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/XPC.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/ObjectiveC.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreML.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Combine.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_StringProcessing.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Dispatch.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Metal.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/System.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Darwin.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Foundation.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreFoundation.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Observation.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Synchronization.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreVideo.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreGraphics.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/UniformTypeIdentifiers.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_Builtin_float.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Swift.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/IOKit.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreText.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_Concurrency.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/_DarwinFoundation2.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/XPC.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/ObjectiveC.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreML.framework/Headers/CoreML.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/Dispatch.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Metal.framework/Headers/Metal.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Foundation.framework/Headers/Foundation.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreGraphics.framework/Headers/CoreGraphics.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/ApplicationServices.framework/Headers/ApplicationServices.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/UniformTypeIdentifiers.framework/Headers/UniformTypeIdentifiers.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreText.framework/Headers/CoreText.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Security.framework/Headers/Security.apinotes
+/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules/Iter3Bench.swiftdoc : /Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3Bench/main.swift /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_DarwinFoundation1.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_DarwinFoundation2.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_DarwinFoundation3.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/XPC.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/ObjectiveC.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreML.framework/Modules/CoreML.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Combine.framework/Modules/Combine.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_StringProcessing.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Dispatch.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Metal.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/System.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Darwin.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Foundation.framework/Modules/Foundation.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/CoreFoundation.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Observation.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Synchronization.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreVideo.framework/Modules/CoreVideo.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreGraphics.framework/Modules/CoreGraphics.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/UniformTypeIdentifiers.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_Builtin_float.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Swift.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/IOKit.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreText.framework/Modules/CoreText.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_Concurrency.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_DarwinFoundation1.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_DarwinFoundation2.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_DarwinFoundation3.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/XPC.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/ObjectiveC.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreML.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Combine.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_StringProcessing.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Dispatch.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Metal.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/System.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Darwin.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Foundation.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreFoundation.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Observation.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Synchronization.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreVideo.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreGraphics.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/UniformTypeIdentifiers.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_Builtin_float.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Swift.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/IOKit.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreText.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_Concurrency.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/_DarwinFoundation2.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/XPC.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/ObjectiveC.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreML.framework/Headers/CoreML.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/Dispatch.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Metal.framework/Headers/Metal.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Foundation.framework/Headers/Foundation.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreGraphics.framework/Headers/CoreGraphics.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/ApplicationServices.framework/Headers/ApplicationServices.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/UniformTypeIdentifiers.framework/Headers/UniformTypeIdentifiers.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreText.framework/Headers/CoreText.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Security.framework/Headers/Security.apinotes
+/Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/.build/arm64-apple-macosx/release/Modules/Iter3Bench.swiftsourceinfo : /Users/kikow/brandon/voicelink/mobius-styletts2-coreml/models/tts/styletts2/iteration_3/swift/Sources/Iter3Bench/main.swift /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_DarwinFoundation1.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_DarwinFoundation2.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_DarwinFoundation3.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/XPC.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/ObjectiveC.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreML.framework/Modules/CoreML.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Combine.framework/Modules/Combine.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_StringProcessing.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Dispatch.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Metal.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/System.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Darwin.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Foundation.framework/Modules/Foundation.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/CoreFoundation.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Observation.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Synchronization.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreVideo.framework/Modules/CoreVideo.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreGraphics.framework/Modules/CoreGraphics.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/UniformTypeIdentifiers.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_Builtin_float.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/Swift.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/IOKit.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreText.framework/Modules/CoreText.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/lib/swift/_Concurrency.swiftmodule/arm64e-apple-macos.swiftinterface /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_DarwinFoundation1.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_DarwinFoundation2.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_DarwinFoundation3.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/XPC.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/ObjectiveC.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreML.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Combine.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_StringProcessing.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Dispatch.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Metal.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/System.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Darwin.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Foundation.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreFoundation.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Observation.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Synchronization.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreVideo.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreGraphics.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/UniformTypeIdentifiers.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_Builtin_float.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/Swift.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/IOKit.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/CoreText.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/lib/swift/macosx/prebuilt-modules/26.4/_Concurrency.swiftmodule/arm64e-apple-macos.swiftmodule /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/_DarwinFoundation2.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/XPC.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/ObjectiveC.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreML.framework/Headers/CoreML.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/usr/include/Dispatch.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Metal.framework/Headers/Metal.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Foundation.framework/Headers/Foundation.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreGraphics.framework/Headers/CoreGraphics.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/ApplicationServices.framework/Headers/ApplicationServices.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/UniformTypeIdentifiers.framework/Headers/UniformTypeIdentifiers.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/CoreText.framework/Headers/CoreText.apinotes /Applications/Xcode-26.4.0.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX26.4.sdk/System/Library/Frameworks/Security.framework/Headers/Security.apinotes