diff --git a/ANE/ANE-zh/KokoroAlbert.mlmodelc/analytics/coremldata.bin b/ANE/ANE-zh/KokoroAlbert.mlmodelc/analytics/coremldata.bin
new file mode 100644
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--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlbert.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
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+size 243
diff --git a/ANE/ANE-zh/KokoroAlbert.mlmodelc/coremldata.bin b/ANE/ANE-zh/KokoroAlbert.mlmodelc/coremldata.bin
new file mode 100644
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--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlbert.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bae8190320b079bc995d69b19c2247c2e66faf5ebefe0cfb99bddf4efa98a86c
+size 433
diff --git a/ANE/ANE-zh/KokoroAlbert.mlmodelc/metadata.json b/ANE/ANE-zh/KokoroAlbert.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..14825d4bf2d0131482e24da3a33b82e46676ee0f
--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlbert.mlmodelc/metadata.json
@@ -0,0 +1,90 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Mixed (Float16, Palettized (8 bits))",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "bert_dur",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios17.mul" : 13,
+      "Ios17.linear" : 73,
+      "Ios17.transpose" : 48,
+      "Ios17.sub" : 2,
+      "Ios16.constexprLutToDense" : 9,
+      "Range1d" : 1,
+      "Ios17.matmul" : 24,
+      "Shape" : 1,
+      "Ios17.gather" : 4,
+      "Ios17.add" : 38,
+      "Ios16.softmax" : 12,
+      "Ios17.expandDims" : 3,
+      "Ios16.gelu" : 12,
+      "Ios17.cast" : 6,
+      "Ios17.layerNorm" : 25,
+      "Ios17.reshape" : 48
+    },
+    "computePrecision" : "Mixed (Float16, Int16, Int32, UInt16)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-05-03",
+      "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 × 2...512",
+        "shapeRange" : "[[1, 1], [2, 512]]",
+        "formattedType" : "MultiArray (Int32 1 × 37)",
+        "type" : "MultiArray",
+        "shape" : "[1, 37]",
+        "name" : "input_ids",
+        "shortDescription" : ""
+      },
+      {
+        "dataType" : "Int32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 2...512",
+        "shapeRange" : "[[1, 1], [2, 512]]",
+        "formattedType" : "MultiArray (Int32 1 × 37)",
+        "type" : "MultiArray",
+        "shape" : "[1, 37]",
+        "name" : "attention_mask",
+        "shortDescription" : ""
+      }
+    ],
+    "generatedClassName" : "KokoroAlbert",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/ANE/ANE-zh/KokoroAlbert.mlmodelc/model.mil b/ANE/ANE-zh/KokoroAlbert.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..5a192119856009b2ff1099024bf1df016d2b44be
--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlbert.mlmodelc/model.mil
@@ -0,0 +1,560 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})]
+{
+    func main<ios17>(tensor<int32, [1, ?]> attention_mask, tensor<int32, [1, ?]> input_ids) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"attention_mask", [1, 37]}, {"input_ids", [1, 37]}}), ("RangeDims", {{"attention_mask", [[1, 1], [2, 512]]}, {"input_ids", [[1, 1], [2, 512]]}})))] {
+            tensor<int32, [1, ?]> input_3 = sub(x = input_ids, y = input_ids)[name = tensor<string, []>("sub_0")];
+            tensor<int32, []> var_21 = const()[name = tensor<string, []>("op_21"), val = tensor<int32, []>(-1)];
+            tensor<int32, [2]> var_40_shape = shape(x = input_ids)[name = tensor<string, []>("op_40_shape")];
+            tensor<int32, []> gather_0_axis_0 = const()[name = tensor<string, []>("gather_0_axis_0"), val = tensor<int32, []>(0)];
+            tensor<int32, []> gather_0_batch_dims_0 = const()[name = tensor<string, []>("gather_0_batch_dims_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> gather_0_validate_indices_0 = const()[name = tensor<string, []>("gather_0_validate_indices_0"), val = tensor<bool, []>(false)];
+            tensor<string, []> var_40_shape_to_int16_dtype_0 = const()[name = tensor<string, []>("op_40_shape_to_int16_dtype_0"), val = tensor<string, []>("int16")];
+            tensor<uint16, []> gather_0_indices_0_to_uint16 = const()[name = tensor<string, []>("gather_0_indices_0_to_uint16"), val = tensor<uint16, []>(1)];
+            tensor<int16, [2]> var_40_shape_to_int16 = cast(dtype = var_40_shape_to_int16_dtype_0, x = var_40_shape)[name = tensor<string, []>("cast_5")];
+            tensor<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_40_shape_to_int16)[name = tensor<string, []>("gather_0_cast_uint16")];
+            tensor<string, []> gather_0_cast_uint16_to_int32_dtype_0 = const()[name = tensor<string, []>("gather_0_cast_uint16_to_int32_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, []> const_0 = const()[name = tensor<string, []>("const_0"), val = tensor<int32, []>(0)];
+            tensor<int32, []> const_1 = const()[name = tensor<string, []>("const_1"), val = tensor<int32, []>(1)];
+            tensor<int32, []> gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = tensor<string, []>("cast_4")];
+            tensor<int32, [?]> var_41 = range_1d(end = gather_0_cast_uint16_to_int32, start = const_0, step = const_1)[name = tensor<string, []>("op_41")];
+            tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<int32, [1, ?]> input_1 = expand_dims(axes = input_1_axes_0, x = var_41)[name = tensor<string, []>("input_1")];
+            tensor<int32, []> var_44_axis_0 = const()[name = tensor<string, []>("op_44_axis_0"), val = tensor<int32, []>(0)];
+            tensor<int32, []> var_44_batch_dims_0 = const()[name = tensor<string, []>("op_44_batch_dims_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> var_44_validate_indices_0 = const()[name = tensor<string, []>("op_44_validate_indices_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [178, 128]> bert_word_embeddings_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [22784]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22912))), name = tensor<string, []>("bert_word_embeddings_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([178, 128])];
+            tensor<string, []> input_ids_to_uint16_dtype_0 = const()[name = tensor<string, []>("input_ids_to_uint16_dtype_0"), val = tensor<string, []>("uint16")];
+            tensor<uint16, [1, ?]> input_ids_to_uint16 = cast(dtype = input_ids_to_uint16_dtype_0, x = input_ids)[name = tensor<string, []>("cast_3")];
+            tensor<fp16, [1, ?, 128]> var_44_cast_fp16_cast_uint16 = gather(axis = var_44_axis_0, batch_dims = var_44_batch_dims_0, indices = input_ids_to_uint16, validate_indices = var_44_validate_indices_0, x = bert_word_embeddings_weight_to_fp16_palettized)[name = tensor<string, []>("op_44_cast_fp16_cast_uint16")];
+            tensor<int32, []> var_46_axis_0 = const()[name = tensor<string, []>("op_46_axis_0"), val = tensor<int32, []>(0)];
+            tensor<int32, []> var_46_batch_dims_0 = const()[name = tensor<string, []>("op_46_batch_dims_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> var_46_validate_indices_0 = const()[name = tensor<string, []>("op_46_validate_indices_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [512, 128]> bert_position_embeddings_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23488))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89088))), name = tensor<string, []>("bert_position_embeddings_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<string, []> input_1_to_uint16_dtype_0 = const()[name = tensor<string, []>("input_1_to_uint16_dtype_0"), val = tensor<string, []>("uint16")];
+            tensor<uint16, [1, ?]> input_1_to_uint16 = cast(dtype = input_1_to_uint16_dtype_0, x = input_1)[name = tensor<string, []>("cast_2")];
+            tensor<fp16, [1, ?, 128]> var_46_cast_fp16_cast_uint16 = gather(axis = var_46_axis_0, batch_dims = var_46_batch_dims_0, indices = input_1_to_uint16, validate_indices = var_46_validate_indices_0, x = bert_position_embeddings_weight_to_fp16_palettized)[name = tensor<string, []>("op_46_cast_fp16_cast_uint16")];
+            tensor<fp16, [1, ?, 128]> var_47_cast_fp16 = add(x = var_44_cast_fp16_cast_uint16, y = var_46_cast_fp16_cast_uint16)[name = tensor<string, []>("op_47_cast_fp16")];
+            tensor<int32, []> var_49_axis_0 = const()[name = tensor<string, []>("op_49_axis_0"), val = tensor<int32, []>(0)];
+            tensor<int32, []> var_49_batch_dims_0 = const()[name = tensor<string, []>("op_49_batch_dims_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> var_49_validate_indices_0 = const()[name = tensor<string, []>("op_49_validate_indices_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [2, 128]> bert_token_type_embeddings_weight_to_fp16 = const()[name = tensor<string, []>("bert_token_type_embeddings_weight_to_fp16"), val = tensor<fp16, [2, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89664)))];
+            tensor<string, []> input_3_to_uint16_dtype_0 = const()[name = tensor<string, []>("input_3_to_uint16_dtype_0"), val = tensor<string, []>("uint16")];
+            tensor<uint16, [1, ?]> input_3_to_uint16 = cast(dtype = input_3_to_uint16_dtype_0, x = input_3)[name = tensor<string, []>("cast_1")];
+            tensor<fp16, [1, ?, 128]> var_49_cast_fp16_cast_uint16 = gather(axis = var_49_axis_0, batch_dims = var_49_batch_dims_0, indices = input_3_to_uint16, validate_indices = var_49_validate_indices_0, x = bert_token_type_embeddings_weight_to_fp16)[name = tensor<string, []>("op_49_cast_fp16_cast_uint16")];
+            tensor<fp16, [1, ?, 128]> input_5_cast_fp16 = add(x = var_47_cast_fp16, y = var_49_cast_fp16_cast_uint16)[name = tensor<string, []>("input_5_cast_fp16")];
+            tensor<int32, [1]> input_7_axes_0 = const()[name = tensor<string, []>("input_7_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [128]> bert_embedding_LayerNorm_weight_to_fp16 = const()[name = tensor<string, []>("bert_embedding_LayerNorm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90240)))];
+            tensor<fp16, [128]> bert_embedding_LayerNorm_bias_to_fp16 = const()[name = tensor<string, []>("bert_embedding_LayerNorm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90560)))];
+            tensor<fp16, []> var_19_to_fp16 = const()[name = tensor<string, []>("op_19_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
+            tensor<fp16, [1, ?, 128]> input_7_cast_fp16 = layer_norm(axes = input_7_axes_0, beta = bert_embedding_LayerNorm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_embedding_LayerNorm_weight_to_fp16, x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
+            tensor<fp16, [768, 128]> bert_embedding_projection_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [98304]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90880))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189248))), name = tensor<string, []>("bert_embedding_projection_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([768, 128])];
+            tensor<fp16, [768]> bert_embedding_projection_bias_to_fp16 = const()[name = tensor<string, []>("bert_embedding_projection_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189824)))];
+            tensor<fp16, [1, ?, 768]> linear_0_cast_fp16 = linear(bias = bert_embedding_projection_bias_to_fp16, weight = bert_embedding_projection_weight_to_fp16_palettized, x = input_7_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
+            tensor<int32, [1]> var_58_axes_0 = const()[name = tensor<string, []>("op_58_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1, 1, ?]> var_58 = expand_dims(axes = var_58_axes_0, x = attention_mask)[name = tensor<string, []>("op_58")];
+            tensor<int32, [1]> var_59_axes_0 = const()[name = tensor<string, []>("op_59_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<int32, [1, 1, 1, ?]> var_59 = expand_dims(axes = var_59_axes_0, x = var_58)[name = tensor<string, []>("op_59")];
+            tensor<fp16, []> var_15_to_fp16 = const()[name = tensor<string, []>("op_15_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<string, []> var_60_to_fp16_dtype_0 = const()[name = tensor<string, []>("op_60_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [1, 1, 1, ?]> var_59_to_fp16 = cast(dtype = var_60_to_fp16_dtype_0, x = var_59)[name = tensor<string, []>("cast_0")];
+            tensor<fp16, [1, 1, 1, ?]> var_61_cast_fp16 = sub(x = var_15_to_fp16, y = var_59_to_fp16)[name = tensor<string, []>("op_61_cast_fp16")];
+            tensor<fp16, []> var_62_to_fp16 = const()[name = tensor<string, []>("op_62_to_fp16"), val = tensor<fp16, []>(-0x1.388p+13)];
+            tensor<fp16, [1, 1, 1, ?]> attention_mask_cast_fp16 = mul(x = var_61_cast_fp16, y = var_62_to_fp16)[name = tensor<string, []>("attention_mask_cast_fp16")];
+            tensor<fp16, [768, 768]> bert_query_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [589824]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191424))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(781312))), name = tensor<string, []>("bert_query_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([768, 768])];
+            tensor<fp16, [768]> bert_query_bias_to_fp16 = const()[name = tensor<string, []>("bert_query_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(781888)))];
+            tensor<fp16, [1, ?, 768]> linear_1_cast_fp16 = linear(bias = bert_query_bias_to_fp16, weight = bert_query_weight_to_fp16_palettized, x = linear_0_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")];
+            tensor<int32, [4]> concat_0x = const()[name = tensor<string, []>("concat_0x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_70_cast_fp16 = reshape(shape = concat_0x, x = linear_1_cast_fp16)[name = tensor<string, []>("op_70_cast_fp16")];
+            tensor<fp16, [768, 768]> bert_key_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [589824]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(783488))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1373376))), name = tensor<string, []>("bert_key_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([768, 768])];
+            tensor<fp16, [768]> bert_key_bias_to_fp16 = const()[name = tensor<string, []>("bert_key_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1373952)))];
+            tensor<fp16, [1, ?, 768]> linear_2_cast_fp16 = linear(bias = bert_key_bias_to_fp16, weight = bert_key_weight_to_fp16_palettized, x = linear_0_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")];
+            tensor<int32, [4]> concat_1x = const()[name = tensor<string, []>("concat_1x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_76_cast_fp16 = reshape(shape = concat_1x, x = linear_2_cast_fp16)[name = tensor<string, []>("op_76_cast_fp16")];
+            tensor<fp16, [768, 768]> bert_value_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [589824]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1375552))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1965440))), name = tensor<string, []>("bert_value_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([768, 768])];
+            tensor<fp16, [768]> bert_value_bias_to_fp16 = const()[name = tensor<string, []>("bert_value_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1966016)))];
+            tensor<fp16, [1, ?, 768]> linear_3_cast_fp16 = linear(bias = bert_value_bias_to_fp16, weight = bert_value_weight_to_fp16_palettized, x = linear_0_cast_fp16)[name = tensor<string, []>("linear_3_cast_fp16")];
+            tensor<int32, [4]> concat_2x = const()[name = tensor<string, []>("concat_2x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_82_cast_fp16 = reshape(shape = concat_2x, x = linear_3_cast_fp16)[name = tensor<string, []>("op_82_cast_fp16")];
+            tensor<int32, [4]> V_1_perm_0 = const()[name = tensor<string, []>("V_1_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> var_85_transpose_x_0 = const()[name = tensor<string, []>("op_85_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> var_85_transpose_y_0 = const()[name = tensor<string, []>("op_85_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_48_perm_0 = const()[name = tensor<string, []>("transpose_48_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_49_perm_0 = const()[name = tensor<string, []>("transpose_49_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 12, 64, ?]> transpose_49 = transpose(perm = transpose_49_perm_0, x = var_76_cast_fp16)[name = tensor<string, []>("transpose_118")];
+            tensor<fp16, [1, 12, ?, 64]> transpose_48 = transpose(perm = transpose_48_perm_0, x = var_70_cast_fp16)[name = tensor<string, []>("transpose_119")];
+            tensor<fp16, [1, 12, ?, ?]> var_85_cast_fp16 = matmul(transpose_x = var_85_transpose_x_0, transpose_y = var_85_transpose_y_0, x = transpose_48, y = transpose_49)[name = tensor<string, []>("op_85_cast_fp16")];
+            tensor<fp16, []> _inversed_attn_scores_1_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_attn_scores_1_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
+            tensor<fp16, [1, 12, ?, ?]> _inversed_attn_scores_1_cast_fp16 = mul(x = var_85_cast_fp16, y = _inversed_attn_scores_1_y_0_to_fp16)[name = tensor<string, []>("_inversed_attn_scores_1_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> input_9_cast_fp16 = add(x = _inversed_attn_scores_1_cast_fp16, y = attention_mask_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> attn_probs_1_cast_fp16 = softmax(axis = var_21, x = input_9_cast_fp16)[name = tensor<string, []>("attn_probs_1_cast_fp16")];
+            tensor<bool, []> context_1_transpose_x_0 = const()[name = tensor<string, []>("context_1_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> context_1_transpose_y_0 = const()[name = tensor<string, []>("context_1_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 12, ?, 64]> V_1_cast_fp16 = transpose(perm = V_1_perm_0, x = var_82_cast_fp16)[name = tensor<string, []>("transpose_117")];
+            tensor<fp16, [1, 12, ?, 64]> context_1_cast_fp16 = matmul(transpose_x = context_1_transpose_x_0, transpose_y = context_1_transpose_y_0, x = attn_probs_1_cast_fp16, y = V_1_cast_fp16)[name = tensor<string, []>("context_1_cast_fp16")];
+            tensor<int32, [4]> var_91_perm_0 = const()[name = tensor<string, []>("op_91_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> concat_3x = const()[name = tensor<string, []>("concat_3x"), val = tensor<int32, [3]>([1, -1, 768])];
+            tensor<fp16, [1, ?, 12, 64]> var_91_cast_fp16 = transpose(perm = var_91_perm_0, x = context_1_cast_fp16)[name = tensor<string, []>("transpose_116")];
+            tensor<fp16, [1, ?, 768]> input_11_cast_fp16 = reshape(shape = concat_3x, x = var_91_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
+            tensor<fp16, [768, 768]> bert_attn_dense_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [589824]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1967616))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2557504))), name = tensor<string, []>("bert_attn_dense_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([768, 768])];
+            tensor<fp16, [768]> bert_attn_dense_bias_to_fp16 = const()[name = tensor<string, []>("bert_attn_dense_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2558080)))];
+            tensor<fp16, [1, ?, 768]> linear_4_cast_fp16 = linear(bias = bert_attn_dense_bias_to_fp16, weight = bert_attn_dense_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = tensor<string, []>("linear_4_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_13_cast_fp16 = add(x = linear_4_cast_fp16, y = linear_0_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
+            tensor<int32, [1]> input_15_axes_0 = const()[name = tensor<string, []>("input_15_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [768]> bert_attn_LayerNorm_weight_to_fp16 = const()[name = tensor<string, []>("bert_attn_LayerNorm_weight_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2559680)))];
+            tensor<fp16, [768]> bert_attn_LayerNorm_bias_to_fp16 = const()[name = tensor<string, []>("bert_attn_LayerNorm_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2561280)))];
+            tensor<fp16, [1, ?, 768]> input_15_cast_fp16 = layer_norm(axes = input_15_axes_0, beta = bert_attn_LayerNorm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_attn_LayerNorm_weight_to_fp16, x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
+            tensor<fp16, [2048, 768]> bert_ffn_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1572864]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2562880))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4135808))), name = tensor<string, []>("bert_ffn_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([2048, 768])];
+            tensor<fp16, [2048]> bert_ffn_bias_to_fp16 = const()[name = tensor<string, []>("bert_ffn_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4136384)))];
+            tensor<fp16, [1, ?, 2048]> linear_5_cast_fp16 = linear(bias = bert_ffn_bias_to_fp16, weight = bert_ffn_weight_to_fp16_palettized, x = input_15_cast_fp16)[name = tensor<string, []>("linear_5_cast_fp16")];
+            tensor<string, []> input_17_mode_0 = const()[name = tensor<string, []>("input_17_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
+            tensor<fp16, [1, ?, 2048]> input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = linear_5_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
+            tensor<fp16, [768, 2048]> bert_ffn_output_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1572864]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4140544))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5713472))), name = tensor<string, []>("bert_ffn_output_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([768, 2048])];
+            tensor<fp16, [768]> bert_ffn_output_bias_to_fp16 = const()[name = tensor<string, []>("bert_ffn_output_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5714048)))];
+            tensor<fp16, [1, ?, 768]> linear_6_cast_fp16 = linear(bias = bert_ffn_output_bias_to_fp16, weight = bert_ffn_output_weight_to_fp16_palettized, x = input_17_cast_fp16)[name = tensor<string, []>("linear_6_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_19_cast_fp16 = add(x = linear_6_cast_fp16, y = input_15_cast_fp16)[name = tensor<string, []>("input_19_cast_fp16")];
+            tensor<int32, [1]> hidden_3_axes_0 = const()[name = tensor<string, []>("hidden_3_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [768]> bert_full_layer_norm_weight_to_fp16 = const()[name = tensor<string, []>("bert_full_layer_norm_weight_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5715648)))];
+            tensor<fp16, [768]> bert_full_layer_norm_bias_to_fp16 = const()[name = tensor<string, []>("bert_full_layer_norm_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5717248)))];
+            tensor<fp16, [1, ?, 768]> hidden_3_cast_fp16 = layer_norm(axes = hidden_3_axes_0, beta = bert_full_layer_norm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_full_layer_norm_weight_to_fp16, x = input_19_cast_fp16)[name = tensor<string, []>("hidden_3_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_7_cast_fp16 = linear(bias = bert_query_bias_to_fp16, weight = bert_query_weight_to_fp16_palettized, x = hidden_3_cast_fp16)[name = tensor<string, []>("linear_7_cast_fp16")];
+            tensor<int32, [4]> concat_4x = const()[name = tensor<string, []>("concat_4x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_121_cast_fp16 = reshape(shape = concat_4x, x = linear_7_cast_fp16)[name = tensor<string, []>("op_121_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_8_cast_fp16 = linear(bias = bert_key_bias_to_fp16, weight = bert_key_weight_to_fp16_palettized, x = hidden_3_cast_fp16)[name = tensor<string, []>("linear_8_cast_fp16")];
+            tensor<int32, [4]> concat_5x = const()[name = tensor<string, []>("concat_5x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_127_cast_fp16 = reshape(shape = concat_5x, x = linear_8_cast_fp16)[name = tensor<string, []>("op_127_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_9_cast_fp16 = linear(bias = bert_value_bias_to_fp16, weight = bert_value_weight_to_fp16_palettized, x = hidden_3_cast_fp16)[name = tensor<string, []>("linear_9_cast_fp16")];
+            tensor<int32, [4]> concat_6x = const()[name = tensor<string, []>("concat_6x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_133_cast_fp16 = reshape(shape = concat_6x, x = linear_9_cast_fp16)[name = tensor<string, []>("op_133_cast_fp16")];
+            tensor<int32, [4]> V_3_perm_0 = const()[name = tensor<string, []>("V_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> var_136_transpose_x_0 = const()[name = tensor<string, []>("op_136_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> var_136_transpose_y_0 = const()[name = tensor<string, []>("op_136_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_50_perm_0 = const()[name = tensor<string, []>("transpose_50_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_51_perm_0 = const()[name = tensor<string, []>("transpose_51_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 12, 64, ?]> transpose_51 = transpose(perm = transpose_51_perm_0, x = var_127_cast_fp16)[name = tensor<string, []>("transpose_114")];
+            tensor<fp16, [1, 12, ?, 64]> transpose_50 = transpose(perm = transpose_50_perm_0, x = var_121_cast_fp16)[name = tensor<string, []>("transpose_115")];
+            tensor<fp16, [1, 12, ?, ?]> var_136_cast_fp16 = matmul(transpose_x = var_136_transpose_x_0, transpose_y = var_136_transpose_y_0, x = transpose_50, y = transpose_51)[name = tensor<string, []>("op_136_cast_fp16")];
+            tensor<fp16, []> _inversed_attn_scores_3_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_attn_scores_3_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
+            tensor<fp16, [1, 12, ?, ?]> _inversed_attn_scores_3_cast_fp16 = mul(x = var_136_cast_fp16, y = _inversed_attn_scores_3_y_0_to_fp16)[name = tensor<string, []>("_inversed_attn_scores_3_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> input_21_cast_fp16 = add(x = _inversed_attn_scores_3_cast_fp16, y = attention_mask_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> attn_probs_3_cast_fp16 = softmax(axis = var_21, x = input_21_cast_fp16)[name = tensor<string, []>("attn_probs_3_cast_fp16")];
+            tensor<bool, []> context_3_transpose_x_0 = const()[name = tensor<string, []>("context_3_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> context_3_transpose_y_0 = const()[name = tensor<string, []>("context_3_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 12, ?, 64]> V_3_cast_fp16 = transpose(perm = V_3_perm_0, x = var_133_cast_fp16)[name = tensor<string, []>("transpose_113")];
+            tensor<fp16, [1, 12, ?, 64]> context_3_cast_fp16 = matmul(transpose_x = context_3_transpose_x_0, transpose_y = context_3_transpose_y_0, x = attn_probs_3_cast_fp16, y = V_3_cast_fp16)[name = tensor<string, []>("context_3_cast_fp16")];
+            tensor<int32, [4]> var_142_perm_0 = const()[name = tensor<string, []>("op_142_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> concat_7x = const()[name = tensor<string, []>("concat_7x"), val = tensor<int32, [3]>([1, -1, 768])];
+            tensor<fp16, [1, ?, 12, 64]> var_142_cast_fp16 = transpose(perm = var_142_perm_0, x = context_3_cast_fp16)[name = tensor<string, []>("transpose_112")];
+            tensor<fp16, [1, ?, 768]> input_23_cast_fp16 = reshape(shape = concat_7x, x = var_142_cast_fp16)[name = tensor<string, []>("input_23_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_10_cast_fp16 = linear(bias = bert_attn_dense_bias_to_fp16, weight = bert_attn_dense_weight_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor<string, []>("linear_10_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_25_cast_fp16 = add(x = linear_10_cast_fp16, y = hidden_3_cast_fp16)[name = tensor<string, []>("input_25_cast_fp16")];
+            tensor<int32, [1]> input_27_axes_0 = const()[name = tensor<string, []>("input_27_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> input_27_cast_fp16 = layer_norm(axes = input_27_axes_0, beta = bert_attn_LayerNorm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_attn_LayerNorm_weight_to_fp16, x = input_25_cast_fp16)[name = tensor<string, []>("input_27_cast_fp16")];
+            tensor<fp16, [1, ?, 2048]> linear_11_cast_fp16 = linear(bias = bert_ffn_bias_to_fp16, weight = bert_ffn_weight_to_fp16_palettized, x = input_27_cast_fp16)[name = tensor<string, []>("linear_11_cast_fp16")];
+            tensor<string, []> input_29_mode_0 = const()[name = tensor<string, []>("input_29_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
+            tensor<fp16, [1, ?, 2048]> input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = linear_11_cast_fp16)[name = tensor<string, []>("input_29_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_12_cast_fp16 = linear(bias = bert_ffn_output_bias_to_fp16, weight = bert_ffn_output_weight_to_fp16_palettized, x = input_29_cast_fp16)[name = tensor<string, []>("linear_12_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_31_cast_fp16 = add(x = linear_12_cast_fp16, y = input_27_cast_fp16)[name = tensor<string, []>("input_31_cast_fp16")];
+            tensor<int32, [1]> hidden_5_axes_0 = const()[name = tensor<string, []>("hidden_5_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> hidden_5_cast_fp16 = layer_norm(axes = hidden_5_axes_0, beta = bert_full_layer_norm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_full_layer_norm_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("hidden_5_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_13_cast_fp16 = linear(bias = bert_query_bias_to_fp16, weight = bert_query_weight_to_fp16_palettized, x = hidden_5_cast_fp16)[name = tensor<string, []>("linear_13_cast_fp16")];
+            tensor<int32, [4]> concat_8x = const()[name = tensor<string, []>("concat_8x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_172_cast_fp16 = reshape(shape = concat_8x, x = linear_13_cast_fp16)[name = tensor<string, []>("op_172_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_14_cast_fp16 = linear(bias = bert_key_bias_to_fp16, weight = bert_key_weight_to_fp16_palettized, x = hidden_5_cast_fp16)[name = tensor<string, []>("linear_14_cast_fp16")];
+            tensor<int32, [4]> concat_9x = const()[name = tensor<string, []>("concat_9x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_178_cast_fp16 = reshape(shape = concat_9x, x = linear_14_cast_fp16)[name = tensor<string, []>("op_178_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_15_cast_fp16 = linear(bias = bert_value_bias_to_fp16, weight = bert_value_weight_to_fp16_palettized, x = hidden_5_cast_fp16)[name = tensor<string, []>("linear_15_cast_fp16")];
+            tensor<int32, [4]> concat_10x = const()[name = tensor<string, []>("concat_10x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_184_cast_fp16 = reshape(shape = concat_10x, x = linear_15_cast_fp16)[name = tensor<string, []>("op_184_cast_fp16")];
+            tensor<int32, [4]> V_5_perm_0 = const()[name = tensor<string, []>("V_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> var_187_transpose_x_0 = const()[name = tensor<string, []>("op_187_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> var_187_transpose_y_0 = const()[name = tensor<string, []>("op_187_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_52_perm_0 = const()[name = tensor<string, []>("transpose_52_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_53_perm_0 = const()[name = tensor<string, []>("transpose_53_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 12, 64, ?]> transpose_53 = transpose(perm = transpose_53_perm_0, x = var_178_cast_fp16)[name = tensor<string, []>("transpose_110")];
+            tensor<fp16, [1, 12, ?, 64]> transpose_52 = transpose(perm = transpose_52_perm_0, x = var_172_cast_fp16)[name = tensor<string, []>("transpose_111")];
+            tensor<fp16, [1, 12, ?, ?]> var_187_cast_fp16 = matmul(transpose_x = var_187_transpose_x_0, transpose_y = var_187_transpose_y_0, x = transpose_52, y = transpose_53)[name = tensor<string, []>("op_187_cast_fp16")];
+            tensor<fp16, []> _inversed_attn_scores_5_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_attn_scores_5_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
+            tensor<fp16, [1, 12, ?, ?]> _inversed_attn_scores_5_cast_fp16 = mul(x = var_187_cast_fp16, y = _inversed_attn_scores_5_y_0_to_fp16)[name = tensor<string, []>("_inversed_attn_scores_5_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> input_33_cast_fp16 = add(x = _inversed_attn_scores_5_cast_fp16, y = attention_mask_cast_fp16)[name = tensor<string, []>("input_33_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> attn_probs_5_cast_fp16 = softmax(axis = var_21, x = input_33_cast_fp16)[name = tensor<string, []>("attn_probs_5_cast_fp16")];
+            tensor<bool, []> context_5_transpose_x_0 = const()[name = tensor<string, []>("context_5_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> context_5_transpose_y_0 = const()[name = tensor<string, []>("context_5_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 12, ?, 64]> V_5_cast_fp16 = transpose(perm = V_5_perm_0, x = var_184_cast_fp16)[name = tensor<string, []>("transpose_109")];
+            tensor<fp16, [1, 12, ?, 64]> context_5_cast_fp16 = matmul(transpose_x = context_5_transpose_x_0, transpose_y = context_5_transpose_y_0, x = attn_probs_5_cast_fp16, y = V_5_cast_fp16)[name = tensor<string, []>("context_5_cast_fp16")];
+            tensor<int32, [4]> var_193_perm_0 = const()[name = tensor<string, []>("op_193_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> concat_11x = const()[name = tensor<string, []>("concat_11x"), val = tensor<int32, [3]>([1, -1, 768])];
+            tensor<fp16, [1, ?, 12, 64]> var_193_cast_fp16 = transpose(perm = var_193_perm_0, x = context_5_cast_fp16)[name = tensor<string, []>("transpose_108")];
+            tensor<fp16, [1, ?, 768]> input_35_cast_fp16 = reshape(shape = concat_11x, x = var_193_cast_fp16)[name = tensor<string, []>("input_35_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_16_cast_fp16 = linear(bias = bert_attn_dense_bias_to_fp16, weight = bert_attn_dense_weight_to_fp16_palettized, x = input_35_cast_fp16)[name = tensor<string, []>("linear_16_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_37_cast_fp16 = add(x = linear_16_cast_fp16, y = hidden_5_cast_fp16)[name = tensor<string, []>("input_37_cast_fp16")];
+            tensor<int32, [1]> input_39_axes_0 = const()[name = tensor<string, []>("input_39_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> input_39_cast_fp16 = layer_norm(axes = input_39_axes_0, beta = bert_attn_LayerNorm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_attn_LayerNorm_weight_to_fp16, x = input_37_cast_fp16)[name = tensor<string, []>("input_39_cast_fp16")];
+            tensor<fp16, [1, ?, 2048]> linear_17_cast_fp16 = linear(bias = bert_ffn_bias_to_fp16, weight = bert_ffn_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = tensor<string, []>("linear_17_cast_fp16")];
+            tensor<string, []> input_41_mode_0 = const()[name = tensor<string, []>("input_41_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
+            tensor<fp16, [1, ?, 2048]> input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = linear_17_cast_fp16)[name = tensor<string, []>("input_41_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_18_cast_fp16 = linear(bias = bert_ffn_output_bias_to_fp16, weight = bert_ffn_output_weight_to_fp16_palettized, x = input_41_cast_fp16)[name = tensor<string, []>("linear_18_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_43_cast_fp16 = add(x = linear_18_cast_fp16, y = input_39_cast_fp16)[name = tensor<string, []>("input_43_cast_fp16")];
+            tensor<int32, [1]> hidden_7_axes_0 = const()[name = tensor<string, []>("hidden_7_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> hidden_7_cast_fp16 = layer_norm(axes = hidden_7_axes_0, beta = bert_full_layer_norm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_full_layer_norm_weight_to_fp16, x = input_43_cast_fp16)[name = tensor<string, []>("hidden_7_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_19_cast_fp16 = linear(bias = bert_query_bias_to_fp16, weight = bert_query_weight_to_fp16_palettized, x = hidden_7_cast_fp16)[name = tensor<string, []>("linear_19_cast_fp16")];
+            tensor<int32, [4]> concat_12x = const()[name = tensor<string, []>("concat_12x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_223_cast_fp16 = reshape(shape = concat_12x, x = linear_19_cast_fp16)[name = tensor<string, []>("op_223_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_20_cast_fp16 = linear(bias = bert_key_bias_to_fp16, weight = bert_key_weight_to_fp16_palettized, x = hidden_7_cast_fp16)[name = tensor<string, []>("linear_20_cast_fp16")];
+            tensor<int32, [4]> concat_13x = const()[name = tensor<string, []>("concat_13x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_229_cast_fp16 = reshape(shape = concat_13x, x = linear_20_cast_fp16)[name = tensor<string, []>("op_229_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_21_cast_fp16 = linear(bias = bert_value_bias_to_fp16, weight = bert_value_weight_to_fp16_palettized, x = hidden_7_cast_fp16)[name = tensor<string, []>("linear_21_cast_fp16")];
+            tensor<int32, [4]> concat_14x = const()[name = tensor<string, []>("concat_14x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_235_cast_fp16 = reshape(shape = concat_14x, x = linear_21_cast_fp16)[name = tensor<string, []>("op_235_cast_fp16")];
+            tensor<int32, [4]> V_7_perm_0 = const()[name = tensor<string, []>("V_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> var_238_transpose_x_0 = const()[name = tensor<string, []>("op_238_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> var_238_transpose_y_0 = const()[name = tensor<string, []>("op_238_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_54_perm_0 = const()[name = tensor<string, []>("transpose_54_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_55_perm_0 = const()[name = tensor<string, []>("transpose_55_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 12, 64, ?]> transpose_55 = transpose(perm = transpose_55_perm_0, x = var_229_cast_fp16)[name = tensor<string, []>("transpose_106")];
+            tensor<fp16, [1, 12, ?, 64]> transpose_54 = transpose(perm = transpose_54_perm_0, x = var_223_cast_fp16)[name = tensor<string, []>("transpose_107")];
+            tensor<fp16, [1, 12, ?, ?]> var_238_cast_fp16 = matmul(transpose_x = var_238_transpose_x_0, transpose_y = var_238_transpose_y_0, x = transpose_54, y = transpose_55)[name = tensor<string, []>("op_238_cast_fp16")];
+            tensor<fp16, []> _inversed_attn_scores_7_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_attn_scores_7_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
+            tensor<fp16, [1, 12, ?, ?]> _inversed_attn_scores_7_cast_fp16 = mul(x = var_238_cast_fp16, y = _inversed_attn_scores_7_y_0_to_fp16)[name = tensor<string, []>("_inversed_attn_scores_7_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> input_45_cast_fp16 = add(x = _inversed_attn_scores_7_cast_fp16, y = attention_mask_cast_fp16)[name = tensor<string, []>("input_45_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> attn_probs_7_cast_fp16 = softmax(axis = var_21, x = input_45_cast_fp16)[name = tensor<string, []>("attn_probs_7_cast_fp16")];
+            tensor<bool, []> context_7_transpose_x_0 = const()[name = tensor<string, []>("context_7_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> context_7_transpose_y_0 = const()[name = tensor<string, []>("context_7_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 12, ?, 64]> V_7_cast_fp16 = transpose(perm = V_7_perm_0, x = var_235_cast_fp16)[name = tensor<string, []>("transpose_105")];
+            tensor<fp16, [1, 12, ?, 64]> context_7_cast_fp16 = matmul(transpose_x = context_7_transpose_x_0, transpose_y = context_7_transpose_y_0, x = attn_probs_7_cast_fp16, y = V_7_cast_fp16)[name = tensor<string, []>("context_7_cast_fp16")];
+            tensor<int32, [4]> var_244_perm_0 = const()[name = tensor<string, []>("op_244_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> concat_15x = const()[name = tensor<string, []>("concat_15x"), val = tensor<int32, [3]>([1, -1, 768])];
+            tensor<fp16, [1, ?, 12, 64]> var_244_cast_fp16 = transpose(perm = var_244_perm_0, x = context_7_cast_fp16)[name = tensor<string, []>("transpose_104")];
+            tensor<fp16, [1, ?, 768]> input_47_cast_fp16 = reshape(shape = concat_15x, x = var_244_cast_fp16)[name = tensor<string, []>("input_47_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_22_cast_fp16 = linear(bias = bert_attn_dense_bias_to_fp16, weight = bert_attn_dense_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = tensor<string, []>("linear_22_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_49_cast_fp16 = add(x = linear_22_cast_fp16, y = hidden_7_cast_fp16)[name = tensor<string, []>("input_49_cast_fp16")];
+            tensor<int32, [1]> input_51_axes_0 = const()[name = tensor<string, []>("input_51_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> input_51_cast_fp16 = layer_norm(axes = input_51_axes_0, beta = bert_attn_LayerNorm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_attn_LayerNorm_weight_to_fp16, x = input_49_cast_fp16)[name = tensor<string, []>("input_51_cast_fp16")];
+            tensor<fp16, [1, ?, 2048]> linear_23_cast_fp16 = linear(bias = bert_ffn_bias_to_fp16, weight = bert_ffn_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = tensor<string, []>("linear_23_cast_fp16")];
+            tensor<string, []> input_53_mode_0 = const()[name = tensor<string, []>("input_53_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
+            tensor<fp16, [1, ?, 2048]> input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = linear_23_cast_fp16)[name = tensor<string, []>("input_53_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_24_cast_fp16 = linear(bias = bert_ffn_output_bias_to_fp16, weight = bert_ffn_output_weight_to_fp16_palettized, x = input_53_cast_fp16)[name = tensor<string, []>("linear_24_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_55_cast_fp16 = add(x = linear_24_cast_fp16, y = input_51_cast_fp16)[name = tensor<string, []>("input_55_cast_fp16")];
+            tensor<int32, [1]> hidden_9_axes_0 = const()[name = tensor<string, []>("hidden_9_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> hidden_9_cast_fp16 = layer_norm(axes = hidden_9_axes_0, beta = bert_full_layer_norm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_full_layer_norm_weight_to_fp16, x = input_55_cast_fp16)[name = tensor<string, []>("hidden_9_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_25_cast_fp16 = linear(bias = bert_query_bias_to_fp16, weight = bert_query_weight_to_fp16_palettized, x = hidden_9_cast_fp16)[name = tensor<string, []>("linear_25_cast_fp16")];
+            tensor<int32, [4]> concat_16x = const()[name = tensor<string, []>("concat_16x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_274_cast_fp16 = reshape(shape = concat_16x, x = linear_25_cast_fp16)[name = tensor<string, []>("op_274_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_26_cast_fp16 = linear(bias = bert_key_bias_to_fp16, weight = bert_key_weight_to_fp16_palettized, x = hidden_9_cast_fp16)[name = tensor<string, []>("linear_26_cast_fp16")];
+            tensor<int32, [4]> concat_17x = const()[name = tensor<string, []>("concat_17x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_280_cast_fp16 = reshape(shape = concat_17x, x = linear_26_cast_fp16)[name = tensor<string, []>("op_280_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_27_cast_fp16 = linear(bias = bert_value_bias_to_fp16, weight = bert_value_weight_to_fp16_palettized, x = hidden_9_cast_fp16)[name = tensor<string, []>("linear_27_cast_fp16")];
+            tensor<int32, [4]> concat_18x = const()[name = tensor<string, []>("concat_18x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_286_cast_fp16 = reshape(shape = concat_18x, x = linear_27_cast_fp16)[name = tensor<string, []>("op_286_cast_fp16")];
+            tensor<int32, [4]> V_9_perm_0 = const()[name = tensor<string, []>("V_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> var_289_transpose_x_0 = const()[name = tensor<string, []>("op_289_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> var_289_transpose_y_0 = const()[name = tensor<string, []>("op_289_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_56_perm_0 = const()[name = tensor<string, []>("transpose_56_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_57_perm_0 = const()[name = tensor<string, []>("transpose_57_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 12, 64, ?]> transpose_57 = transpose(perm = transpose_57_perm_0, x = var_280_cast_fp16)[name = tensor<string, []>("transpose_102")];
+            tensor<fp16, [1, 12, ?, 64]> transpose_56 = transpose(perm = transpose_56_perm_0, x = var_274_cast_fp16)[name = tensor<string, []>("transpose_103")];
+            tensor<fp16, [1, 12, ?, ?]> var_289_cast_fp16 = matmul(transpose_x = var_289_transpose_x_0, transpose_y = var_289_transpose_y_0, x = transpose_56, y = transpose_57)[name = tensor<string, []>("op_289_cast_fp16")];
+            tensor<fp16, []> _inversed_attn_scores_9_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_attn_scores_9_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
+            tensor<fp16, [1, 12, ?, ?]> _inversed_attn_scores_9_cast_fp16 = mul(x = var_289_cast_fp16, y = _inversed_attn_scores_9_y_0_to_fp16)[name = tensor<string, []>("_inversed_attn_scores_9_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> input_57_cast_fp16 = add(x = _inversed_attn_scores_9_cast_fp16, y = attention_mask_cast_fp16)[name = tensor<string, []>("input_57_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> attn_probs_9_cast_fp16 = softmax(axis = var_21, x = input_57_cast_fp16)[name = tensor<string, []>("attn_probs_9_cast_fp16")];
+            tensor<bool, []> context_9_transpose_x_0 = const()[name = tensor<string, []>("context_9_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> context_9_transpose_y_0 = const()[name = tensor<string, []>("context_9_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 12, ?, 64]> V_9_cast_fp16 = transpose(perm = V_9_perm_0, x = var_286_cast_fp16)[name = tensor<string, []>("transpose_101")];
+            tensor<fp16, [1, 12, ?, 64]> context_9_cast_fp16 = matmul(transpose_x = context_9_transpose_x_0, transpose_y = context_9_transpose_y_0, x = attn_probs_9_cast_fp16, y = V_9_cast_fp16)[name = tensor<string, []>("context_9_cast_fp16")];
+            tensor<int32, [4]> var_295_perm_0 = const()[name = tensor<string, []>("op_295_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> concat_19x = const()[name = tensor<string, []>("concat_19x"), val = tensor<int32, [3]>([1, -1, 768])];
+            tensor<fp16, [1, ?, 12, 64]> var_295_cast_fp16 = transpose(perm = var_295_perm_0, x = context_9_cast_fp16)[name = tensor<string, []>("transpose_100")];
+            tensor<fp16, [1, ?, 768]> input_59_cast_fp16 = reshape(shape = concat_19x, x = var_295_cast_fp16)[name = tensor<string, []>("input_59_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_28_cast_fp16 = linear(bias = bert_attn_dense_bias_to_fp16, weight = bert_attn_dense_weight_to_fp16_palettized, x = input_59_cast_fp16)[name = tensor<string, []>("linear_28_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_61_cast_fp16 = add(x = linear_28_cast_fp16, y = hidden_9_cast_fp16)[name = tensor<string, []>("input_61_cast_fp16")];
+            tensor<int32, [1]> input_63_axes_0 = const()[name = tensor<string, []>("input_63_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> input_63_cast_fp16 = layer_norm(axes = input_63_axes_0, beta = bert_attn_LayerNorm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_attn_LayerNorm_weight_to_fp16, x = input_61_cast_fp16)[name = tensor<string, []>("input_63_cast_fp16")];
+            tensor<fp16, [1, ?, 2048]> linear_29_cast_fp16 = linear(bias = bert_ffn_bias_to_fp16, weight = bert_ffn_weight_to_fp16_palettized, x = input_63_cast_fp16)[name = tensor<string, []>("linear_29_cast_fp16")];
+            tensor<string, []> input_65_mode_0 = const()[name = tensor<string, []>("input_65_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
+            tensor<fp16, [1, ?, 2048]> input_65_cast_fp16 = gelu(mode = input_65_mode_0, x = linear_29_cast_fp16)[name = tensor<string, []>("input_65_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_30_cast_fp16 = linear(bias = bert_ffn_output_bias_to_fp16, weight = bert_ffn_output_weight_to_fp16_palettized, x = input_65_cast_fp16)[name = tensor<string, []>("linear_30_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_67_cast_fp16 = add(x = linear_30_cast_fp16, y = input_63_cast_fp16)[name = tensor<string, []>("input_67_cast_fp16")];
+            tensor<int32, [1]> hidden_11_axes_0 = const()[name = tensor<string, []>("hidden_11_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> hidden_11_cast_fp16 = layer_norm(axes = hidden_11_axes_0, beta = bert_full_layer_norm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_full_layer_norm_weight_to_fp16, x = input_67_cast_fp16)[name = tensor<string, []>("hidden_11_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_31_cast_fp16 = linear(bias = bert_query_bias_to_fp16, weight = bert_query_weight_to_fp16_palettized, x = hidden_11_cast_fp16)[name = tensor<string, []>("linear_31_cast_fp16")];
+            tensor<int32, [4]> concat_20x = const()[name = tensor<string, []>("concat_20x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_325_cast_fp16 = reshape(shape = concat_20x, x = linear_31_cast_fp16)[name = tensor<string, []>("op_325_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_32_cast_fp16 = linear(bias = bert_key_bias_to_fp16, weight = bert_key_weight_to_fp16_palettized, x = hidden_11_cast_fp16)[name = tensor<string, []>("linear_32_cast_fp16")];
+            tensor<int32, [4]> concat_21x = const()[name = tensor<string, []>("concat_21x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_331_cast_fp16 = reshape(shape = concat_21x, x = linear_32_cast_fp16)[name = tensor<string, []>("op_331_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_33_cast_fp16 = linear(bias = bert_value_bias_to_fp16, weight = bert_value_weight_to_fp16_palettized, x = hidden_11_cast_fp16)[name = tensor<string, []>("linear_33_cast_fp16")];
+            tensor<int32, [4]> concat_22x = const()[name = tensor<string, []>("concat_22x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_337_cast_fp16 = reshape(shape = concat_22x, x = linear_33_cast_fp16)[name = tensor<string, []>("op_337_cast_fp16")];
+            tensor<int32, [4]> V_11_perm_0 = const()[name = tensor<string, []>("V_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> var_340_transpose_x_0 = const()[name = tensor<string, []>("op_340_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> var_340_transpose_y_0 = const()[name = tensor<string, []>("op_340_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_58_perm_0 = const()[name = tensor<string, []>("transpose_58_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_59_perm_0 = const()[name = tensor<string, []>("transpose_59_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 12, 64, ?]> transpose_59 = transpose(perm = transpose_59_perm_0, x = var_331_cast_fp16)[name = tensor<string, []>("transpose_98")];
+            tensor<fp16, [1, 12, ?, 64]> transpose_58 = transpose(perm = transpose_58_perm_0, x = var_325_cast_fp16)[name = tensor<string, []>("transpose_99")];
+            tensor<fp16, [1, 12, ?, ?]> var_340_cast_fp16 = matmul(transpose_x = var_340_transpose_x_0, transpose_y = var_340_transpose_y_0, x = transpose_58, y = transpose_59)[name = tensor<string, []>("op_340_cast_fp16")];
+            tensor<fp16, []> _inversed_attn_scores_11_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_attn_scores_11_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
+            tensor<fp16, [1, 12, ?, ?]> _inversed_attn_scores_11_cast_fp16 = mul(x = var_340_cast_fp16, y = _inversed_attn_scores_11_y_0_to_fp16)[name = tensor<string, []>("_inversed_attn_scores_11_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> input_69_cast_fp16 = add(x = _inversed_attn_scores_11_cast_fp16, y = attention_mask_cast_fp16)[name = tensor<string, []>("input_69_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> attn_probs_11_cast_fp16 = softmax(axis = var_21, x = input_69_cast_fp16)[name = tensor<string, []>("attn_probs_11_cast_fp16")];
+            tensor<bool, []> context_11_transpose_x_0 = const()[name = tensor<string, []>("context_11_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> context_11_transpose_y_0 = const()[name = tensor<string, []>("context_11_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 12, ?, 64]> V_11_cast_fp16 = transpose(perm = V_11_perm_0, x = var_337_cast_fp16)[name = tensor<string, []>("transpose_97")];
+            tensor<fp16, [1, 12, ?, 64]> context_11_cast_fp16 = matmul(transpose_x = context_11_transpose_x_0, transpose_y = context_11_transpose_y_0, x = attn_probs_11_cast_fp16, y = V_11_cast_fp16)[name = tensor<string, []>("context_11_cast_fp16")];
+            tensor<int32, [4]> var_346_perm_0 = const()[name = tensor<string, []>("op_346_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> concat_23x = const()[name = tensor<string, []>("concat_23x"), val = tensor<int32, [3]>([1, -1, 768])];
+            tensor<fp16, [1, ?, 12, 64]> var_346_cast_fp16 = transpose(perm = var_346_perm_0, x = context_11_cast_fp16)[name = tensor<string, []>("transpose_96")];
+            tensor<fp16, [1, ?, 768]> input_71_cast_fp16 = reshape(shape = concat_23x, x = var_346_cast_fp16)[name = tensor<string, []>("input_71_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_34_cast_fp16 = linear(bias = bert_attn_dense_bias_to_fp16, weight = bert_attn_dense_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = tensor<string, []>("linear_34_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_73_cast_fp16 = add(x = linear_34_cast_fp16, y = hidden_11_cast_fp16)[name = tensor<string, []>("input_73_cast_fp16")];
+            tensor<int32, [1]> input_75_axes_0 = const()[name = tensor<string, []>("input_75_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> input_75_cast_fp16 = layer_norm(axes = input_75_axes_0, beta = bert_attn_LayerNorm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_attn_LayerNorm_weight_to_fp16, x = input_73_cast_fp16)[name = tensor<string, []>("input_75_cast_fp16")];
+            tensor<fp16, [1, ?, 2048]> linear_35_cast_fp16 = linear(bias = bert_ffn_bias_to_fp16, weight = bert_ffn_weight_to_fp16_palettized, x = input_75_cast_fp16)[name = tensor<string, []>("linear_35_cast_fp16")];
+            tensor<string, []> input_77_mode_0 = const()[name = tensor<string, []>("input_77_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
+            tensor<fp16, [1, ?, 2048]> input_77_cast_fp16 = gelu(mode = input_77_mode_0, x = linear_35_cast_fp16)[name = tensor<string, []>("input_77_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_36_cast_fp16 = linear(bias = bert_ffn_output_bias_to_fp16, weight = bert_ffn_output_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = tensor<string, []>("linear_36_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_79_cast_fp16 = add(x = linear_36_cast_fp16, y = input_75_cast_fp16)[name = tensor<string, []>("input_79_cast_fp16")];
+            tensor<int32, [1]> hidden_13_axes_0 = const()[name = tensor<string, []>("hidden_13_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> hidden_13_cast_fp16 = layer_norm(axes = hidden_13_axes_0, beta = bert_full_layer_norm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_full_layer_norm_weight_to_fp16, x = input_79_cast_fp16)[name = tensor<string, []>("hidden_13_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_37_cast_fp16 = linear(bias = bert_query_bias_to_fp16, weight = bert_query_weight_to_fp16_palettized, x = hidden_13_cast_fp16)[name = tensor<string, []>("linear_37_cast_fp16")];
+            tensor<int32, [4]> concat_24x = const()[name = tensor<string, []>("concat_24x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_376_cast_fp16 = reshape(shape = concat_24x, x = linear_37_cast_fp16)[name = tensor<string, []>("op_376_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_38_cast_fp16 = linear(bias = bert_key_bias_to_fp16, weight = bert_key_weight_to_fp16_palettized, x = hidden_13_cast_fp16)[name = tensor<string, []>("linear_38_cast_fp16")];
+            tensor<int32, [4]> concat_25x = const()[name = tensor<string, []>("concat_25x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_382_cast_fp16 = reshape(shape = concat_25x, x = linear_38_cast_fp16)[name = tensor<string, []>("op_382_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_39_cast_fp16 = linear(bias = bert_value_bias_to_fp16, weight = bert_value_weight_to_fp16_palettized, x = hidden_13_cast_fp16)[name = tensor<string, []>("linear_39_cast_fp16")];
+            tensor<int32, [4]> concat_26x = const()[name = tensor<string, []>("concat_26x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_388_cast_fp16 = reshape(shape = concat_26x, x = linear_39_cast_fp16)[name = tensor<string, []>("op_388_cast_fp16")];
+            tensor<int32, [4]> V_13_perm_0 = const()[name = tensor<string, []>("V_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> var_391_transpose_x_0 = const()[name = tensor<string, []>("op_391_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> var_391_transpose_y_0 = const()[name = tensor<string, []>("op_391_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_60_perm_0 = const()[name = tensor<string, []>("transpose_60_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_61_perm_0 = const()[name = tensor<string, []>("transpose_61_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 12, 64, ?]> transpose_61 = transpose(perm = transpose_61_perm_0, x = var_382_cast_fp16)[name = tensor<string, []>("transpose_94")];
+            tensor<fp16, [1, 12, ?, 64]> transpose_60 = transpose(perm = transpose_60_perm_0, x = var_376_cast_fp16)[name = tensor<string, []>("transpose_95")];
+            tensor<fp16, [1, 12, ?, ?]> var_391_cast_fp16 = matmul(transpose_x = var_391_transpose_x_0, transpose_y = var_391_transpose_y_0, x = transpose_60, y = transpose_61)[name = tensor<string, []>("op_391_cast_fp16")];
+            tensor<fp16, []> _inversed_attn_scores_13_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_attn_scores_13_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
+            tensor<fp16, [1, 12, ?, ?]> _inversed_attn_scores_13_cast_fp16 = mul(x = var_391_cast_fp16, y = _inversed_attn_scores_13_y_0_to_fp16)[name = tensor<string, []>("_inversed_attn_scores_13_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> input_81_cast_fp16 = add(x = _inversed_attn_scores_13_cast_fp16, y = attention_mask_cast_fp16)[name = tensor<string, []>("input_81_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> attn_probs_13_cast_fp16 = softmax(axis = var_21, x = input_81_cast_fp16)[name = tensor<string, []>("attn_probs_13_cast_fp16")];
+            tensor<bool, []> context_13_transpose_x_0 = const()[name = tensor<string, []>("context_13_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> context_13_transpose_y_0 = const()[name = tensor<string, []>("context_13_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 12, ?, 64]> V_13_cast_fp16 = transpose(perm = V_13_perm_0, x = var_388_cast_fp16)[name = tensor<string, []>("transpose_93")];
+            tensor<fp16, [1, 12, ?, 64]> context_13_cast_fp16 = matmul(transpose_x = context_13_transpose_x_0, transpose_y = context_13_transpose_y_0, x = attn_probs_13_cast_fp16, y = V_13_cast_fp16)[name = tensor<string, []>("context_13_cast_fp16")];
+            tensor<int32, [4]> var_397_perm_0 = const()[name = tensor<string, []>("op_397_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> concat_27x = const()[name = tensor<string, []>("concat_27x"), val = tensor<int32, [3]>([1, -1, 768])];
+            tensor<fp16, [1, ?, 12, 64]> var_397_cast_fp16 = transpose(perm = var_397_perm_0, x = context_13_cast_fp16)[name = tensor<string, []>("transpose_92")];
+            tensor<fp16, [1, ?, 768]> input_83_cast_fp16 = reshape(shape = concat_27x, x = var_397_cast_fp16)[name = tensor<string, []>("input_83_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_40_cast_fp16 = linear(bias = bert_attn_dense_bias_to_fp16, weight = bert_attn_dense_weight_to_fp16_palettized, x = input_83_cast_fp16)[name = tensor<string, []>("linear_40_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_85_cast_fp16 = add(x = linear_40_cast_fp16, y = hidden_13_cast_fp16)[name = tensor<string, []>("input_85_cast_fp16")];
+            tensor<int32, [1]> input_87_axes_0 = const()[name = tensor<string, []>("input_87_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> input_87_cast_fp16 = layer_norm(axes = input_87_axes_0, beta = bert_attn_LayerNorm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_attn_LayerNorm_weight_to_fp16, x = input_85_cast_fp16)[name = tensor<string, []>("input_87_cast_fp16")];
+            tensor<fp16, [1, ?, 2048]> linear_41_cast_fp16 = linear(bias = bert_ffn_bias_to_fp16, weight = bert_ffn_weight_to_fp16_palettized, x = input_87_cast_fp16)[name = tensor<string, []>("linear_41_cast_fp16")];
+            tensor<string, []> input_89_mode_0 = const()[name = tensor<string, []>("input_89_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
+            tensor<fp16, [1, ?, 2048]> input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_41_cast_fp16)[name = tensor<string, []>("input_89_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_42_cast_fp16 = linear(bias = bert_ffn_output_bias_to_fp16, weight = bert_ffn_output_weight_to_fp16_palettized, x = input_89_cast_fp16)[name = tensor<string, []>("linear_42_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_91_cast_fp16 = add(x = linear_42_cast_fp16, y = input_87_cast_fp16)[name = tensor<string, []>("input_91_cast_fp16")];
+            tensor<int32, [1]> hidden_15_axes_0 = const()[name = tensor<string, []>("hidden_15_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> hidden_15_cast_fp16 = layer_norm(axes = hidden_15_axes_0, beta = bert_full_layer_norm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_full_layer_norm_weight_to_fp16, x = input_91_cast_fp16)[name = tensor<string, []>("hidden_15_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_43_cast_fp16 = linear(bias = bert_query_bias_to_fp16, weight = bert_query_weight_to_fp16_palettized, x = hidden_15_cast_fp16)[name = tensor<string, []>("linear_43_cast_fp16")];
+            tensor<int32, [4]> concat_28x = const()[name = tensor<string, []>("concat_28x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_427_cast_fp16 = reshape(shape = concat_28x, x = linear_43_cast_fp16)[name = tensor<string, []>("op_427_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_44_cast_fp16 = linear(bias = bert_key_bias_to_fp16, weight = bert_key_weight_to_fp16_palettized, x = hidden_15_cast_fp16)[name = tensor<string, []>("linear_44_cast_fp16")];
+            tensor<int32, [4]> concat_29x = const()[name = tensor<string, []>("concat_29x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_433_cast_fp16 = reshape(shape = concat_29x, x = linear_44_cast_fp16)[name = tensor<string, []>("op_433_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_45_cast_fp16 = linear(bias = bert_value_bias_to_fp16, weight = bert_value_weight_to_fp16_palettized, x = hidden_15_cast_fp16)[name = tensor<string, []>("linear_45_cast_fp16")];
+            tensor<int32, [4]> concat_30x = const()[name = tensor<string, []>("concat_30x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_439_cast_fp16 = reshape(shape = concat_30x, x = linear_45_cast_fp16)[name = tensor<string, []>("op_439_cast_fp16")];
+            tensor<int32, [4]> V_15_perm_0 = const()[name = tensor<string, []>("V_15_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> var_442_transpose_x_0 = const()[name = tensor<string, []>("op_442_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> var_442_transpose_y_0 = const()[name = tensor<string, []>("op_442_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_62_perm_0 = const()[name = tensor<string, []>("transpose_62_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_63_perm_0 = const()[name = tensor<string, []>("transpose_63_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 12, 64, ?]> transpose_63 = transpose(perm = transpose_63_perm_0, x = var_433_cast_fp16)[name = tensor<string, []>("transpose_90")];
+            tensor<fp16, [1, 12, ?, 64]> transpose_62 = transpose(perm = transpose_62_perm_0, x = var_427_cast_fp16)[name = tensor<string, []>("transpose_91")];
+            tensor<fp16, [1, 12, ?, ?]> var_442_cast_fp16 = matmul(transpose_x = var_442_transpose_x_0, transpose_y = var_442_transpose_y_0, x = transpose_62, y = transpose_63)[name = tensor<string, []>("op_442_cast_fp16")];
+            tensor<fp16, []> _inversed_attn_scores_15_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_attn_scores_15_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
+            tensor<fp16, [1, 12, ?, ?]> _inversed_attn_scores_15_cast_fp16 = mul(x = var_442_cast_fp16, y = _inversed_attn_scores_15_y_0_to_fp16)[name = tensor<string, []>("_inversed_attn_scores_15_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> input_93_cast_fp16 = add(x = _inversed_attn_scores_15_cast_fp16, y = attention_mask_cast_fp16)[name = tensor<string, []>("input_93_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> attn_probs_15_cast_fp16 = softmax(axis = var_21, x = input_93_cast_fp16)[name = tensor<string, []>("attn_probs_15_cast_fp16")];
+            tensor<bool, []> context_15_transpose_x_0 = const()[name = tensor<string, []>("context_15_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> context_15_transpose_y_0 = const()[name = tensor<string, []>("context_15_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 12, ?, 64]> V_15_cast_fp16 = transpose(perm = V_15_perm_0, x = var_439_cast_fp16)[name = tensor<string, []>("transpose_89")];
+            tensor<fp16, [1, 12, ?, 64]> context_15_cast_fp16 = matmul(transpose_x = context_15_transpose_x_0, transpose_y = context_15_transpose_y_0, x = attn_probs_15_cast_fp16, y = V_15_cast_fp16)[name = tensor<string, []>("context_15_cast_fp16")];
+            tensor<int32, [4]> var_448_perm_0 = const()[name = tensor<string, []>("op_448_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> concat_31x = const()[name = tensor<string, []>("concat_31x"), val = tensor<int32, [3]>([1, -1, 768])];
+            tensor<fp16, [1, ?, 12, 64]> var_448_cast_fp16 = transpose(perm = var_448_perm_0, x = context_15_cast_fp16)[name = tensor<string, []>("transpose_88")];
+            tensor<fp16, [1, ?, 768]> input_95_cast_fp16 = reshape(shape = concat_31x, x = var_448_cast_fp16)[name = tensor<string, []>("input_95_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_46_cast_fp16 = linear(bias = bert_attn_dense_bias_to_fp16, weight = bert_attn_dense_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = tensor<string, []>("linear_46_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_97_cast_fp16 = add(x = linear_46_cast_fp16, y = hidden_15_cast_fp16)[name = tensor<string, []>("input_97_cast_fp16")];
+            tensor<int32, [1]> input_99_axes_0 = const()[name = tensor<string, []>("input_99_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> input_99_cast_fp16 = layer_norm(axes = input_99_axes_0, beta = bert_attn_LayerNorm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_attn_LayerNorm_weight_to_fp16, x = input_97_cast_fp16)[name = tensor<string, []>("input_99_cast_fp16")];
+            tensor<fp16, [1, ?, 2048]> linear_47_cast_fp16 = linear(bias = bert_ffn_bias_to_fp16, weight = bert_ffn_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = tensor<string, []>("linear_47_cast_fp16")];
+            tensor<string, []> input_101_mode_0 = const()[name = tensor<string, []>("input_101_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
+            tensor<fp16, [1, ?, 2048]> input_101_cast_fp16 = gelu(mode = input_101_mode_0, x = linear_47_cast_fp16)[name = tensor<string, []>("input_101_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_48_cast_fp16 = linear(bias = bert_ffn_output_bias_to_fp16, weight = bert_ffn_output_weight_to_fp16_palettized, x = input_101_cast_fp16)[name = tensor<string, []>("linear_48_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_103_cast_fp16 = add(x = linear_48_cast_fp16, y = input_99_cast_fp16)[name = tensor<string, []>("input_103_cast_fp16")];
+            tensor<int32, [1]> hidden_17_axes_0 = const()[name = tensor<string, []>("hidden_17_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> hidden_17_cast_fp16 = layer_norm(axes = hidden_17_axes_0, beta = bert_full_layer_norm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_full_layer_norm_weight_to_fp16, x = input_103_cast_fp16)[name = tensor<string, []>("hidden_17_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_49_cast_fp16 = linear(bias = bert_query_bias_to_fp16, weight = bert_query_weight_to_fp16_palettized, x = hidden_17_cast_fp16)[name = tensor<string, []>("linear_49_cast_fp16")];
+            tensor<int32, [4]> concat_32x = const()[name = tensor<string, []>("concat_32x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_478_cast_fp16 = reshape(shape = concat_32x, x = linear_49_cast_fp16)[name = tensor<string, []>("op_478_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_50_cast_fp16 = linear(bias = bert_key_bias_to_fp16, weight = bert_key_weight_to_fp16_palettized, x = hidden_17_cast_fp16)[name = tensor<string, []>("linear_50_cast_fp16")];
+            tensor<int32, [4]> concat_33x = const()[name = tensor<string, []>("concat_33x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_484_cast_fp16 = reshape(shape = concat_33x, x = linear_50_cast_fp16)[name = tensor<string, []>("op_484_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_51_cast_fp16 = linear(bias = bert_value_bias_to_fp16, weight = bert_value_weight_to_fp16_palettized, x = hidden_17_cast_fp16)[name = tensor<string, []>("linear_51_cast_fp16")];
+            tensor<int32, [4]> concat_34x = const()[name = tensor<string, []>("concat_34x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_490_cast_fp16 = reshape(shape = concat_34x, x = linear_51_cast_fp16)[name = tensor<string, []>("op_490_cast_fp16")];
+            tensor<int32, [4]> V_17_perm_0 = const()[name = tensor<string, []>("V_17_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> var_493_transpose_x_0 = const()[name = tensor<string, []>("op_493_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> var_493_transpose_y_0 = const()[name = tensor<string, []>("op_493_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_64_perm_0 = const()[name = tensor<string, []>("transpose_64_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_65_perm_0 = const()[name = tensor<string, []>("transpose_65_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 12, 64, ?]> transpose_65 = transpose(perm = transpose_65_perm_0, x = var_484_cast_fp16)[name = tensor<string, []>("transpose_86")];
+            tensor<fp16, [1, 12, ?, 64]> transpose_64 = transpose(perm = transpose_64_perm_0, x = var_478_cast_fp16)[name = tensor<string, []>("transpose_87")];
+            tensor<fp16, [1, 12, ?, ?]> var_493_cast_fp16 = matmul(transpose_x = var_493_transpose_x_0, transpose_y = var_493_transpose_y_0, x = transpose_64, y = transpose_65)[name = tensor<string, []>("op_493_cast_fp16")];
+            tensor<fp16, []> _inversed_attn_scores_17_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_attn_scores_17_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
+            tensor<fp16, [1, 12, ?, ?]> _inversed_attn_scores_17_cast_fp16 = mul(x = var_493_cast_fp16, y = _inversed_attn_scores_17_y_0_to_fp16)[name = tensor<string, []>("_inversed_attn_scores_17_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> input_105_cast_fp16 = add(x = _inversed_attn_scores_17_cast_fp16, y = attention_mask_cast_fp16)[name = tensor<string, []>("input_105_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> attn_probs_17_cast_fp16 = softmax(axis = var_21, x = input_105_cast_fp16)[name = tensor<string, []>("attn_probs_17_cast_fp16")];
+            tensor<bool, []> context_17_transpose_x_0 = const()[name = tensor<string, []>("context_17_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> context_17_transpose_y_0 = const()[name = tensor<string, []>("context_17_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 12, ?, 64]> V_17_cast_fp16 = transpose(perm = V_17_perm_0, x = var_490_cast_fp16)[name = tensor<string, []>("transpose_85")];
+            tensor<fp16, [1, 12, ?, 64]> context_17_cast_fp16 = matmul(transpose_x = context_17_transpose_x_0, transpose_y = context_17_transpose_y_0, x = attn_probs_17_cast_fp16, y = V_17_cast_fp16)[name = tensor<string, []>("context_17_cast_fp16")];
+            tensor<int32, [4]> var_499_perm_0 = const()[name = tensor<string, []>("op_499_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> concat_35x = const()[name = tensor<string, []>("concat_35x"), val = tensor<int32, [3]>([1, -1, 768])];
+            tensor<fp16, [1, ?, 12, 64]> var_499_cast_fp16 = transpose(perm = var_499_perm_0, x = context_17_cast_fp16)[name = tensor<string, []>("transpose_84")];
+            tensor<fp16, [1, ?, 768]> input_107_cast_fp16 = reshape(shape = concat_35x, x = var_499_cast_fp16)[name = tensor<string, []>("input_107_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_52_cast_fp16 = linear(bias = bert_attn_dense_bias_to_fp16, weight = bert_attn_dense_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = tensor<string, []>("linear_52_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_109_cast_fp16 = add(x = linear_52_cast_fp16, y = hidden_17_cast_fp16)[name = tensor<string, []>("input_109_cast_fp16")];
+            tensor<int32, [1]> input_111_axes_0 = const()[name = tensor<string, []>("input_111_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> input_111_cast_fp16 = layer_norm(axes = input_111_axes_0, beta = bert_attn_LayerNorm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_attn_LayerNorm_weight_to_fp16, x = input_109_cast_fp16)[name = tensor<string, []>("input_111_cast_fp16")];
+            tensor<fp16, [1, ?, 2048]> linear_53_cast_fp16 = linear(bias = bert_ffn_bias_to_fp16, weight = bert_ffn_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = tensor<string, []>("linear_53_cast_fp16")];
+            tensor<string, []> input_113_mode_0 = const()[name = tensor<string, []>("input_113_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
+            tensor<fp16, [1, ?, 2048]> input_113_cast_fp16 = gelu(mode = input_113_mode_0, x = linear_53_cast_fp16)[name = tensor<string, []>("input_113_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_54_cast_fp16 = linear(bias = bert_ffn_output_bias_to_fp16, weight = bert_ffn_output_weight_to_fp16_palettized, x = input_113_cast_fp16)[name = tensor<string, []>("linear_54_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_115_cast_fp16 = add(x = linear_54_cast_fp16, y = input_111_cast_fp16)[name = tensor<string, []>("input_115_cast_fp16")];
+            tensor<int32, [1]> hidden_19_axes_0 = const()[name = tensor<string, []>("hidden_19_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> hidden_19_cast_fp16 = layer_norm(axes = hidden_19_axes_0, beta = bert_full_layer_norm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_full_layer_norm_weight_to_fp16, x = input_115_cast_fp16)[name = tensor<string, []>("hidden_19_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_55_cast_fp16 = linear(bias = bert_query_bias_to_fp16, weight = bert_query_weight_to_fp16_palettized, x = hidden_19_cast_fp16)[name = tensor<string, []>("linear_55_cast_fp16")];
+            tensor<int32, [4]> concat_36x = const()[name = tensor<string, []>("concat_36x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_529_cast_fp16 = reshape(shape = concat_36x, x = linear_55_cast_fp16)[name = tensor<string, []>("op_529_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_56_cast_fp16 = linear(bias = bert_key_bias_to_fp16, weight = bert_key_weight_to_fp16_palettized, x = hidden_19_cast_fp16)[name = tensor<string, []>("linear_56_cast_fp16")];
+            tensor<int32, [4]> concat_37x = const()[name = tensor<string, []>("concat_37x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_535_cast_fp16 = reshape(shape = concat_37x, x = linear_56_cast_fp16)[name = tensor<string, []>("op_535_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_57_cast_fp16 = linear(bias = bert_value_bias_to_fp16, weight = bert_value_weight_to_fp16_palettized, x = hidden_19_cast_fp16)[name = tensor<string, []>("linear_57_cast_fp16")];
+            tensor<int32, [4]> concat_38x = const()[name = tensor<string, []>("concat_38x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_541_cast_fp16 = reshape(shape = concat_38x, x = linear_57_cast_fp16)[name = tensor<string, []>("op_541_cast_fp16")];
+            tensor<int32, [4]> V_19_perm_0 = const()[name = tensor<string, []>("V_19_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> var_544_transpose_x_0 = const()[name = tensor<string, []>("op_544_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> var_544_transpose_y_0 = const()[name = tensor<string, []>("op_544_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_66_perm_0 = const()[name = tensor<string, []>("transpose_66_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_67_perm_0 = const()[name = tensor<string, []>("transpose_67_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 12, 64, ?]> transpose_67 = transpose(perm = transpose_67_perm_0, x = var_535_cast_fp16)[name = tensor<string, []>("transpose_82")];
+            tensor<fp16, [1, 12, ?, 64]> transpose_66 = transpose(perm = transpose_66_perm_0, x = var_529_cast_fp16)[name = tensor<string, []>("transpose_83")];
+            tensor<fp16, [1, 12, ?, ?]> var_544_cast_fp16 = matmul(transpose_x = var_544_transpose_x_0, transpose_y = var_544_transpose_y_0, x = transpose_66, y = transpose_67)[name = tensor<string, []>("op_544_cast_fp16")];
+            tensor<fp16, []> _inversed_attn_scores_19_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_attn_scores_19_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
+            tensor<fp16, [1, 12, ?, ?]> _inversed_attn_scores_19_cast_fp16 = mul(x = var_544_cast_fp16, y = _inversed_attn_scores_19_y_0_to_fp16)[name = tensor<string, []>("_inversed_attn_scores_19_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> input_117_cast_fp16 = add(x = _inversed_attn_scores_19_cast_fp16, y = attention_mask_cast_fp16)[name = tensor<string, []>("input_117_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> attn_probs_19_cast_fp16 = softmax(axis = var_21, x = input_117_cast_fp16)[name = tensor<string, []>("attn_probs_19_cast_fp16")];
+            tensor<bool, []> context_19_transpose_x_0 = const()[name = tensor<string, []>("context_19_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> context_19_transpose_y_0 = const()[name = tensor<string, []>("context_19_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 12, ?, 64]> V_19_cast_fp16 = transpose(perm = V_19_perm_0, x = var_541_cast_fp16)[name = tensor<string, []>("transpose_81")];
+            tensor<fp16, [1, 12, ?, 64]> context_19_cast_fp16 = matmul(transpose_x = context_19_transpose_x_0, transpose_y = context_19_transpose_y_0, x = attn_probs_19_cast_fp16, y = V_19_cast_fp16)[name = tensor<string, []>("context_19_cast_fp16")];
+            tensor<int32, [4]> var_550_perm_0 = const()[name = tensor<string, []>("op_550_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> concat_39x = const()[name = tensor<string, []>("concat_39x"), val = tensor<int32, [3]>([1, -1, 768])];
+            tensor<fp16, [1, ?, 12, 64]> var_550_cast_fp16 = transpose(perm = var_550_perm_0, x = context_19_cast_fp16)[name = tensor<string, []>("transpose_80")];
+            tensor<fp16, [1, ?, 768]> input_119_cast_fp16 = reshape(shape = concat_39x, x = var_550_cast_fp16)[name = tensor<string, []>("input_119_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_58_cast_fp16 = linear(bias = bert_attn_dense_bias_to_fp16, weight = bert_attn_dense_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = tensor<string, []>("linear_58_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_121_cast_fp16 = add(x = linear_58_cast_fp16, y = hidden_19_cast_fp16)[name = tensor<string, []>("input_121_cast_fp16")];
+            tensor<int32, [1]> input_123_axes_0 = const()[name = tensor<string, []>("input_123_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> input_123_cast_fp16 = layer_norm(axes = input_123_axes_0, beta = bert_attn_LayerNorm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_attn_LayerNorm_weight_to_fp16, x = input_121_cast_fp16)[name = tensor<string, []>("input_123_cast_fp16")];
+            tensor<fp16, [1, ?, 2048]> linear_59_cast_fp16 = linear(bias = bert_ffn_bias_to_fp16, weight = bert_ffn_weight_to_fp16_palettized, x = input_123_cast_fp16)[name = tensor<string, []>("linear_59_cast_fp16")];
+            tensor<string, []> input_125_mode_0 = const()[name = tensor<string, []>("input_125_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
+            tensor<fp16, [1, ?, 2048]> input_125_cast_fp16 = gelu(mode = input_125_mode_0, x = linear_59_cast_fp16)[name = tensor<string, []>("input_125_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_60_cast_fp16 = linear(bias = bert_ffn_output_bias_to_fp16, weight = bert_ffn_output_weight_to_fp16_palettized, x = input_125_cast_fp16)[name = tensor<string, []>("linear_60_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_127_cast_fp16 = add(x = linear_60_cast_fp16, y = input_123_cast_fp16)[name = tensor<string, []>("input_127_cast_fp16")];
+            tensor<int32, [1]> hidden_21_axes_0 = const()[name = tensor<string, []>("hidden_21_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> hidden_21_cast_fp16 = layer_norm(axes = hidden_21_axes_0, beta = bert_full_layer_norm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_full_layer_norm_weight_to_fp16, x = input_127_cast_fp16)[name = tensor<string, []>("hidden_21_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_61_cast_fp16 = linear(bias = bert_query_bias_to_fp16, weight = bert_query_weight_to_fp16_palettized, x = hidden_21_cast_fp16)[name = tensor<string, []>("linear_61_cast_fp16")];
+            tensor<int32, [4]> concat_40x = const()[name = tensor<string, []>("concat_40x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_580_cast_fp16 = reshape(shape = concat_40x, x = linear_61_cast_fp16)[name = tensor<string, []>("op_580_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_62_cast_fp16 = linear(bias = bert_key_bias_to_fp16, weight = bert_key_weight_to_fp16_palettized, x = hidden_21_cast_fp16)[name = tensor<string, []>("linear_62_cast_fp16")];
+            tensor<int32, [4]> concat_41x = const()[name = tensor<string, []>("concat_41x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_586_cast_fp16 = reshape(shape = concat_41x, x = linear_62_cast_fp16)[name = tensor<string, []>("op_586_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_63_cast_fp16 = linear(bias = bert_value_bias_to_fp16, weight = bert_value_weight_to_fp16_palettized, x = hidden_21_cast_fp16)[name = tensor<string, []>("linear_63_cast_fp16")];
+            tensor<int32, [4]> concat_42x = const()[name = tensor<string, []>("concat_42x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_592_cast_fp16 = reshape(shape = concat_42x, x = linear_63_cast_fp16)[name = tensor<string, []>("op_592_cast_fp16")];
+            tensor<int32, [4]> V_21_perm_0 = const()[name = tensor<string, []>("V_21_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> var_595_transpose_x_0 = const()[name = tensor<string, []>("op_595_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> var_595_transpose_y_0 = const()[name = tensor<string, []>("op_595_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_68_perm_0 = const()[name = tensor<string, []>("transpose_68_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_69_perm_0 = const()[name = tensor<string, []>("transpose_69_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 12, 64, ?]> transpose_69 = transpose(perm = transpose_69_perm_0, x = var_586_cast_fp16)[name = tensor<string, []>("transpose_78")];
+            tensor<fp16, [1, 12, ?, 64]> transpose_68 = transpose(perm = transpose_68_perm_0, x = var_580_cast_fp16)[name = tensor<string, []>("transpose_79")];
+            tensor<fp16, [1, 12, ?, ?]> var_595_cast_fp16 = matmul(transpose_x = var_595_transpose_x_0, transpose_y = var_595_transpose_y_0, x = transpose_68, y = transpose_69)[name = tensor<string, []>("op_595_cast_fp16")];
+            tensor<fp16, []> _inversed_attn_scores_21_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_attn_scores_21_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
+            tensor<fp16, [1, 12, ?, ?]> _inversed_attn_scores_21_cast_fp16 = mul(x = var_595_cast_fp16, y = _inversed_attn_scores_21_y_0_to_fp16)[name = tensor<string, []>("_inversed_attn_scores_21_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> input_129_cast_fp16 = add(x = _inversed_attn_scores_21_cast_fp16, y = attention_mask_cast_fp16)[name = tensor<string, []>("input_129_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> attn_probs_21_cast_fp16 = softmax(axis = var_21, x = input_129_cast_fp16)[name = tensor<string, []>("attn_probs_21_cast_fp16")];
+            tensor<bool, []> context_21_transpose_x_0 = const()[name = tensor<string, []>("context_21_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> context_21_transpose_y_0 = const()[name = tensor<string, []>("context_21_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 12, ?, 64]> V_21_cast_fp16 = transpose(perm = V_21_perm_0, x = var_592_cast_fp16)[name = tensor<string, []>("transpose_77")];
+            tensor<fp16, [1, 12, ?, 64]> context_21_cast_fp16 = matmul(transpose_x = context_21_transpose_x_0, transpose_y = context_21_transpose_y_0, x = attn_probs_21_cast_fp16, y = V_21_cast_fp16)[name = tensor<string, []>("context_21_cast_fp16")];
+            tensor<int32, [4]> var_601_perm_0 = const()[name = tensor<string, []>("op_601_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> concat_43x = const()[name = tensor<string, []>("concat_43x"), val = tensor<int32, [3]>([1, -1, 768])];
+            tensor<fp16, [1, ?, 12, 64]> var_601_cast_fp16 = transpose(perm = var_601_perm_0, x = context_21_cast_fp16)[name = tensor<string, []>("transpose_76")];
+            tensor<fp16, [1, ?, 768]> input_131_cast_fp16 = reshape(shape = concat_43x, x = var_601_cast_fp16)[name = tensor<string, []>("input_131_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_64_cast_fp16 = linear(bias = bert_attn_dense_bias_to_fp16, weight = bert_attn_dense_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = tensor<string, []>("linear_64_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_133_cast_fp16 = add(x = linear_64_cast_fp16, y = hidden_21_cast_fp16)[name = tensor<string, []>("input_133_cast_fp16")];
+            tensor<int32, [1]> input_135_axes_0 = const()[name = tensor<string, []>("input_135_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> input_135_cast_fp16 = layer_norm(axes = input_135_axes_0, beta = bert_attn_LayerNorm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_attn_LayerNorm_weight_to_fp16, x = input_133_cast_fp16)[name = tensor<string, []>("input_135_cast_fp16")];
+            tensor<fp16, [1, ?, 2048]> linear_65_cast_fp16 = linear(bias = bert_ffn_bias_to_fp16, weight = bert_ffn_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = tensor<string, []>("linear_65_cast_fp16")];
+            tensor<string, []> input_137_mode_0 = const()[name = tensor<string, []>("input_137_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
+            tensor<fp16, [1, ?, 2048]> input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = linear_65_cast_fp16)[name = tensor<string, []>("input_137_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_66_cast_fp16 = linear(bias = bert_ffn_output_bias_to_fp16, weight = bert_ffn_output_weight_to_fp16_palettized, x = input_137_cast_fp16)[name = tensor<string, []>("linear_66_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_139_cast_fp16 = add(x = linear_66_cast_fp16, y = input_135_cast_fp16)[name = tensor<string, []>("input_139_cast_fp16")];
+            tensor<int32, [1]> hidden_axes_0 = const()[name = tensor<string, []>("hidden_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> hidden_cast_fp16 = layer_norm(axes = hidden_axes_0, beta = bert_full_layer_norm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_full_layer_norm_weight_to_fp16, x = input_139_cast_fp16)[name = tensor<string, []>("hidden_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_67_cast_fp16 = linear(bias = bert_query_bias_to_fp16, weight = bert_query_weight_to_fp16_palettized, x = hidden_cast_fp16)[name = tensor<string, []>("linear_67_cast_fp16")];
+            tensor<int32, [4]> concat_44x = const()[name = tensor<string, []>("concat_44x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_631_cast_fp16 = reshape(shape = concat_44x, x = linear_67_cast_fp16)[name = tensor<string, []>("op_631_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_68_cast_fp16 = linear(bias = bert_key_bias_to_fp16, weight = bert_key_weight_to_fp16_palettized, x = hidden_cast_fp16)[name = tensor<string, []>("linear_68_cast_fp16")];
+            tensor<int32, [4]> concat_45x = const()[name = tensor<string, []>("concat_45x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_637_cast_fp16 = reshape(shape = concat_45x, x = linear_68_cast_fp16)[name = tensor<string, []>("op_637_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_69_cast_fp16 = linear(bias = bert_value_bias_to_fp16, weight = bert_value_weight_to_fp16_palettized, x = hidden_cast_fp16)[name = tensor<string, []>("linear_69_cast_fp16")];
+            tensor<int32, [4]> concat_46x = const()[name = tensor<string, []>("concat_46x"), val = tensor<int32, [4]>([1, -1, 12, 64])];
+            tensor<fp16, [1, ?, 12, 64]> var_643_cast_fp16 = reshape(shape = concat_46x, x = linear_69_cast_fp16)[name = tensor<string, []>("op_643_cast_fp16")];
+            tensor<int32, [4]> V_perm_0 = const()[name = tensor<string, []>("V_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<bool, []> var_646_transpose_x_0 = const()[name = tensor<string, []>("op_646_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> var_646_transpose_y_0 = const()[name = tensor<string, []>("op_646_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [4]> transpose_70_perm_0 = const()[name = tensor<string, []>("transpose_70_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
+            tensor<int32, [4]> transpose_71_perm_0 = const()[name = tensor<string, []>("transpose_71_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
+            tensor<fp16, [1, 12, 64, ?]> transpose_71 = transpose(perm = transpose_71_perm_0, x = var_637_cast_fp16)[name = tensor<string, []>("transpose_74")];
+            tensor<fp16, [1, 12, ?, 64]> transpose_70 = transpose(perm = transpose_70_perm_0, x = var_631_cast_fp16)[name = tensor<string, []>("transpose_75")];
+            tensor<fp16, [1, 12, ?, ?]> var_646_cast_fp16 = matmul(transpose_x = var_646_transpose_x_0, transpose_y = var_646_transpose_y_0, x = transpose_70, y = transpose_71)[name = tensor<string, []>("op_646_cast_fp16")];
+            tensor<fp16, []> _inversed_attn_scores_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_attn_scores_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
+            tensor<fp16, [1, 12, ?, ?]> _inversed_attn_scores_cast_fp16 = mul(x = var_646_cast_fp16, y = _inversed_attn_scores_y_0_to_fp16)[name = tensor<string, []>("_inversed_attn_scores_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> input_141_cast_fp16 = add(x = _inversed_attn_scores_cast_fp16, y = attention_mask_cast_fp16)[name = tensor<string, []>("input_141_cast_fp16")];
+            tensor<fp16, [1, 12, ?, ?]> attn_probs_cast_fp16 = softmax(axis = var_21, x = input_141_cast_fp16)[name = tensor<string, []>("attn_probs_cast_fp16")];
+            tensor<bool, []> context_transpose_x_0 = const()[name = tensor<string, []>("context_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> context_transpose_y_0 = const()[name = tensor<string, []>("context_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 12, ?, 64]> V_cast_fp16 = transpose(perm = V_perm_0, x = var_643_cast_fp16)[name = tensor<string, []>("transpose_73")];
+            tensor<fp16, [1, 12, ?, 64]> context_cast_fp16 = matmul(transpose_x = context_transpose_x_0, transpose_y = context_transpose_y_0, x = attn_probs_cast_fp16, y = V_cast_fp16)[name = tensor<string, []>("context_cast_fp16")];
+            tensor<int32, [4]> var_652_perm_0 = const()[name = tensor<string, []>("op_652_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
+            tensor<int32, [3]> concat_47x = const()[name = tensor<string, []>("concat_47x"), val = tensor<int32, [3]>([1, -1, 768])];
+            tensor<fp16, [1, ?, 12, 64]> var_652_cast_fp16 = transpose(perm = var_652_perm_0, x = context_cast_fp16)[name = tensor<string, []>("transpose_72")];
+            tensor<fp16, [1, ?, 768]> input_143_cast_fp16 = reshape(shape = concat_47x, x = var_652_cast_fp16)[name = tensor<string, []>("input_143_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_70_cast_fp16 = linear(bias = bert_attn_dense_bias_to_fp16, weight = bert_attn_dense_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = tensor<string, []>("linear_70_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_145_cast_fp16 = add(x = linear_70_cast_fp16, y = hidden_cast_fp16)[name = tensor<string, []>("input_145_cast_fp16")];
+            tensor<int32, [1]> input_147_axes_0 = const()[name = tensor<string, []>("input_147_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> input_147_cast_fp16 = layer_norm(axes = input_147_axes_0, beta = bert_attn_LayerNorm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_attn_LayerNorm_weight_to_fp16, x = input_145_cast_fp16)[name = tensor<string, []>("input_147_cast_fp16")];
+            tensor<fp16, [1, ?, 2048]> linear_71_cast_fp16 = linear(bias = bert_ffn_bias_to_fp16, weight = bert_ffn_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = tensor<string, []>("linear_71_cast_fp16")];
+            tensor<string, []> input_149_mode_0 = const()[name = tensor<string, []>("input_149_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")];
+            tensor<fp16, [1, ?, 2048]> input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_71_cast_fp16)[name = tensor<string, []>("input_149_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> linear_72_cast_fp16 = linear(bias = bert_ffn_output_bias_to_fp16, weight = bert_ffn_output_weight_to_fp16_palettized, x = input_149_cast_fp16)[name = tensor<string, []>("linear_72_cast_fp16")];
+            tensor<fp16, [1, ?, 768]> input_cast_fp16 = add(x = linear_72_cast_fp16, y = input_147_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
+            tensor<int32, [1]> var_675_axes_0 = const()[name = tensor<string, []>("op_675_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 768]> bert_dur = layer_norm(axes = var_675_axes_0, beta = bert_full_layer_norm_bias_to_fp16, epsilon = var_19_to_fp16, gamma = bert_full_layer_norm_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("op_675_cast_fp16")];
+        } -> (bert_dur);
+}
\ No newline at end of file
diff --git a/ANE/ANE-zh/KokoroAlbert.mlmodelc/weights/weight.bin b/ANE/ANE-zh/KokoroAlbert.mlmodelc/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..d640b41d5feef3f95fde274685036e57c23cb95c
--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlbert.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8c3d8458f357f2e60380de284139af566c6c22e3719bb985524616d0a4f57e5b
+size 5718848
diff --git a/ANE/ANE-zh/KokoroAlbert.mlpackage/Data/com.apple.CoreML/model.mlmodel b/ANE/ANE-zh/KokoroAlbert.mlpackage/Data/com.apple.CoreML/model.mlmodel
new file mode 100644
index 0000000000000000000000000000000000000000..e800772d2cb663e4933eecf511d0bb6adfaeded4
--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlbert.mlpackage/Data/com.apple.CoreML/model.mlmodel
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7871b55a006dd65f8531edbbd48acefe4f554bad1182b852630641c3dfde2d79
+size 92156
diff --git a/ANE/ANE-zh/KokoroAlbert.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/ANE/ANE-zh/KokoroAlbert.mlpackage/Data/com.apple.CoreML/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..d640b41d5feef3f95fde274685036e57c23cb95c
--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlbert.mlpackage/Data/com.apple.CoreML/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8c3d8458f357f2e60380de284139af566c6c22e3719bb985524616d0a4f57e5b
+size 5718848
diff --git a/ANE/ANE-zh/KokoroAlbert.mlpackage/Manifest.json b/ANE/ANE-zh/KokoroAlbert.mlpackage/Manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..c78fe08ab6416dc6e7f961d39ed14598649c800c
--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlbert.mlpackage/Manifest.json
@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "9FF918C7-3A11-4CA4-8998-080F81BAF843": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "EFC4F885-8922-4367-A207-C478D0734CCA": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "EFC4F885-8922-4367-A207-C478D0734CCA"
+}
diff --git a/ANE/ANE-zh/KokoroAlignment.mlmodelc/analytics/coremldata.bin b/ANE/ANE-zh/KokoroAlignment.mlmodelc/analytics/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..2addc577ba1d39f762bc83caaa38604e761be5f3
--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlignment.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7c84c8820e2e6f1b27229db6ce4ec2cc2817c546aa31d2d2f677d35be5445443
+size 243
diff --git a/ANE/ANE-zh/KokoroAlignment.mlmodelc/coremldata.bin b/ANE/ANE-zh/KokoroAlignment.mlmodelc/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..0f820020b67f13bb9135c752667aaff44c1affcd
--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlignment.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8bede89325746a1b540c3350a473ceeaea39a5cea7a641c340e9013655865936
+size 484
diff --git a/ANE/ANE-zh/KokoroAlignment.mlmodelc/metadata.json b/ANE/ANE-zh/KokoroAlignment.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..a78f3e75fdfb288bd29cea1703f3f3e88b93eb70
--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlignment.mlmodelc/metadata.json
@@ -0,0 +1,107 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "en",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "asr",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios17.squeeze" : 1,
+      "Ios16.cumsum" : 1,
+      "Ios17.logicalAnd" : 1,
+      "Ios17.cast" : 4,
+      "Ios17.less" : 1,
+      "Ios17.matmul" : 2,
+      "Ios17.sliceByIndex" : 3,
+      "Ios17.concat" : 2,
+      "Ios17.greaterEqual" : 1,
+      "Ios17.sub" : 1,
+      "Ios17.expandDims" : 2
+    },
+    "computePrecision" : "Mixed (Float16, Int16, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-05-03",
+      "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 × 2...512",
+        "shapeRange" : "[[1, 1], [2, 512]]",
+        "formattedType" : "MultiArray (Int32 1 × 37)",
+        "type" : "MultiArray",
+        "shape" : "[1, 37]",
+        "name" : "pred_dur",
+        "shortDescription" : ""
+      },
+      {
+        "dataType" : "Float16",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 2...512 × 640",
+        "shapeRange" : "[[1, 1], [2, 512], [640, 640]]",
+        "formattedType" : "MultiArray (Float16 1 × 37 × 640)",
+        "type" : "MultiArray",
+        "shape" : "[1, 37, 640]",
+        "name" : "d",
+        "shortDescription" : ""
+      },
+      {
+        "dataType" : "Float16",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 512 × 2...512",
+        "shapeRange" : "[[1, 1], [512, 512], [2, 512]]",
+        "formattedType" : "MultiArray (Float16 1 × 512 × 37)",
+        "type" : "MultiArray",
+        "shape" : "[1, 512, 37]",
+        "name" : "t_en",
+        "shortDescription" : ""
+      }
+    ],
+    "generatedClassName" : "KokoroAlignment",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/ANE/ANE-zh/KokoroAlignment.mlmodelc/model.mil b/ANE/ANE-zh/KokoroAlignment.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..cb2470c26cab125f80f5f4ae3f1f8cdb37a70eec
--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlignment.mlmodelc/model.mil
@@ -0,0 +1,54 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})]
+{
+    func main<ios17>(tensor<fp16, [1, ?, 640]> d, tensor<int32, [1, ?]> pred_dur, tensor<fp16, [1, 512, ?]> t_en) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"d", [1, 37, 640]}, {"pred_dur", [1, 37]}, {"t_en", [1, 512, 37]}}), ("RangeDims", {{"d", [[1, 1], [2, 512], [640, 640]]}, {"pred_dur", [[1, 1], [2, 512]]}, {"t_en", [[1, 1], [512, 512], [2, 512]]}})))] {
+            tensor<int32, []> var_19 = const()[name = tensor<string, []>("op_19"), val = tensor<int32, []>(-1)];
+            tensor<bool, []> cum_dur_exclusive_0 = const()[name = tensor<string, []>("cum_dur_exclusive_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> cum_dur_reverse_0 = const()[name = tensor<string, []>("cum_dur_reverse_0"), val = tensor<bool, []>(false)];
+            tensor<string, []> dur_to_fp16_dtype_0 = const()[name = tensor<string, []>("dur_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [1, ?]> pred_dur_to_fp16 = cast(dtype = dur_to_fp16_dtype_0, x = pred_dur)[name = tensor<string, []>("cast_3")];
+            tensor<fp16, [1, ?]> cum_dur_cast_fp16 = cumsum(axis = var_19, exclusive = cum_dur_exclusive_0, reverse = cum_dur_reverse_0, x = pred_dur_to_fp16)[name = tensor<string, []>("cum_dur_cast_fp16")];
+            tensor<fp16, [1, ?]> starts_cast_fp16 = sub(x = cum_dur_cast_fp16, y = pred_dur_to_fp16)[name = tensor<string, []>("starts_cast_fp16")];
+            tensor<int32, [1]> var_40_axes_0 = const()[name = tensor<string, []>("op_40_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 1]> var_40_cast_fp16 = expand_dims(axes = var_40_axes_0, x = starts_cast_fp16)[name = tensor<string, []>("op_40_cast_fp16")];
+            tensor<fp16, [1, 1, 2000]> frames_to_fp16 = const()[name = tensor<string, []>("frames_to_fp16"), val = tensor<fp16, [1, 1, 2000]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<bool, [1, ?, 2000]> var_41_cast_fp16 = greater_equal(x = frames_to_fp16, y = var_40_cast_fp16)[name = tensor<string, []>("op_41_cast_fp16")];
+            tensor<int32, [1]> var_43_axes_0 = const()[name = tensor<string, []>("op_43_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 1]> var_43_cast_fp16 = expand_dims(axes = var_43_axes_0, x = cum_dur_cast_fp16)[name = tensor<string, []>("op_43_cast_fp16")];
+            tensor<bool, [1, ?, 2000]> var_44_cast_fp16 = less(x = frames_to_fp16, y = var_43_cast_fp16)[name = tensor<string, []>("op_44_cast_fp16")];
+            tensor<bool, [1, ?, 2000]> var_45 = logical_and(x = var_41_cast_fp16, y = var_44_cast_fp16)[name = tensor<string, []>("op_45")];
+            tensor<bool, []> en_transpose_x_1 = const()[name = tensor<string, []>("en_transpose_x_1"), val = tensor<bool, []>(true)];
+            tensor<bool, []> en_transpose_y_1 = const()[name = tensor<string, []>("en_transpose_y_1"), val = tensor<bool, []>(false)];
+            tensor<string, []> alignment_to_fp16_dtype_0 = const()[name = tensor<string, []>("alignment_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [1, ?, 2000]> var_45_to_fp16 = cast(dtype = alignment_to_fp16_dtype_0, x = var_45)[name = tensor<string, []>("cast_2")];
+            tensor<fp16, [1, 640, 2000]> en_cast_fp16 = matmul(transpose_x = en_transpose_x_1, transpose_y = en_transpose_y_1, x = d, y = var_45_to_fp16)[name = tensor<string, []>("en_cast_fp16")];
+            tensor<bool, []> asr_transpose_x_0 = const()[name = tensor<string, []>("asr_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> asr_transpose_y_0 = const()[name = tensor<string, []>("asr_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 512, 2000]> asr_cast_fp16 = matmul(transpose_x = asr_transpose_x_0, transpose_y = asr_transpose_y_0, x = t_en, y = var_45_to_fp16)[name = tensor<string, []>("asr_cast_fp16")];
+            tensor<int32, [2]> var_65_begin_0 = const()[name = tensor<string, []>("op_65_begin_0"), val = tensor<int32, [2]>([0, -1])];
+            tensor<int32, [2]> var_65_end_0 = const()[name = tensor<string, []>("op_65_end_0"), val = tensor<int32, [2]>([1, 0])];
+            tensor<bool, [2]> var_65_end_mask_0 = const()[name = tensor<string, []>("op_65_end_mask_0"), val = tensor<bool, [2]>([true, true])];
+            tensor<fp16, [1, 1]> var_65_cast_fp16 = slice_by_index(begin = var_65_begin_0, end = var_65_end_0, end_mask = var_65_end_mask_0, x = cum_dur_cast_fp16)[name = tensor<string, []>("op_65_cast_fp16")];
+            tensor<string, []> var_70_to_int16_dtype_0 = const()[name = tensor<string, []>("op_70_to_int16_dtype_0"), val = tensor<string, []>("int16")];
+            tensor<int16, [1, 1]> var_65_cast_fp16_to_int16 = cast(dtype = var_70_to_int16_dtype_0, x = var_65_cast_fp16)[name = tensor<string, []>("cast_1")];
+            tensor<int16, []> T_a_cast_int16 = squeeze(x = var_65_cast_fp16_to_int16)[name = tensor<string, []>("T_a_cast_int16")];
+            tensor<string, []> T_a_cast_int16_to_int32_dtype_0 = const()[name = tensor<string, []>("T_a_cast_int16_to_int32_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, []> concat_0_values0_0 = const()[name = tensor<string, []>("concat_0_values0_0"), val = tensor<int32, []>(1)];
+            tensor<int32, []> concat_0_values1_0 = const()[name = tensor<string, []>("concat_0_values1_0"), val = tensor<int32, []>(640)];
+            tensor<int32, []> concat_0_axis_0 = const()[name = tensor<string, []>("concat_0_axis_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> concat_0_interleave_0 = const()[name = tensor<string, []>("concat_0_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<int32, []> T_a_cast_int16_to_int32 = cast(dtype = T_a_cast_int16_to_int32_dtype_0, x = T_a_cast_int16)[name = tensor<string, []>("cast_0")];
+            tensor<int32, [3]> concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (concat_0_values0_0, concat_0_values1_0, T_a_cast_int16_to_int32))[name = tensor<string, []>("concat_0")];
+            tensor<int32, [3]> var_87_begin_0 = const()[name = tensor<string, []>("op_87_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<bool, [3]> var_87_end_mask_0 = const()[name = tensor<string, []>("op_87_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<fp16, [1, 640, ?]> en = slice_by_index(begin = var_87_begin_0, end = concat_0, end_mask = var_87_end_mask_0, x = en_cast_fp16)[name = tensor<string, []>("op_87_cast_fp16")];
+            tensor<int32, []> concat_1_values0_0 = const()[name = tensor<string, []>("concat_1_values0_0"), val = tensor<int32, []>(1)];
+            tensor<int32, []> concat_1_values1_0 = const()[name = tensor<string, []>("concat_1_values1_0"), val = tensor<int32, []>(512)];
+            tensor<int32, []> concat_1_axis_0 = const()[name = tensor<string, []>("concat_1_axis_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> concat_1_interleave_0 = const()[name = tensor<string, []>("concat_1_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [3]> concat_1 = concat(axis = concat_1_axis_0, interleave = concat_1_interleave_0, values = (concat_1_values0_0, concat_1_values1_0, T_a_cast_int16_to_int32))[name = tensor<string, []>("concat_1")];
+            tensor<int32, [3]> var_101_begin_0 = const()[name = tensor<string, []>("op_101_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<bool, [3]> var_101_end_mask_0 = const()[name = tensor<string, []>("op_101_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<fp16, [1, 512, ?]> asr = slice_by_index(begin = var_101_begin_0, end = concat_1, end_mask = var_101_end_mask_0, x = asr_cast_fp16)[name = tensor<string, []>("op_101_cast_fp16")];
+        } -> (en, asr);
+}
\ No newline at end of file
diff --git a/ANE/ANE-zh/KokoroAlignment.mlmodelc/weights/weight.bin b/ANE/ANE-zh/KokoroAlignment.mlmodelc/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..6104dbdaf764de798e11c12c90ac8fa596b41bd5
--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlignment.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2e7d69128b59d615fc3d3cf85637a687235fc086b1eb136359adb11a61615f6b
+size 4128
diff --git a/ANE/ANE-zh/KokoroAlignment.mlpackage/Data/com.apple.CoreML/model.mlmodel b/ANE/ANE-zh/KokoroAlignment.mlpackage/Data/com.apple.CoreML/model.mlmodel
new file mode 100644
index 0000000000000000000000000000000000000000..96f73b4e9dacd0529c8c9da7a68aaefc57ed3cf9
--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlignment.mlpackage/Data/com.apple.CoreML/model.mlmodel
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4e5b90ecdad726428342766d68fecde858ea7218484141cde85af1672aa98279
+size 6971
diff --git a/ANE/ANE-zh/KokoroAlignment.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/ANE/ANE-zh/KokoroAlignment.mlpackage/Data/com.apple.CoreML/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..6104dbdaf764de798e11c12c90ac8fa596b41bd5
--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlignment.mlpackage/Data/com.apple.CoreML/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2e7d69128b59d615fc3d3cf85637a687235fc086b1eb136359adb11a61615f6b
+size 4128
diff --git a/ANE/ANE-zh/KokoroAlignment.mlpackage/Manifest.json b/ANE/ANE-zh/KokoroAlignment.mlpackage/Manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..e6670661a0a1d37994e8a6af298dead812f396f2
--- /dev/null
+++ b/ANE/ANE-zh/KokoroAlignment.mlpackage/Manifest.json
@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "538DCB08-7D66-45AF-AB6B-C363411A8FC9": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        },
+        "910C3F65-B5FE-43B5-80BA-3B9677FBA054": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "538DCB08-7D66-45AF-AB6B-C363411A8FC9"
+}
diff --git a/ANE/ANE-zh/KokoroNoise.mlmodelc/analytics/coremldata.bin b/ANE/ANE-zh/KokoroNoise.mlmodelc/analytics/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..8fd76779f8d49471b9a939cb0d38034a3c3df2c9
--- /dev/null
+++ b/ANE/ANE-zh/KokoroNoise.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7536e2bb87ceabc3b06e8e7c44cd658e050f7c4fab89b45e3c758ff5c8f1d59
+size 243
diff --git a/ANE/ANE-zh/KokoroNoise.mlmodelc/coremldata.bin b/ANE/ANE-zh/KokoroNoise.mlmodelc/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..c5f8b53d077eb7752b4a70309a22e388383a0746
--- /dev/null
+++ b/ANE/ANE-zh/KokoroNoise.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7030a1d8a73eb6cda44c7ba9afabb6856c098b62bd728b5af2875900a5e5f155
+size 440
diff --git a/ANE/ANE-zh/KokoroNoise.mlmodelc/metadata.json b/ANE/ANE-zh/KokoroNoise.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..1e6481e4092b364ce98bd86460ff64712f396e4b
--- /dev/null
+++ b/ANE/ANE-zh/KokoroNoise.mlmodelc/metadata.json
@@ -0,0 +1,111 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Mixed (Float32, Palettized (8 bits))",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "x_source_0",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "x_source_1",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios16.cumsum" : 1,
+      "Ios17.concat" : 1,
+      "UpsampleNearestNeighbor" : 1,
+      "Ios17.equal" : 1,
+      "Ios17.logicalAnd" : 5,
+      "Ios17.reshape" : 12,
+      "Ios17.instanceNorm" : 12,
+      "Ios17.transpose" : 5,
+      "Ios17.sin" : 13,
+      "Split" : 12,
+      "Ios17.expandDims" : 4,
+      "Ios16.avgPool" : 1,
+      "Ios17.add" : 50,
+      "Ios17.squeeze" : 3,
+      "Pad" : 1,
+      "Ios16.upsampleBilinear" : 1,
+      "Ios17.sqrt" : 1,
+      "Ios17.sub" : 4,
+      "Ios16.constexprLutToDense" : 26,
+      "Ios17.conv" : 16,
+      "Ios17.tanh" : 1,
+      "Ios17.linear" : 13,
+      "Ios17.pow" : 14,
+      "Ios17.cast" : 6,
+      "Ios17.less" : 3,
+      "Ios17.realDiv" : 1,
+      "Ios17.atan" : 1,
+      "Ios17.greater" : 3,
+      "Ios17.mul" : 53
+    },
+    "computePrecision" : "Mixed (Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-05-03",
+      "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 × 2...4000",
+        "shapeRange" : "[[1, 1], [2, 4000]]",
+        "formattedType" : "MultiArray (Float32 1 × 266)",
+        "type" : "MultiArray",
+        "shape" : "[1, 266]",
+        "name" : "F0_curve",
+        "shortDescription" : ""
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 128)",
+        "shortDescription" : "",
+        "shape" : "[1, 128]",
+        "name" : "style_timbre",
+        "type" : "MultiArray"
+      }
+    ],
+    "generatedClassName" : "KokoroNoise",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/ANE/ANE-zh/KokoroNoise.mlmodelc/model.mil b/ANE/ANE-zh/KokoroNoise.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..2f9ed4843914b45f5c1bdf86b5f9110a359ad480
--- /dev/null
+++ b/ANE/ANE-zh/KokoroNoise.mlmodelc/model.mil
@@ -0,0 +1,535 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})]
+{
+    func main<ios17>(tensor<fp32, [1, ?]> F0_curve, tensor<fp32, [1, 128]> style_timbre) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"F0_curve", [1, 266]}}), ("RangeDims", {{"F0_curve", [[1, 1], [2, 4000]]}})))] {
+            tensor<fp32, [1]> m_source_l_linear_bias = const()[name = tensor<string, []>("m_source_l_linear_bias"), val = tensor<fp32, [1]>([-0x1.e28358p-6])];
+            tensor<fp32, [1, 9]> m_source_l_linear_weight = const()[name = tensor<string, []>("m_source_l_linear_weight"), val = tensor<fp32, [1, 9]>([[-0x1.4dfed8p-4, -0x1.7b4864p-3, -0x1.7608cep-3, -0x1.6d4e54p-3, -0x1.946f4ap-4, 0x1.527ebcp-4, 0x1.66277ap-4, -0x1.900fdap-2, -0x1.1871f2p-1]])];
+            tensor<fp32, [11, 1, 20]> stft_conv_real_weight = const()[name = tensor<string, []>("stft_conv_real_weight"), val = tensor<fp32, [11, 1, 20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp32, [11, 1, 20]> stft_conv_imag_weight = const()[name = tensor<string, []>("stft_conv_imag_weight"), val = tensor<fp32, [11, 1, 20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1024)))];
+            tensor<fp32, [256]> noise_convs_0_bias = const()[name = tensor<string, []>("noise_convs_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1984)))];
+            tensor<fp32, [256, 22, 12]> noise_convs_0_weight_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [67584]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3072))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(70720))), name = tensor<string, []>("noise_convs_0_weight_palettized"), shape = tensor<uint32, [3]>([256, 22, 12])];
+            tensor<fp32, [1, 256, 1]> noise_res_0_alpha2_2 = const()[name = tensor<string, []>("noise_res_0_alpha2_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(71808)))];
+            tensor<fp32, [1, 256, 1]> noise_res_0_alpha1_2 = const()[name = tensor<string, []>("noise_res_0_alpha1_2"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(72896)))];
+            tensor<fp32, [1, 256, 1]> noise_res_0_alpha2_1 = const()[name = tensor<string, []>("noise_res_0_alpha2_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(73984)))];
+            tensor<fp32, [1, 256, 1]> noise_res_0_alpha1_1 = const()[name = tensor<string, []>("noise_res_0_alpha1_1"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(75072)))];
+            tensor<fp32, [1, 256, 1]> noise_res_0_alpha2_0 = const()[name = tensor<string, []>("noise_res_0_alpha2_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(76160)))];
+            tensor<fp32, [1, 256, 1]> noise_res_0_alpha1_0 = const()[name = tensor<string, []>("noise_res_0_alpha1_0"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77248)))];
+            tensor<fp32, [512]> noise_res_0_adain1_0_fc_bias = const()[name = tensor<string, []>("noise_res_0_adain1_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(78336)))];
+            tensor<fp32, [512, 128]> noise_res_0_adain1_0_fc_weight_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(80448))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(146048))), name = tensor<string, []>("noise_res_0_adain1_0_fc_weight_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp32, [256]> noise_res_0_adain1_0_norm_bias = const()[name = tensor<string, []>("noise_res_0_adain1_0_norm_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(147136)))];
+            tensor<fp32, [256]> noise_res_0_adain1_0_norm_weight = const()[name = tensor<string, []>("noise_res_0_adain1_0_norm_weight"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(148224)))];
+            tensor<fp32, [256]> noise_res_0_convs1_0_bias = const()[name = tensor<string, []>("noise_res_0_convs1_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149312)))];
+            tensor<fp32, [512]> noise_res_0_adain2_0_fc_bias = const()[name = tensor<string, []>("noise_res_0_adain2_0_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(150400)))];
+            tensor<fp32, [512, 128]> noise_res_0_adain2_0_fc_weight_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(152512))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(218112))), name = tensor<string, []>("noise_res_0_adain2_0_fc_weight_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp32, [256]> noise_res_0_convs2_0_bias = const()[name = tensor<string, []>("noise_res_0_convs2_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219200)))];
+            tensor<fp32, [512]> noise_res_0_adain1_1_fc_bias = const()[name = tensor<string, []>("noise_res_0_adain1_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(220288)))];
+            tensor<fp32, [512, 128]> noise_res_0_adain1_1_fc_weight_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222400))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288000))), name = tensor<string, []>("noise_res_0_adain1_1_fc_weight_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp32, [256]> noise_res_0_convs1_1_bias = const()[name = tensor<string, []>("noise_res_0_convs1_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(289088)))];
+            tensor<fp32, [512]> noise_res_0_adain2_1_fc_bias = const()[name = tensor<string, []>("noise_res_0_adain2_1_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(290176)))];
+            tensor<fp32, [512, 128]> noise_res_0_adain2_1_fc_weight_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(292288))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(357888))), name = tensor<string, []>("noise_res_0_adain2_1_fc_weight_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp32, [256]> noise_res_0_convs2_1_bias = const()[name = tensor<string, []>("noise_res_0_convs2_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(358976)))];
+            tensor<fp32, [512]> noise_res_0_adain1_2_fc_bias = const()[name = tensor<string, []>("noise_res_0_adain1_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(360064)))];
+            tensor<fp32, [512, 128]> noise_res_0_adain1_2_fc_weight_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(362176))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(427776))), name = tensor<string, []>("noise_res_0_adain1_2_fc_weight_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp32, [256]> noise_res_0_convs1_2_bias = const()[name = tensor<string, []>("noise_res_0_convs1_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(428864)))];
+            tensor<fp32, [512]> noise_res_0_adain2_2_fc_bias = const()[name = tensor<string, []>("noise_res_0_adain2_2_fc_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(429952)))];
+            tensor<fp32, [512, 128]> noise_res_0_adain2_2_fc_weight_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(432064))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(497664))), name = tensor<string, []>("noise_res_0_adain2_2_fc_weight_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp32, [256]> noise_res_0_convs2_2_bias = const()[name = tensor<string, []>("noise_res_0_convs2_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498752)))];
+            tensor<fp32, [128]> noise_convs_1_bias = const()[name = tensor<string, []>("noise_convs_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(499840)))];
+            tensor<fp32, [128, 22, 1]> noise_convs_1_weight_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [2816]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500416))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503296))), name = tensor<string, []>("noise_convs_1_weight_palettized"), shape = tensor<uint32, [3]>([128, 22, 1])];
+            tensor<fp32, [1, 128, 1]> noise_res_1_alpha2_2 = const()[name = tensor<string, []>("noise_res_1_alpha2_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504384)))];
+            tensor<fp32, [1, 128, 1]> noise_res_1_alpha1_2 = const()[name = tensor<string, []>("noise_res_1_alpha1_2"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(504960)))];
+            tensor<fp32, [1, 128, 1]> noise_res_1_alpha2_1 = const()[name = tensor<string, []>("noise_res_1_alpha2_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(505536)))];
+            tensor<fp32, [1, 128, 1]> noise_res_1_alpha1_1 = const()[name = tensor<string, []>("noise_res_1_alpha1_1"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506112)))];
+            tensor<fp32, [1, 128, 1]> noise_res_1_alpha2_0 = const()[name = tensor<string, []>("noise_res_1_alpha2_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(506688)))];
+            tensor<fp32, [1, 128, 1]> noise_res_1_alpha1_0 = const()[name = tensor<string, []>("noise_res_1_alpha1_0"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507264)))];
+            tensor<fp32, [256]> noise_res_1_adain1_0_fc_bias = const()[name = tensor<string, []>("noise_res_1_adain1_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(507840)))];
+            tensor<fp32, [256, 128]> noise_res_1_adain1_0_fc_weight_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(508928))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(541760))), name = tensor<string, []>("noise_res_1_adain1_0_fc_weight_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp32, [128]> noise_res_1_adain1_0_norm_bias = const()[name = tensor<string, []>("noise_res_1_adain1_0_norm_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(542848)))];
+            tensor<fp32, [128]> noise_res_1_adain1_0_norm_weight = const()[name = tensor<string, []>("noise_res_1_adain1_0_norm_weight"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543424)))];
+            tensor<fp32, [128]> noise_res_1_convs1_0_bias = const()[name = tensor<string, []>("noise_res_1_convs1_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(544000)))];
+            tensor<fp32, [256]> noise_res_1_adain2_0_fc_bias = const()[name = tensor<string, []>("noise_res_1_adain2_0_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(544576)))];
+            tensor<fp32, [256, 128]> noise_res_1_adain2_0_fc_weight_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545664))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(578496))), name = tensor<string, []>("noise_res_1_adain2_0_fc_weight_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp32, [128]> noise_res_1_convs2_0_bias = const()[name = tensor<string, []>("noise_res_1_convs2_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579584)))];
+            tensor<fp32, [256]> noise_res_1_adain1_1_fc_bias = const()[name = tensor<string, []>("noise_res_1_adain1_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(580160)))];
+            tensor<fp32, [256, 128]> noise_res_1_adain1_1_fc_weight_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(581248))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(614080))), name = tensor<string, []>("noise_res_1_adain1_1_fc_weight_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp32, [128]> noise_res_1_convs1_1_bias = const()[name = tensor<string, []>("noise_res_1_convs1_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(615168)))];
+            tensor<fp32, [256]> noise_res_1_adain2_1_fc_bias = const()[name = tensor<string, []>("noise_res_1_adain2_1_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(615744)))];
+            tensor<fp32, [256, 128]> noise_res_1_adain2_1_fc_weight_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(616832))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(649664))), name = tensor<string, []>("noise_res_1_adain2_1_fc_weight_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp32, [128]> noise_res_1_convs2_1_bias = const()[name = tensor<string, []>("noise_res_1_convs2_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(650752)))];
+            tensor<fp32, [256]> noise_res_1_adain1_2_fc_bias = const()[name = tensor<string, []>("noise_res_1_adain1_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(651328)))];
+            tensor<fp32, [256, 128]> noise_res_1_adain1_2_fc_weight_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(652416))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(685248))), name = tensor<string, []>("noise_res_1_adain1_2_fc_weight_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp32, [128]> noise_res_1_convs1_2_bias = const()[name = tensor<string, []>("noise_res_1_convs1_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(686336)))];
+            tensor<fp32, [256]> noise_res_1_adain2_2_fc_bias = const()[name = tensor<string, []>("noise_res_1_adain2_2_fc_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(686912)))];
+            tensor<fp32, [256, 128]> noise_res_1_adain2_2_fc_weight_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(688000))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(720832))), name = tensor<string, []>("noise_res_1_adain2_2_fc_weight_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp32, [128]> noise_res_1_convs2_2_bias = const()[name = tensor<string, []>("noise_res_1_convs2_2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(721920)))];
+            tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 1, ?]> input_1 = expand_dims(axes = input_1_axes_0, x = F0_curve)[name = tensor<string, []>("input_1")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = tensor<string, []>("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [1, 1, ?, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input_1)[name = tensor<string, []>("expand_dims_0")];
+            tensor<int32, []> upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_0_scale_factor_height_0"), val = tensor<int32, []>(300)];
+            tensor<int32, []> upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_0_scale_factor_width_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 1, ?, 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 = tensor<string, []>("upsample_nearest_neighbor_0")];
+            tensor<int32, [1]> var_26_axes_0 = const()[name = tensor<string, []>("op_26_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [1, 1, ?]> var_26 = squeeze(axes = var_26_axes_0, x = upsample_nearest_neighbor_0)[name = tensor<string, []>("op_26")];
+            tensor<int32, []> var_30 = const()[name = tensor<string, []>("op_30"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 9, 1]> const_26 = const()[name = tensor<string, []>("const_26"), val = tensor<fp32, [1, 9, 1]>([[[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, 9, ?]> fn = mul(x = var_26, y = const_26)[name = tensor<string, []>("fn")];
+            tensor<fp32, []> _inversed_rad_values_y_0 = const()[name = tensor<string, []>("_inversed_rad_values_y_0"), val = tensor<fp32, []>(0x1.5d867cp-15)];
+            tensor<fp32, [1, 9, ?]> _inversed_rad_values = mul(x = fn, y = _inversed_rad_values_y_0)[name = tensor<string, []>("_inversed_rad_values")];
+            tensor<int32, [1]> var_50 = const()[name = tensor<string, []>("op_50"), val = tensor<int32, [1]>([300])];
+            tensor<int32, [1]> var_51 = const()[name = tensor<string, []>("op_51"), val = tensor<int32, [1]>([300])];
+            tensor<string, []> rv_down_pad_type_0 = const()[name = tensor<string, []>("rv_down_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> rv_down_pad_0 = const()[name = tensor<string, []>("rv_down_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> rv_down_exclude_padding_from_average_0 = const()[name = tensor<string, []>("rv_down_exclude_padding_from_average_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> rv_down_ceil_mode_0 = const()[name = tensor<string, []>("rv_down_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 9, ?]> rv_down = avg_pool(ceil_mode = rv_down_ceil_mode_0, exclude_padding_from_average = rv_down_exclude_padding_from_average_0, kernel_sizes = var_50, pad = rv_down_pad_0, pad_type = rv_down_pad_type_0, strides = var_51, x = _inversed_rad_values)[name = tensor<string, []>("rv_down")];
+            tensor<int32, [3]> rad_values_down_perm_0 = const()[name = tensor<string, []>("rad_values_down_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<bool, []> var_55_exclusive_0 = const()[name = tensor<string, []>("op_55_exclusive_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> var_55_reverse_0 = const()[name = tensor<string, []>("op_55_reverse_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, ?, 9]> rad_values_down = transpose(perm = rad_values_down_perm_0, x = rv_down)[name = tensor<string, []>("transpose_4")];
+            tensor<fp32, [1, ?, 9]> var_55 = cumsum(axis = var_30, exclusive = var_55_exclusive_0, reverse = var_55_reverse_0, x = rad_values_down)[name = tensor<string, []>("op_55")];
+            tensor<fp32, []> var_56 = const()[name = tensor<string, []>("op_56"), val = tensor<fp32, []>(0x1.921fb6p+2)];
+            tensor<fp32, [1, ?, 9]> phase_1 = mul(x = var_55, y = var_56)[name = tensor<string, []>("phase_1")];
+            tensor<int32, [3]> var_58_perm_0 = const()[name = tensor<string, []>("op_58_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp32, []> var_59_promoted = const()[name = tensor<string, []>("op_59_promoted"), val = tensor<fp32, []>(0x1.2cp+8)];
+            tensor<fp32, [1, 9, ?]> var_58 = transpose(perm = var_58_perm_0, x = phase_1)[name = tensor<string, []>("transpose_3")];
+            tensor<fp32, [1, 9, ?]> input_3 = mul(x = var_58, y = var_59_promoted)[name = tensor<string, []>("input_3")];
+            tensor<int32, [1]> expand_dims_1_axes_0 = const()[name = tensor<string, []>("expand_dims_1_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [1, 9, ?, 1]> expand_dims_1 = expand_dims(axes = expand_dims_1_axes_0, x = input_3)[name = tensor<string, []>("expand_dims_1")];
+            tensor<int32, []> upsample_bilinear_0_scale_factor_height_0 = const()[name = tensor<string, []>("upsample_bilinear_0_scale_factor_height_0"), val = tensor<int32, []>(300)];
+            tensor<bool, []> upsample_bilinear_0_align_corners_0 = const()[name = tensor<string, []>("upsample_bilinear_0_align_corners_0"), val = tensor<bool, []>(false)];
+            tensor<int32, []> upsample_bilinear_0_scale_factor_width_0 = const()[name = tensor<string, []>("upsample_bilinear_0_scale_factor_width_0"), val = tensor<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_1)[name = tensor<string, []>("upsample_bilinear_0")];
+            tensor<int32, [1]> ph_up_axes_0 = const()[name = tensor<string, []>("ph_up_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [1, 9, ?]> ph_up = squeeze(axes = ph_up_axes_0, x = upsample_bilinear_0)[name = tensor<string, []>("ph_up")];
+            tensor<int32, [3]> phase_perm_0 = const()[name = tensor<string, []>("phase_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp32, [1, ?, 9]> phase = transpose(perm = phase_perm_0, x = ph_up)[name = tensor<string, []>("transpose_2")];
+            tensor<fp32, [1, ?, 9]> var_64 = sin(x = phase)[name = tensor<string, []>("op_64")];
+            tensor<fp32, []> var_65 = const()[name = tensor<string, []>("op_65"), val = tensor<fp32, []>(0x1.99999ap-4)];
+            tensor<fp32, [1, ?, 9]> sines = mul(x = var_64, y = var_65)[name = tensor<string, []>("sines")];
+            tensor<fp32, []> var_31_promoted = const()[name = tensor<string, []>("op_31_promoted"), val = tensor<fp32, []>(0x1.4p+3)];
+            tensor<int32, [3]> transpose_0_perm_0 = const()[name = tensor<string, []>("transpose_0_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp32, [1, ?, 1]> transpose_0 = transpose(perm = transpose_0_perm_0, x = var_26)[name = tensor<string, []>("transpose_1")];
+            tensor<bool, [1, ?, 1]> var_67 = greater(x = transpose_0, y = var_31_promoted)[name = tensor<string, []>("op_67")];
+            tensor<string, []> uv_dtype_0 = const()[name = tensor<string, []>("uv_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp32, []> var_69 = const()[name = tensor<string, []>("op_69"), val = tensor<fp32, []>(0x1.89374cp-9)];
+            tensor<fp32, [1, ?, 1]> uv = cast(dtype = uv_dtype_0, x = var_67)[name = tensor<string, []>("cast_5")];
+            tensor<fp32, [1, ?, 1]> var_70 = mul(x = uv, y = var_69)[name = tensor<string, []>("op_70")];
+            tensor<fp32, []> var_30_promoted = const()[name = tensor<string, []>("op_30_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, ?, 1]> var_71 = sub(x = var_30_promoted, y = uv)[name = tensor<string, []>("op_71")];
+            tensor<fp32, []> var_72 = const()[name = tensor<string, []>("op_72"), val = tensor<fp32, []>(0x1.99999ap-4)];
+            tensor<fp32, [1, ?, 1]> var_73 = mul(x = var_71, y = var_72)[name = tensor<string, []>("op_73")];
+            tensor<fp32, []> _inversed_75_y_0 = const()[name = tensor<string, []>("_inversed_75_y_0"), val = tensor<fp32, []>(0x1.555556p-2)];
+            tensor<fp32, [1, ?, 1]> _inversed_75 = mul(x = var_73, y = _inversed_75_y_0)[name = tensor<string, []>("_inversed_75")];
+            tensor<fp32, [1, ?, 1]> noise_amp = add(x = var_70, y = _inversed_75)[name = tensor<string, []>("noise_amp")];
+            tensor<fp32, []> var_77 = const()[name = tensor<string, []>("op_77"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, [1, ?, 1]> noise = mul(x = noise_amp, y = var_77)[name = tensor<string, []>("noise")];
+            tensor<fp32, [1, ?, 9]> var_79 = mul(x = sines, y = uv)[name = tensor<string, []>("op_79")];
+            tensor<fp32, [1, ?, 9]> input_5 = add(x = var_79, y = noise)[name = tensor<string, []>("input_5")];
+            tensor<fp32, [1, ?, 1]> input_7 = linear(bias = m_source_l_linear_bias, weight = m_source_l_linear_weight, x = input_5)[name = tensor<string, []>("linear_0")];
+            tensor<fp32, [1, ?, 1]> har_source = tanh(x = input_7)[name = tensor<string, []>("har_source")];
+            tensor<int32, [3]> var_90_perm_0 = const()[name = tensor<string, []>("op_90_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> input_9_axes_0 = const()[name = tensor<string, []>("input_9_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 1, ?]> var_90 = transpose(perm = var_90_perm_0, x = har_source)[name = tensor<string, []>("transpose_0")];
+            tensor<fp32, [1, ?]> input_9 = squeeze(axes = input_9_axes_0, x = var_90)[name = tensor<string, []>("input_9")];
+            tensor<fp32, []> const_1 = const()[name = tensor<string, []>("const_1"), val = tensor<fp32, []>(0x0p+0)];
+            tensor<int32, [4]> waveform_pad_0 = const()[name = tensor<string, []>("waveform_pad_0"), val = tensor<int32, [4]>([0, 0, 10, 10])];
+            tensor<string, []> waveform_mode_0 = const()[name = tensor<string, []>("waveform_mode_0"), val = tensor<string, []>("replicate")];
+            tensor<fp32, [1, ?]> waveform = pad(constant_val = const_1, mode = waveform_mode_0, pad = waveform_pad_0, x = input_9)[name = tensor<string, []>("waveform")];
+            tensor<int32, [1]> input_11_axes_0 = const()[name = tensor<string, []>("input_11_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 1, ?]> input_11 = expand_dims(axes = input_11_axes_0, x = waveform)[name = tensor<string, []>("input_11")];
+            tensor<string, []> real_out_pad_type_0 = const()[name = tensor<string, []>("real_out_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> real_out_strides_0 = const()[name = tensor<string, []>("real_out_strides_0"), val = tensor<int32, [1]>([5])];
+            tensor<int32, [2]> real_out_pad_0 = const()[name = tensor<string, []>("real_out_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> real_out_dilations_0 = const()[name = tensor<string, []>("real_out_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> real_out_groups_0 = const()[name = tensor<string, []>("real_out_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 11, ?]> real_out = conv(dilations = real_out_dilations_0, groups = real_out_groups_0, pad = real_out_pad_0, pad_type = real_out_pad_type_0, strides = real_out_strides_0, weight = stft_conv_real_weight, x = input_11)[name = tensor<string, []>("real_out")];
+            tensor<string, []> imag_out_pad_type_0 = const()[name = tensor<string, []>("imag_out_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> imag_out_strides_0 = const()[name = tensor<string, []>("imag_out_strides_0"), val = tensor<int32, [1]>([5])];
+            tensor<int32, [2]> imag_out_pad_0 = const()[name = tensor<string, []>("imag_out_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> imag_out_dilations_0 = const()[name = tensor<string, []>("imag_out_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> imag_out_groups_0 = const()[name = tensor<string, []>("imag_out_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 11, ?]> imag_out = conv(dilations = imag_out_dilations_0, groups = imag_out_groups_0, pad = imag_out_pad_0, pad_type = imag_out_pad_type_0, strides = imag_out_strides_0, weight = stft_conv_imag_weight, x = input_11)[name = tensor<string, []>("imag_out")];
+            tensor<fp32, []> var_125_promoted = const()[name = tensor<string, []>("op_125_promoted"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 11, ?]> var_126 = pow(x = real_out, y = var_125_promoted)[name = tensor<string, []>("op_126")];
+            tensor<fp32, []> var_127_promoted = const()[name = tensor<string, []>("op_127_promoted"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 11, ?]> var_128 = pow(x = imag_out, y = var_127_promoted)[name = tensor<string, []>("op_128")];
+            tensor<fp32, [1, 11, ?]> var_130 = add(x = var_126, y = var_128)[name = tensor<string, []>("op_130")];
+            tensor<fp32, []> var_132 = const()[name = tensor<string, []>("op_132"), val = tensor<fp32, []>(0x1.6849b8p-47)];
+            tensor<fp32, [1, 11, ?]> var_133 = add(x = var_130, y = var_132)[name = tensor<string, []>("op_133")];
+            tensor<fp32, [1, 11, ?]> har_spec = sqrt(x = var_133)[name = tensor<string, []>("har_spec")];
+            tensor<fp32, []> less_0_y_0 = const()[name = tensor<string, []>("less_0_y_0"), val = tensor<fp32, []>(0x0p+0)];
+            tensor<bool, [1, 11, ?]> less_0 = less(x = imag_out, y = less_0_y_0)[name = tensor<string, []>("less_0")];
+            tensor<fp32, []> greater_0_y_0 = const()[name = tensor<string, []>("greater_0_y_0"), val = tensor<fp32, []>(0x0p+0)];
+            tensor<bool, [1, 11, ?]> greater_0 = greater(x = imag_out, y = greater_0_y_0)[name = tensor<string, []>("greater_0")];
+            tensor<fp32, []> less_1_y_0 = const()[name = tensor<string, []>("less_1_y_0"), val = tensor<fp32, []>(0x0p+0)];
+            tensor<bool, [1, 11, ?]> less_1 = less(x = real_out, y = less_1_y_0)[name = tensor<string, []>("less_1")];
+            tensor<fp32, []> equal_0_y_0 = const()[name = tensor<string, []>("equal_0_y_0"), val = tensor<fp32, []>(0x0p+0)];
+            tensor<bool, [1, 11, ?]> equal_0 = equal(x = real_out, y = equal_0_y_0)[name = tensor<string, []>("equal_0")];
+            tensor<bool, [1, 11, ?]> logical_and_0 = logical_and(x = greater_0, y = less_1)[name = tensor<string, []>("logical_and_0")];
+            tensor<bool, [1, 11, ?]> logical_and_1 = logical_and(x = less_0, y = less_1)[name = tensor<string, []>("logical_and_1")];
+            tensor<bool, [1, 11, ?]> logical_and_2 = logical_and(x = greater_0, y = equal_0)[name = tensor<string, []>("logical_and_2")];
+            tensor<bool, [1, 11, ?]> logical_and_3 = logical_and(x = less_0, y = equal_0)[name = tensor<string, []>("logical_and_3")];
+            tensor<string, []> cast_5_dtype_0 = const()[name = tensor<string, []>("cast_5_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<string, []> cast_6_dtype_0 = const()[name = tensor<string, []>("cast_6_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<string, []> cast_7_dtype_0 = const()[name = tensor<string, []>("cast_7_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<string, []> cast_8_dtype_0 = const()[name = tensor<string, []>("cast_8_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp32, []> mul_0_y_0 = const()[name = tensor<string, []>("mul_0_y_0"), val = tensor<fp32, []>(0x1.921fb6p+1)];
+            tensor<fp32, [1, 11, ?]> cast_5 = cast(dtype = cast_5_dtype_0, x = logical_and_0)[name = tensor<string, []>("cast_4")];
+            tensor<fp32, [1, 11, ?]> mul_0 = mul(x = cast_5, y = mul_0_y_0)[name = tensor<string, []>("mul_0")];
+            tensor<fp32, []> mul_1_y_0 = const()[name = tensor<string, []>("mul_1_y_0"), val = tensor<fp32, []>(0x1.921fb6p+1)];
+            tensor<fp32, [1, 11, ?]> cast_6 = cast(dtype = cast_6_dtype_0, x = logical_and_1)[name = tensor<string, []>("cast_3")];
+            tensor<fp32, [1, 11, ?]> mul_1 = mul(x = cast_6, y = mul_1_y_0)[name = tensor<string, []>("mul_1")];
+            tensor<fp32, []> sub_0_x_0 = const()[name = tensor<string, []>("sub_0_x_0"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 11, ?]> cast_7 = cast(dtype = cast_7_dtype_0, x = logical_and_2)[name = tensor<string, []>("cast_2")];
+            tensor<fp32, [1, 11, ?]> sub_0 = sub(x = sub_0_x_0, y = cast_7)[name = tensor<string, []>("sub_0")];
+            tensor<fp32, []> mul_2_y_0 = const()[name = tensor<string, []>("mul_2_y_0"), val = tensor<fp32, []>(0x1.921fb6p+0)];
+            tensor<fp32, [1, 11, ?]> mul_2 = mul(x = cast_7, y = mul_2_y_0)[name = tensor<string, []>("mul_2")];
+            tensor<fp32, []> sub_1_x_0 = const()[name = tensor<string, []>("sub_1_x_0"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 11, ?]> cast_8 = cast(dtype = cast_8_dtype_0, x = logical_and_3)[name = tensor<string, []>("cast_1")];
+            tensor<fp32, [1, 11, ?]> sub_1 = sub(x = sub_1_x_0, y = cast_8)[name = tensor<string, []>("sub_1")];
+            tensor<fp32, []> mul_3_y_0 = const()[name = tensor<string, []>("mul_3_y_0"), val = tensor<fp32, []>(-0x1.921fb6p+0)];
+            tensor<fp32, [1, 11, ?]> mul_3 = mul(x = cast_8, y = mul_3_y_0)[name = tensor<string, []>("mul_3")];
+            tensor<fp32, []> greater_1_y_0 = const()[name = tensor<string, []>("greater_1_y_0"), val = tensor<fp32, []>(-0x1.5798eep-27)];
+            tensor<bool, [1, 11, ?]> greater_1 = greater(x = real_out, y = greater_1_y_0)[name = tensor<string, []>("greater_1")];
+            tensor<fp32, []> less_2_y_0 = const()[name = tensor<string, []>("less_2_y_0"), val = tensor<fp32, []>(0x1.5798eep-27)];
+            tensor<bool, [1, 11, ?]> less_2 = less(x = real_out, y = less_2_y_0)[name = tensor<string, []>("less_2")];
+            tensor<bool, [1, 11, ?]> logical_and_4 = logical_and(x = greater_1, y = less_2)[name = tensor<string, []>("logical_and_4")];
+            tensor<string, []> cast_9_dtype_0 = const()[name = tensor<string, []>("cast_9_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp32, []> mul_4_y_0 = const()[name = tensor<string, []>("mul_4_y_0"), val = tensor<fp32, []>(0x1.5798eep-26)];
+            tensor<fp32, [1, 11, ?]> cast_9 = cast(dtype = cast_9_dtype_0, x = logical_and_4)[name = tensor<string, []>("cast_0")];
+            tensor<fp32, [1, 11, ?]> mul_4 = mul(x = cast_9, y = mul_4_y_0)[name = tensor<string, []>("mul_4")];
+            tensor<fp32, [1, 11, ?]> add_0 = add(x = real_out, y = mul_4)[name = tensor<string, []>("add_0")];
+            tensor<fp32, [1, 11, ?]> real_div_0 = real_div(x = imag_out, y = add_0)[name = tensor<string, []>("real_div_0")];
+            tensor<fp32, [1, 11, ?]> atan_0 = atan(x = real_div_0)[name = tensor<string, []>("atan_0")];
+            tensor<fp32, [1, 11, ?]> add_1 = add(x = atan_0, y = mul_0)[name = tensor<string, []>("add_1")];
+            tensor<fp32, [1, 11, ?]> sub_2 = sub(x = add_1, y = mul_1)[name = tensor<string, []>("sub_2")];
+            tensor<fp32, [1, 11, ?]> mul_5 = mul(x = sub_2, y = sub_0)[name = tensor<string, []>("mul_5")];
+            tensor<fp32, [1, 11, ?]> add_2 = add(x = mul_5, y = mul_2)[name = tensor<string, []>("add_2")];
+            tensor<fp32, [1, 11, ?]> mul_6 = mul(x = add_2, y = sub_1)[name = tensor<string, []>("mul_6")];
+            tensor<fp32, [1, 11, ?]> har_phase = add(x = mul_6, y = mul_3)[name = tensor<string, []>("har_phase")];
+            tensor<int32, []> var_137 = const()[name = tensor<string, []>("op_137"), val = tensor<int32, []>(1)];
+            tensor<bool, []> input_13_interleave_0 = const()[name = tensor<string, []>("input_13_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [1, 22, ?]> input_13 = concat(axis = var_137, interleave = input_13_interleave_0, values = (har_spec, har_phase))[name = tensor<string, []>("input_13")];
+            tensor<string, []> input_15_pad_type_0 = const()[name = tensor<string, []>("input_15_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_15_pad_0 = const()[name = tensor<string, []>("input_15_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> input_15_strides_0 = const()[name = tensor<string, []>("input_15_strides_0"), val = tensor<int32, [1]>([6])];
+            tensor<int32, [1]> input_15_dilations_0 = const()[name = tensor<string, []>("input_15_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_15_groups_0 = const()[name = tensor<string, []>("input_15_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, ?]> input_15 = conv(bias = noise_convs_0_bias, 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 = noise_convs_0_weight_palettized, x = input_13)[name = tensor<string, []>("input_15")];
+            tensor<fp32, []> var_160 = const()[name = tensor<string, []>("op_160"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
+            tensor<fp32, [1, 512]> h_1 = linear(bias = noise_res_0_adain1_0_fc_bias, weight = noise_res_0_adain1_0_fc_weight_palettized, x = style_timbre)[name = tensor<string, []>("linear_1")];
+            tensor<int32, [3]> var_243 = const()[name = tensor<string, []>("op_243"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp32, [1, 512, 1]> h_3 = reshape(shape = var_243, x = h_1)[name = tensor<string, []>("h_3")];
+            tensor<int32, [2]> var_245_split_sizes_0 = const()[name = tensor<string, []>("op_245_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_245_axis_0 = const()[name = tensor<string, []>("op_245_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, 1]> var_245_0, tensor<fp32, [1, 256, 1]> var_245_1 = split(axis = var_245_axis_0, split_sizes = var_245_split_sizes_0, x = h_3)[name = tensor<string, []>("op_245")];
+            tensor<fp32, []> var_247_promoted = const()[name = tensor<string, []>("op_247_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 256, 1]> var_248 = add(x = var_245_0, y = var_247_promoted)[name = tensor<string, []>("op_248")];
+            tensor<fp32, [1, 256, ?]> var_251 = instance_norm(beta = noise_res_0_adain1_0_norm_bias, epsilon = var_160, gamma = noise_res_0_adain1_0_norm_weight, x = input_15)[name = tensor<string, []>("op_251")];
+            tensor<fp32, [1, 256, ?]> var_252 = mul(x = var_248, y = var_251)[name = tensor<string, []>("op_252")];
+            tensor<fp32, [1, 256, ?]> xt_1 = add(x = var_252, y = var_245_1)[name = tensor<string, []>("xt_1")];
+            tensor<fp32, [1, 256, 1]> var_254 = const()[name = tensor<string, []>("op_254"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(722496)))];
+            tensor<fp32, [1, 256, ?]> var_257 = mul(x = noise_res_0_alpha1_0, y = xt_1)[name = tensor<string, []>("op_257")];
+            tensor<fp32, [1, 256, ?]> var_258 = sin(x = var_257)[name = tensor<string, []>("op_258")];
+            tensor<fp32, []> var_159_promoted = const()[name = tensor<string, []>("op_159_promoted"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 256, ?]> var_259 = pow(x = var_258, y = var_159_promoted)[name = tensor<string, []>("op_259")];
+            tensor<fp32, [1, 256, ?]> var_260 = mul(x = var_254, y = var_259)[name = tensor<string, []>("op_260")];
+            tensor<fp32, [1, 256, ?]> input_17 = add(x = xt_1, y = var_260)[name = tensor<string, []>("input_17")];
+            tensor<fp32, [256, 256, 7]> weight_9_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [458752]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(723584))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1182400))), name = tensor<string, []>("weight_9_palettized"), shape = tensor<uint32, [3]>([256, 256, 7])];
+            tensor<string, []> input_19_pad_type_0 = const()[name = tensor<string, []>("input_19_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> input_19_strides_0 = const()[name = tensor<string, []>("input_19_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_19_dilations_0 = const()[name = tensor<string, []>("input_19_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_19_groups_0 = const()[name = tensor<string, []>("input_19_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, ?]> input_19 = conv(bias = noise_res_0_convs1_0_bias, dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = weight_9_palettized, x = input_17)[name = tensor<string, []>("input_19")];
+            tensor<fp32, [1, 512]> h_5 = linear(bias = noise_res_0_adain2_0_fc_bias, weight = noise_res_0_adain2_0_fc_weight_palettized, x = style_timbre)[name = tensor<string, []>("linear_2")];
+            tensor<int32, [3]> var_276 = const()[name = tensor<string, []>("op_276"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp32, [1, 512, 1]> h_7 = reshape(shape = var_276, x = h_5)[name = tensor<string, []>("h_7")];
+            tensor<int32, [2]> var_278_split_sizes_0 = const()[name = tensor<string, []>("op_278_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_278_axis_0 = const()[name = tensor<string, []>("op_278_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, 1]> var_278_0, tensor<fp32, [1, 256, 1]> var_278_1 = split(axis = var_278_axis_0, split_sizes = var_278_split_sizes_0, x = h_7)[name = tensor<string, []>("op_278")];
+            tensor<fp32, []> var_280_promoted = const()[name = tensor<string, []>("op_280_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 256, 1]> var_281 = add(x = var_278_0, y = var_280_promoted)[name = tensor<string, []>("op_281")];
+            tensor<fp32, [1, 256, ?]> var_284 = instance_norm(beta = noise_res_0_adain1_0_norm_bias, epsilon = var_160, gamma = noise_res_0_adain1_0_norm_weight, x = input_19)[name = tensor<string, []>("op_284")];
+            tensor<fp32, [1, 256, ?]> var_285 = mul(x = var_281, y = var_284)[name = tensor<string, []>("op_285")];
+            tensor<fp32, [1, 256, ?]> xt_3 = add(x = var_285, y = var_278_1)[name = tensor<string, []>("xt_3")];
+            tensor<fp32, [1, 256, 1]> var_287 = const()[name = tensor<string, []>("op_287"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1183488)))];
+            tensor<fp32, [1, 256, ?]> var_290 = mul(x = noise_res_0_alpha2_0, y = xt_3)[name = tensor<string, []>("op_290")];
+            tensor<fp32, [1, 256, ?]> var_291 = sin(x = var_290)[name = tensor<string, []>("op_291")];
+            tensor<fp32, []> var_159_promoted_1 = const()[name = tensor<string, []>("op_159_promoted_1"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 256, ?]> var_292 = pow(x = var_291, y = var_159_promoted_1)[name = tensor<string, []>("op_292")];
+            tensor<fp32, [1, 256, ?]> var_293 = mul(x = var_287, y = var_292)[name = tensor<string, []>("op_293")];
+            tensor<fp32, [1, 256, ?]> input_21 = add(x = xt_3, y = var_293)[name = tensor<string, []>("input_21")];
+            tensor<fp32, [256, 256, 7]> weight_13_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [458752]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1184576))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1643392))), name = tensor<string, []>("weight_13_palettized"), shape = tensor<uint32, [3]>([256, 256, 7])];
+            tensor<string, []> xt_5_pad_type_0 = const()[name = tensor<string, []>("xt_5_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_5_pad_0 = const()[name = tensor<string, []>("xt_5_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> xt_5_strides_0 = const()[name = tensor<string, []>("xt_5_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_5_dilations_0 = const()[name = tensor<string, []>("xt_5_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_5_groups_0 = const()[name = tensor<string, []>("xt_5_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, ?]> xt_5 = conv(bias = noise_res_0_convs2_0_bias, dilations = xt_5_dilations_0, groups = xt_5_groups_0, pad = xt_5_pad_0, pad_type = xt_5_pad_type_0, strides = xt_5_strides_0, weight = weight_13_palettized, x = input_21)[name = tensor<string, []>("xt_5")];
+            tensor<fp32, [1, 256, ?]> input_23 = add(x = xt_5, y = input_15)[name = tensor<string, []>("input_23")];
+            tensor<fp32, [1, 512]> h_9 = linear(bias = noise_res_0_adain1_1_fc_bias, weight = noise_res_0_adain1_1_fc_weight_palettized, x = style_timbre)[name = tensor<string, []>("linear_3")];
+            tensor<int32, [3]> var_310 = const()[name = tensor<string, []>("op_310"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp32, [1, 512, 1]> h_11 = reshape(shape = var_310, x = h_9)[name = tensor<string, []>("h_11")];
+            tensor<int32, [2]> var_312_split_sizes_0 = const()[name = tensor<string, []>("op_312_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_312_axis_0 = const()[name = tensor<string, []>("op_312_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, 1]> var_312_0, tensor<fp32, [1, 256, 1]> var_312_1 = split(axis = var_312_axis_0, split_sizes = var_312_split_sizes_0, x = h_11)[name = tensor<string, []>("op_312")];
+            tensor<fp32, []> var_314_promoted = const()[name = tensor<string, []>("op_314_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 256, 1]> var_315 = add(x = var_312_0, y = var_314_promoted)[name = tensor<string, []>("op_315")];
+            tensor<fp32, [1, 256, ?]> var_318 = instance_norm(beta = noise_res_0_adain1_0_norm_bias, epsilon = var_160, gamma = noise_res_0_adain1_0_norm_weight, x = input_23)[name = tensor<string, []>("op_318")];
+            tensor<fp32, [1, 256, ?]> var_319 = mul(x = var_315, y = var_318)[name = tensor<string, []>("op_319")];
+            tensor<fp32, [1, 256, ?]> xt_7 = add(x = var_319, y = var_312_1)[name = tensor<string, []>("xt_7")];
+            tensor<fp32, [1, 256, 1]> var_321 = const()[name = tensor<string, []>("op_321"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1644480)))];
+            tensor<fp32, [1, 256, ?]> var_324 = mul(x = noise_res_0_alpha1_1, y = xt_7)[name = tensor<string, []>("op_324")];
+            tensor<fp32, [1, 256, ?]> var_325 = sin(x = var_324)[name = tensor<string, []>("op_325")];
+            tensor<fp32, []> var_159_promoted_2 = const()[name = tensor<string, []>("op_159_promoted_2"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 256, ?]> var_326 = pow(x = var_325, y = var_159_promoted_2)[name = tensor<string, []>("op_326")];
+            tensor<fp32, [1, 256, ?]> var_327 = mul(x = var_321, y = var_326)[name = tensor<string, []>("op_327")];
+            tensor<fp32, [1, 256, ?]> input_25 = add(x = xt_7, y = var_327)[name = tensor<string, []>("input_25")];
+            tensor<fp32, [256, 256, 7]> weight_17_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [458752]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1645568))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2104384))), name = tensor<string, []>("weight_17_palettized"), shape = tensor<uint32, [3]>([256, 256, 7])];
+            tensor<string, []> input_27_pad_type_0 = const()[name = tensor<string, []>("input_27_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_27_pad_0 = const()[name = tensor<string, []>("input_27_pad_0"), val = tensor<int32, [2]>([9, 9])];
+            tensor<int32, [1]> input_27_dilations_0 = const()[name = tensor<string, []>("input_27_dilations_0"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> input_27_strides_0 = const()[name = tensor<string, []>("input_27_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_27_groups_0 = const()[name = tensor<string, []>("input_27_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, ?]> input_27 = conv(bias = noise_res_0_convs1_1_bias, 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_17_palettized, x = input_25)[name = tensor<string, []>("input_27")];
+            tensor<fp32, [1, 512]> h_13 = linear(bias = noise_res_0_adain2_1_fc_bias, weight = noise_res_0_adain2_1_fc_weight_palettized, x = style_timbre)[name = tensor<string, []>("linear_4")];
+            tensor<int32, [3]> var_343 = const()[name = tensor<string, []>("op_343"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp32, [1, 512, 1]> h_15 = reshape(shape = var_343, x = h_13)[name = tensor<string, []>("h_15")];
+            tensor<int32, [2]> var_345_split_sizes_0 = const()[name = tensor<string, []>("op_345_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_345_axis_0 = const()[name = tensor<string, []>("op_345_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, 1]> var_345_0, tensor<fp32, [1, 256, 1]> var_345_1 = split(axis = var_345_axis_0, split_sizes = var_345_split_sizes_0, x = h_15)[name = tensor<string, []>("op_345")];
+            tensor<fp32, []> var_347_promoted = const()[name = tensor<string, []>("op_347_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 256, 1]> var_348 = add(x = var_345_0, y = var_347_promoted)[name = tensor<string, []>("op_348")];
+            tensor<fp32, [1, 256, ?]> var_351 = instance_norm(beta = noise_res_0_adain1_0_norm_bias, epsilon = var_160, gamma = noise_res_0_adain1_0_norm_weight, x = input_27)[name = tensor<string, []>("op_351")];
+            tensor<fp32, [1, 256, ?]> var_352 = mul(x = var_348, y = var_351)[name = tensor<string, []>("op_352")];
+            tensor<fp32, [1, 256, ?]> xt_9 = add(x = var_352, y = var_345_1)[name = tensor<string, []>("xt_9")];
+            tensor<fp32, [1, 256, 1]> var_354 = const()[name = tensor<string, []>("op_354"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2105472)))];
+            tensor<fp32, [1, 256, ?]> var_357 = mul(x = noise_res_0_alpha2_1, y = xt_9)[name = tensor<string, []>("op_357")];
+            tensor<fp32, [1, 256, ?]> var_358 = sin(x = var_357)[name = tensor<string, []>("op_358")];
+            tensor<fp32, []> var_159_promoted_3 = const()[name = tensor<string, []>("op_159_promoted_3"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 256, ?]> var_359 = pow(x = var_358, y = var_159_promoted_3)[name = tensor<string, []>("op_359")];
+            tensor<fp32, [1, 256, ?]> var_360 = mul(x = var_354, y = var_359)[name = tensor<string, []>("op_360")];
+            tensor<fp32, [1, 256, ?]> input_29 = add(x = xt_9, y = var_360)[name = tensor<string, []>("input_29")];
+            tensor<fp32, [256, 256, 7]> weight_21_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [458752]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2106560))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2565376))), name = tensor<string, []>("weight_21_palettized"), shape = tensor<uint32, [3]>([256, 256, 7])];
+            tensor<string, []> xt_11_pad_type_0 = const()[name = tensor<string, []>("xt_11_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_11_pad_0 = const()[name = tensor<string, []>("xt_11_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> xt_11_strides_0 = const()[name = tensor<string, []>("xt_11_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_11_dilations_0 = const()[name = tensor<string, []>("xt_11_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_11_groups_0 = const()[name = tensor<string, []>("xt_11_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, ?]> xt_11 = conv(bias = noise_res_0_convs2_1_bias, dilations = xt_11_dilations_0, groups = xt_11_groups_0, pad = xt_11_pad_0, pad_type = xt_11_pad_type_0, strides = xt_11_strides_0, weight = weight_21_palettized, x = input_29)[name = tensor<string, []>("xt_11")];
+            tensor<fp32, [1, 256, ?]> input_31 = add(x = xt_11, y = input_23)[name = tensor<string, []>("input_31")];
+            tensor<fp32, [1, 512]> h_17 = linear(bias = noise_res_0_adain1_2_fc_bias, weight = noise_res_0_adain1_2_fc_weight_palettized, x = style_timbre)[name = tensor<string, []>("linear_5")];
+            tensor<int32, [3]> var_377 = const()[name = tensor<string, []>("op_377"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp32, [1, 512, 1]> h_19 = reshape(shape = var_377, x = h_17)[name = tensor<string, []>("h_19")];
+            tensor<int32, [2]> var_379_split_sizes_0 = const()[name = tensor<string, []>("op_379_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_379_axis_0 = const()[name = tensor<string, []>("op_379_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, 1]> var_379_0, tensor<fp32, [1, 256, 1]> var_379_1 = split(axis = var_379_axis_0, split_sizes = var_379_split_sizes_0, x = h_19)[name = tensor<string, []>("op_379")];
+            tensor<fp32, []> var_381_promoted = const()[name = tensor<string, []>("op_381_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 256, 1]> var_382 = add(x = var_379_0, y = var_381_promoted)[name = tensor<string, []>("op_382")];
+            tensor<fp32, [1, 256, ?]> var_385 = instance_norm(beta = noise_res_0_adain1_0_norm_bias, epsilon = var_160, gamma = noise_res_0_adain1_0_norm_weight, x = input_31)[name = tensor<string, []>("op_385")];
+            tensor<fp32, [1, 256, ?]> var_386 = mul(x = var_382, y = var_385)[name = tensor<string, []>("op_386")];
+            tensor<fp32, [1, 256, ?]> xt_13 = add(x = var_386, y = var_379_1)[name = tensor<string, []>("xt_13")];
+            tensor<fp32, [1, 256, 1]> var_388 = const()[name = tensor<string, []>("op_388"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2566464)))];
+            tensor<fp32, [1, 256, ?]> var_391 = mul(x = noise_res_0_alpha1_2, y = xt_13)[name = tensor<string, []>("op_391")];
+            tensor<fp32, [1, 256, ?]> var_392 = sin(x = var_391)[name = tensor<string, []>("op_392")];
+            tensor<fp32, []> var_159_promoted_4 = const()[name = tensor<string, []>("op_159_promoted_4"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 256, ?]> var_393 = pow(x = var_392, y = var_159_promoted_4)[name = tensor<string, []>("op_393")];
+            tensor<fp32, [1, 256, ?]> var_394 = mul(x = var_388, y = var_393)[name = tensor<string, []>("op_394")];
+            tensor<fp32, [1, 256, ?]> input_33 = add(x = xt_13, y = var_394)[name = tensor<string, []>("input_33")];
+            tensor<fp32, [256, 256, 7]> weight_25_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [458752]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2567552))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3026368))), name = tensor<string, []>("weight_25_palettized"), shape = tensor<uint32, [3]>([256, 256, 7])];
+            tensor<string, []> input_35_pad_type_0 = const()[name = tensor<string, []>("input_35_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_35_pad_0 = const()[name = tensor<string, []>("input_35_pad_0"), val = tensor<int32, [2]>([15, 15])];
+            tensor<int32, [1]> input_35_dilations_0 = const()[name = tensor<string, []>("input_35_dilations_0"), val = tensor<int32, [1]>([5])];
+            tensor<int32, [1]> input_35_strides_0 = const()[name = tensor<string, []>("input_35_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_35_groups_0 = const()[name = tensor<string, []>("input_35_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, ?]> input_35 = conv(bias = noise_res_0_convs1_2_bias, 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_25_palettized, x = input_33)[name = tensor<string, []>("input_35")];
+            tensor<fp32, [1, 512]> h_21 = linear(bias = noise_res_0_adain2_2_fc_bias, weight = noise_res_0_adain2_2_fc_weight_palettized, x = style_timbre)[name = tensor<string, []>("linear_6")];
+            tensor<int32, [3]> var_410 = const()[name = tensor<string, []>("op_410"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp32, [1, 512, 1]> h_23 = reshape(shape = var_410, x = h_21)[name = tensor<string, []>("h_23")];
+            tensor<int32, [2]> var_412_split_sizes_0 = const()[name = tensor<string, []>("op_412_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_412_axis_0 = const()[name = tensor<string, []>("op_412_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, 1]> var_412_0, tensor<fp32, [1, 256, 1]> var_412_1 = split(axis = var_412_axis_0, split_sizes = var_412_split_sizes_0, x = h_23)[name = tensor<string, []>("op_412")];
+            tensor<fp32, []> var_414_promoted = const()[name = tensor<string, []>("op_414_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 256, 1]> var_415 = add(x = var_412_0, y = var_414_promoted)[name = tensor<string, []>("op_415")];
+            tensor<fp32, [1, 256, ?]> var_418 = instance_norm(beta = noise_res_0_adain1_0_norm_bias, epsilon = var_160, gamma = noise_res_0_adain1_0_norm_weight, x = input_35)[name = tensor<string, []>("op_418")];
+            tensor<fp32, [1, 256, ?]> var_419 = mul(x = var_415, y = var_418)[name = tensor<string, []>("op_419")];
+            tensor<fp32, [1, 256, ?]> xt_15 = add(x = var_419, y = var_412_1)[name = tensor<string, []>("xt_15")];
+            tensor<fp32, [1, 256, 1]> var_421 = const()[name = tensor<string, []>("op_421"), val = tensor<fp32, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3027456)))];
+            tensor<fp32, [1, 256, ?]> var_424 = mul(x = noise_res_0_alpha2_2, y = xt_15)[name = tensor<string, []>("op_424")];
+            tensor<fp32, [1, 256, ?]> var_425 = sin(x = var_424)[name = tensor<string, []>("op_425")];
+            tensor<fp32, []> var_159_promoted_5 = const()[name = tensor<string, []>("op_159_promoted_5"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 256, ?]> var_426 = pow(x = var_425, y = var_159_promoted_5)[name = tensor<string, []>("op_426")];
+            tensor<fp32, [1, 256, ?]> var_427 = mul(x = var_421, y = var_426)[name = tensor<string, []>("op_427")];
+            tensor<fp32, [1, 256, ?]> input_37 = add(x = xt_15, y = var_427)[name = tensor<string, []>("input_37")];
+            tensor<fp32, [256, 256, 7]> weight_29_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [458752]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3028544))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3487360))), name = tensor<string, []>("weight_29_palettized"), shape = tensor<uint32, [3]>([256, 256, 7])];
+            tensor<string, []> xt_17_pad_type_0 = const()[name = tensor<string, []>("xt_17_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_17_pad_0 = const()[name = tensor<string, []>("xt_17_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> xt_17_strides_0 = const()[name = tensor<string, []>("xt_17_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_17_dilations_0 = const()[name = tensor<string, []>("xt_17_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_17_groups_0 = const()[name = tensor<string, []>("xt_17_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 256, ?]> xt_17 = conv(bias = noise_res_0_convs2_2_bias, dilations = xt_17_dilations_0, groups = xt_17_groups_0, pad = xt_17_pad_0, pad_type = xt_17_pad_type_0, strides = xt_17_strides_0, weight = weight_29_palettized, x = input_37)[name = tensor<string, []>("xt_17")];
+            tensor<fp32, [1, 256, ?]> x_source_0 = add(x = xt_17, y = input_31)[name = tensor<string, []>("op_436")];
+            tensor<string, []> input_39_pad_type_0 = const()[name = tensor<string, []>("input_39_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_39_strides_0 = const()[name = tensor<string, []>("input_39_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_39_pad_0 = const()[name = tensor<string, []>("input_39_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_39_dilations_0 = const()[name = tensor<string, []>("input_39_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_39_groups_0 = const()[name = tensor<string, []>("input_39_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 128, ?]> input_39 = conv(bias = noise_convs_1_bias, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = noise_convs_1_weight_palettized, x = input_13)[name = tensor<string, []>("input_39")];
+            tensor<fp32, []> var_456 = const()[name = tensor<string, []>("op_456"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
+            tensor<fp32, [1, 256]> h_25 = linear(bias = noise_res_1_adain1_0_fc_bias, weight = noise_res_1_adain1_0_fc_weight_palettized, x = style_timbre)[name = tensor<string, []>("linear_7")];
+            tensor<int32, [3]> var_539 = const()[name = tensor<string, []>("op_539"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp32, [1, 256, 1]> h_27 = reshape(shape = var_539, x = h_25)[name = tensor<string, []>("h_27")];
+            tensor<int32, [2]> var_541_split_sizes_0 = const()[name = tensor<string, []>("op_541_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_541_axis_0 = const()[name = tensor<string, []>("op_541_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 128, 1]> var_541_0, tensor<fp32, [1, 128, 1]> var_541_1 = split(axis = var_541_axis_0, split_sizes = var_541_split_sizes_0, x = h_27)[name = tensor<string, []>("op_541")];
+            tensor<fp32, []> var_543_promoted = const()[name = tensor<string, []>("op_543_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 128, 1]> var_544 = add(x = var_541_0, y = var_543_promoted)[name = tensor<string, []>("op_544")];
+            tensor<fp32, [1, 128, ?]> var_547 = instance_norm(beta = noise_res_1_adain1_0_norm_bias, epsilon = var_456, gamma = noise_res_1_adain1_0_norm_weight, x = input_39)[name = tensor<string, []>("op_547")];
+            tensor<fp32, [1, 128, ?]> var_548 = mul(x = var_544, y = var_547)[name = tensor<string, []>("op_548")];
+            tensor<fp32, [1, 128, ?]> xt_19 = add(x = var_548, y = var_541_1)[name = tensor<string, []>("xt_19")];
+            tensor<fp32, [1, 128, 1]> var_550 = const()[name = tensor<string, []>("op_550"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3488448)))];
+            tensor<fp32, [1, 128, ?]> var_553 = mul(x = noise_res_1_alpha1_0, y = xt_19)[name = tensor<string, []>("op_553")];
+            tensor<fp32, [1, 128, ?]> var_554 = sin(x = var_553)[name = tensor<string, []>("op_554")];
+            tensor<fp32, []> var_455_promoted = const()[name = tensor<string, []>("op_455_promoted"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 128, ?]> var_555 = pow(x = var_554, y = var_455_promoted)[name = tensor<string, []>("op_555")];
+            tensor<fp32, [1, 128, ?]> var_556 = mul(x = var_550, y = var_555)[name = tensor<string, []>("op_556")];
+            tensor<fp32, [1, 128, ?]> input_41 = add(x = xt_19, y = var_556)[name = tensor<string, []>("input_41")];
+            tensor<fp32, [128, 128, 11]> weight_35_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [180224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3489024))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3669312))), name = tensor<string, []>("weight_35_palettized"), shape = tensor<uint32, [3]>([128, 128, 11])];
+            tensor<string, []> input_43_pad_type_0 = const()[name = tensor<string, []>("input_43_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_43_pad_0 = const()[name = tensor<string, []>("input_43_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> input_43_strides_0 = const()[name = tensor<string, []>("input_43_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_43_dilations_0 = const()[name = tensor<string, []>("input_43_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_43_groups_0 = const()[name = tensor<string, []>("input_43_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 128, ?]> input_43 = conv(bias = noise_res_1_convs1_0_bias, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = weight_35_palettized, x = input_41)[name = tensor<string, []>("input_43")];
+            tensor<fp32, [1, 256]> h_29 = linear(bias = noise_res_1_adain2_0_fc_bias, weight = noise_res_1_adain2_0_fc_weight_palettized, x = style_timbre)[name = tensor<string, []>("linear_8")];
+            tensor<int32, [3]> var_572 = const()[name = tensor<string, []>("op_572"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp32, [1, 256, 1]> h_31 = reshape(shape = var_572, x = h_29)[name = tensor<string, []>("h_31")];
+            tensor<int32, [2]> var_574_split_sizes_0 = const()[name = tensor<string, []>("op_574_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_574_axis_0 = const()[name = tensor<string, []>("op_574_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 128, 1]> var_574_0, tensor<fp32, [1, 128, 1]> var_574_1 = split(axis = var_574_axis_0, split_sizes = var_574_split_sizes_0, x = h_31)[name = tensor<string, []>("op_574")];
+            tensor<fp32, []> var_576_promoted = const()[name = tensor<string, []>("op_576_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 128, 1]> var_577 = add(x = var_574_0, y = var_576_promoted)[name = tensor<string, []>("op_577")];
+            tensor<fp32, [1, 128, ?]> var_580 = instance_norm(beta = noise_res_1_adain1_0_norm_bias, epsilon = var_456, gamma = noise_res_1_adain1_0_norm_weight, x = input_43)[name = tensor<string, []>("op_580")];
+            tensor<fp32, [1, 128, ?]> var_581 = mul(x = var_577, y = var_580)[name = tensor<string, []>("op_581")];
+            tensor<fp32, [1, 128, ?]> xt_21 = add(x = var_581, y = var_574_1)[name = tensor<string, []>("xt_21")];
+            tensor<fp32, [1, 128, 1]> var_583 = const()[name = tensor<string, []>("op_583"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3670400)))];
+            tensor<fp32, [1, 128, ?]> var_586 = mul(x = noise_res_1_alpha2_0, y = xt_21)[name = tensor<string, []>("op_586")];
+            tensor<fp32, [1, 128, ?]> var_587 = sin(x = var_586)[name = tensor<string, []>("op_587")];
+            tensor<fp32, []> var_455_promoted_1 = const()[name = tensor<string, []>("op_455_promoted_1"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 128, ?]> var_588 = pow(x = var_587, y = var_455_promoted_1)[name = tensor<string, []>("op_588")];
+            tensor<fp32, [1, 128, ?]> var_589 = mul(x = var_583, y = var_588)[name = tensor<string, []>("op_589")];
+            tensor<fp32, [1, 128, ?]> input_45 = add(x = xt_21, y = var_589)[name = tensor<string, []>("input_45")];
+            tensor<fp32, [128, 128, 11]> weight_39_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [180224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3670976))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3851264))), name = tensor<string, []>("weight_39_palettized"), shape = tensor<uint32, [3]>([128, 128, 11])];
+            tensor<string, []> xt_23_pad_type_0 = const()[name = tensor<string, []>("xt_23_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_23_pad_0 = const()[name = tensor<string, []>("xt_23_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> xt_23_strides_0 = const()[name = tensor<string, []>("xt_23_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_23_dilations_0 = const()[name = tensor<string, []>("xt_23_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_23_groups_0 = const()[name = tensor<string, []>("xt_23_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 128, ?]> xt_23 = conv(bias = noise_res_1_convs2_0_bias, dilations = xt_23_dilations_0, groups = xt_23_groups_0, pad = xt_23_pad_0, pad_type = xt_23_pad_type_0, strides = xt_23_strides_0, weight = weight_39_palettized, x = input_45)[name = tensor<string, []>("xt_23")];
+            tensor<fp32, [1, 128, ?]> input_47 = add(x = xt_23, y = input_39)[name = tensor<string, []>("input_47")];
+            tensor<fp32, [1, 256]> h_33 = linear(bias = noise_res_1_adain1_1_fc_bias, weight = noise_res_1_adain1_1_fc_weight_palettized, x = style_timbre)[name = tensor<string, []>("linear_9")];
+            tensor<int32, [3]> var_606 = const()[name = tensor<string, []>("op_606"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp32, [1, 256, 1]> h_35 = reshape(shape = var_606, x = h_33)[name = tensor<string, []>("h_35")];
+            tensor<int32, [2]> var_608_split_sizes_0 = const()[name = tensor<string, []>("op_608_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_608_axis_0 = const()[name = tensor<string, []>("op_608_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 128, 1]> var_608_0, tensor<fp32, [1, 128, 1]> var_608_1 = split(axis = var_608_axis_0, split_sizes = var_608_split_sizes_0, x = h_35)[name = tensor<string, []>("op_608")];
+            tensor<fp32, []> var_610_promoted = const()[name = tensor<string, []>("op_610_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 128, 1]> var_611 = add(x = var_608_0, y = var_610_promoted)[name = tensor<string, []>("op_611")];
+            tensor<fp32, [1, 128, ?]> var_614 = instance_norm(beta = noise_res_1_adain1_0_norm_bias, epsilon = var_456, gamma = noise_res_1_adain1_0_norm_weight, x = input_47)[name = tensor<string, []>("op_614")];
+            tensor<fp32, [1, 128, ?]> var_615 = mul(x = var_611, y = var_614)[name = tensor<string, []>("op_615")];
+            tensor<fp32, [1, 128, ?]> xt_25 = add(x = var_615, y = var_608_1)[name = tensor<string, []>("xt_25")];
+            tensor<fp32, [1, 128, 1]> var_617 = const()[name = tensor<string, []>("op_617"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3852352)))];
+            tensor<fp32, [1, 128, ?]> var_620 = mul(x = noise_res_1_alpha1_1, y = xt_25)[name = tensor<string, []>("op_620")];
+            tensor<fp32, [1, 128, ?]> var_621 = sin(x = var_620)[name = tensor<string, []>("op_621")];
+            tensor<fp32, []> var_455_promoted_2 = const()[name = tensor<string, []>("op_455_promoted_2"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 128, ?]> var_622 = pow(x = var_621, y = var_455_promoted_2)[name = tensor<string, []>("op_622")];
+            tensor<fp32, [1, 128, ?]> var_623 = mul(x = var_617, y = var_622)[name = tensor<string, []>("op_623")];
+            tensor<fp32, [1, 128, ?]> input_49 = add(x = xt_25, y = var_623)[name = tensor<string, []>("input_49")];
+            tensor<fp32, [128, 128, 11]> weight_43_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [180224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3852928))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4033216))), name = tensor<string, []>("weight_43_palettized"), shape = tensor<uint32, [3]>([128, 128, 11])];
+            tensor<string, []> input_51_pad_type_0 = const()[name = tensor<string, []>("input_51_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_51_pad_0 = const()[name = tensor<string, []>("input_51_pad_0"), val = tensor<int32, [2]>([15, 15])];
+            tensor<int32, [1]> input_51_dilations_0 = const()[name = tensor<string, []>("input_51_dilations_0"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> input_51_strides_0 = const()[name = tensor<string, []>("input_51_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_51_groups_0 = const()[name = tensor<string, []>("input_51_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 128, ?]> input_51 = conv(bias = noise_res_1_convs1_1_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_43_palettized, x = input_49)[name = tensor<string, []>("input_51")];
+            tensor<fp32, [1, 256]> h_37 = linear(bias = noise_res_1_adain2_1_fc_bias, weight = noise_res_1_adain2_1_fc_weight_palettized, x = style_timbre)[name = tensor<string, []>("linear_10")];
+            tensor<int32, [3]> var_639 = const()[name = tensor<string, []>("op_639"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp32, [1, 256, 1]> h_39 = reshape(shape = var_639, x = h_37)[name = tensor<string, []>("h_39")];
+            tensor<int32, [2]> var_641_split_sizes_0 = const()[name = tensor<string, []>("op_641_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_641_axis_0 = const()[name = tensor<string, []>("op_641_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 128, 1]> var_641_0, tensor<fp32, [1, 128, 1]> var_641_1 = split(axis = var_641_axis_0, split_sizes = var_641_split_sizes_0, x = h_39)[name = tensor<string, []>("op_641")];
+            tensor<fp32, []> var_643_promoted = const()[name = tensor<string, []>("op_643_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 128, 1]> var_644 = add(x = var_641_0, y = var_643_promoted)[name = tensor<string, []>("op_644")];
+            tensor<fp32, [1, 128, ?]> var_647 = instance_norm(beta = noise_res_1_adain1_0_norm_bias, epsilon = var_456, gamma = noise_res_1_adain1_0_norm_weight, x = input_51)[name = tensor<string, []>("op_647")];
+            tensor<fp32, [1, 128, ?]> var_648 = mul(x = var_644, y = var_647)[name = tensor<string, []>("op_648")];
+            tensor<fp32, [1, 128, ?]> xt_27 = add(x = var_648, y = var_641_1)[name = tensor<string, []>("xt_27")];
+            tensor<fp32, [1, 128, 1]> var_650 = const()[name = tensor<string, []>("op_650"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4034304)))];
+            tensor<fp32, [1, 128, ?]> var_653 = mul(x = noise_res_1_alpha2_1, y = xt_27)[name = tensor<string, []>("op_653")];
+            tensor<fp32, [1, 128, ?]> var_654 = sin(x = var_653)[name = tensor<string, []>("op_654")];
+            tensor<fp32, []> var_455_promoted_3 = const()[name = tensor<string, []>("op_455_promoted_3"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 128, ?]> var_655 = pow(x = var_654, y = var_455_promoted_3)[name = tensor<string, []>("op_655")];
+            tensor<fp32, [1, 128, ?]> var_656 = mul(x = var_650, y = var_655)[name = tensor<string, []>("op_656")];
+            tensor<fp32, [1, 128, ?]> input_53 = add(x = xt_27, y = var_656)[name = tensor<string, []>("input_53")];
+            tensor<fp32, [128, 128, 11]> weight_47_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [180224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4034880))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4215168))), name = tensor<string, []>("weight_47_palettized"), shape = tensor<uint32, [3]>([128, 128, 11])];
+            tensor<string, []> xt_29_pad_type_0 = const()[name = tensor<string, []>("xt_29_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_29_pad_0 = const()[name = tensor<string, []>("xt_29_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> xt_29_strides_0 = const()[name = tensor<string, []>("xt_29_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_29_dilations_0 = const()[name = tensor<string, []>("xt_29_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_29_groups_0 = const()[name = tensor<string, []>("xt_29_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 128, ?]> xt_29 = conv(bias = noise_res_1_convs2_1_bias, dilations = xt_29_dilations_0, groups = xt_29_groups_0, pad = xt_29_pad_0, pad_type = xt_29_pad_type_0, strides = xt_29_strides_0, weight = weight_47_palettized, x = input_53)[name = tensor<string, []>("xt_29")];
+            tensor<fp32, [1, 128, ?]> input_55 = add(x = xt_29, y = input_47)[name = tensor<string, []>("input_55")];
+            tensor<fp32, [1, 256]> h_41 = linear(bias = noise_res_1_adain1_2_fc_bias, weight = noise_res_1_adain1_2_fc_weight_palettized, x = style_timbre)[name = tensor<string, []>("linear_11")];
+            tensor<int32, [3]> var_673 = const()[name = tensor<string, []>("op_673"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp32, [1, 256, 1]> h_43 = reshape(shape = var_673, x = h_41)[name = tensor<string, []>("h_43")];
+            tensor<int32, [2]> var_675_split_sizes_0 = const()[name = tensor<string, []>("op_675_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_675_axis_0 = const()[name = tensor<string, []>("op_675_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 128, 1]> var_675_0, tensor<fp32, [1, 128, 1]> var_675_1 = split(axis = var_675_axis_0, split_sizes = var_675_split_sizes_0, x = h_43)[name = tensor<string, []>("op_675")];
+            tensor<fp32, []> var_677_promoted = const()[name = tensor<string, []>("op_677_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 128, 1]> var_678 = add(x = var_675_0, y = var_677_promoted)[name = tensor<string, []>("op_678")];
+            tensor<fp32, [1, 128, ?]> var_681 = instance_norm(beta = noise_res_1_adain1_0_norm_bias, epsilon = var_456, gamma = noise_res_1_adain1_0_norm_weight, x = input_55)[name = tensor<string, []>("op_681")];
+            tensor<fp32, [1, 128, ?]> var_682 = mul(x = var_678, y = var_681)[name = tensor<string, []>("op_682")];
+            tensor<fp32, [1, 128, ?]> xt_31 = add(x = var_682, y = var_675_1)[name = tensor<string, []>("xt_31")];
+            tensor<fp32, [1, 128, 1]> var_684 = const()[name = tensor<string, []>("op_684"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4216256)))];
+            tensor<fp32, [1, 128, ?]> var_687 = mul(x = noise_res_1_alpha1_2, y = xt_31)[name = tensor<string, []>("op_687")];
+            tensor<fp32, [1, 128, ?]> var_688 = sin(x = var_687)[name = tensor<string, []>("op_688")];
+            tensor<fp32, []> var_455_promoted_4 = const()[name = tensor<string, []>("op_455_promoted_4"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 128, ?]> var_689 = pow(x = var_688, y = var_455_promoted_4)[name = tensor<string, []>("op_689")];
+            tensor<fp32, [1, 128, ?]> var_690 = mul(x = var_684, y = var_689)[name = tensor<string, []>("op_690")];
+            tensor<fp32, [1, 128, ?]> input_57 = add(x = xt_31, y = var_690)[name = tensor<string, []>("input_57")];
+            tensor<fp32, [128, 128, 11]> weight_51_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [180224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4216832))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4397120))), name = tensor<string, []>("weight_51_palettized"), shape = tensor<uint32, [3]>([128, 128, 11])];
+            tensor<string, []> input_59_pad_type_0 = const()[name = tensor<string, []>("input_59_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_59_pad_0 = const()[name = tensor<string, []>("input_59_pad_0"), val = tensor<int32, [2]>([25, 25])];
+            tensor<int32, [1]> input_59_dilations_0 = const()[name = tensor<string, []>("input_59_dilations_0"), val = tensor<int32, [1]>([5])];
+            tensor<int32, [1]> input_59_strides_0 = const()[name = tensor<string, []>("input_59_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_59_groups_0 = const()[name = tensor<string, []>("input_59_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 128, ?]> input_59 = conv(bias = noise_res_1_convs1_2_bias, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = weight_51_palettized, x = input_57)[name = tensor<string, []>("input_59")];
+            tensor<fp32, [1, 256]> h_45 = linear(bias = noise_res_1_adain2_2_fc_bias, weight = noise_res_1_adain2_2_fc_weight_palettized, x = style_timbre)[name = tensor<string, []>("linear_12")];
+            tensor<int32, [3]> var_706 = const()[name = tensor<string, []>("op_706"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp32, [1, 256, 1]> h = reshape(shape = var_706, x = h_45)[name = tensor<string, []>("h")];
+            tensor<int32, [2]> var_708_split_sizes_0 = const()[name = tensor<string, []>("op_708_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_708_axis_0 = const()[name = tensor<string, []>("op_708_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 128, 1]> var_708_0, tensor<fp32, [1, 128, 1]> var_708_1 = split(axis = var_708_axis_0, split_sizes = var_708_split_sizes_0, x = h)[name = tensor<string, []>("op_708")];
+            tensor<fp32, []> var_710_promoted = const()[name = tensor<string, []>("op_710_promoted"), val = tensor<fp32, []>(0x1p+0)];
+            tensor<fp32, [1, 128, 1]> var_711 = add(x = var_708_0, y = var_710_promoted)[name = tensor<string, []>("op_711")];
+            tensor<fp32, [1, 128, ?]> var_714 = instance_norm(beta = noise_res_1_adain1_0_norm_bias, epsilon = var_456, gamma = noise_res_1_adain1_0_norm_weight, x = input_59)[name = tensor<string, []>("op_714")];
+            tensor<fp32, [1, 128, ?]> var_715 = mul(x = var_711, y = var_714)[name = tensor<string, []>("op_715")];
+            tensor<fp32, [1, 128, ?]> xt_33 = add(x = var_715, y = var_708_1)[name = tensor<string, []>("xt_33")];
+            tensor<fp32, [1, 128, 1]> var_717 = const()[name = tensor<string, []>("op_717"), val = tensor<fp32, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4398208)))];
+            tensor<fp32, [1, 128, ?]> var_720 = mul(x = noise_res_1_alpha2_2, y = xt_33)[name = tensor<string, []>("op_720")];
+            tensor<fp32, [1, 128, ?]> var_721 = sin(x = var_720)[name = tensor<string, []>("op_721")];
+            tensor<fp32, []> var_455_promoted_5 = const()[name = tensor<string, []>("op_455_promoted_5"), val = tensor<fp32, []>(0x1p+1)];
+            tensor<fp32, [1, 128, ?]> var_722 = pow(x = var_721, y = var_455_promoted_5)[name = tensor<string, []>("op_722")];
+            tensor<fp32, [1, 128, ?]> var_723 = mul(x = var_717, y = var_722)[name = tensor<string, []>("op_723")];
+            tensor<fp32, [1, 128, ?]> input = add(x = xt_33, y = var_723)[name = tensor<string, []>("input")];
+            tensor<fp32, [128, 128, 11]> weight_55_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [180224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4398784))), lut = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4579072))), name = tensor<string, []>("weight_55_palettized"), shape = tensor<uint32, [3]>([128, 128, 11])];
+            tensor<string, []> xt_pad_type_0 = const()[name = tensor<string, []>("xt_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_pad_0 = const()[name = tensor<string, []>("xt_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> xt_strides_0 = const()[name = tensor<string, []>("xt_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_dilations_0 = const()[name = tensor<string, []>("xt_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_groups_0 = const()[name = tensor<string, []>("xt_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 128, ?]> xt = conv(bias = noise_res_1_convs2_2_bias, dilations = xt_dilations_0, groups = xt_groups_0, pad = xt_pad_0, pad_type = xt_pad_type_0, strides = xt_strides_0, weight = weight_55_palettized, x = input)[name = tensor<string, []>("xt")];
+            tensor<fp32, [1, 128, ?]> x_source_1 = add(x = xt, y = input_55)[name = tensor<string, []>("op_732")];
+        } -> (x_source_0, x_source_1);
+}
\ No newline at end of file
diff --git a/ANE/ANE-zh/KokoroNoise.mlmodelc/weights/weight.bin b/ANE/ANE-zh/KokoroNoise.mlmodelc/weights/weight.bin
new file mode 100644
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+++ b/ANE/ANE-zh/KokoroNoise.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e1a1edc7ac86b9899cb908d6e4e65f3def26ee7720d8730444bee29385596ab5
+size 4580160
diff --git a/ANE/ANE-zh/KokoroNoise.mlpackage/Data/com.apple.CoreML/model.mlmodel b/ANE/ANE-zh/KokoroNoise.mlpackage/Data/com.apple.CoreML/model.mlmodel
new file mode 100644
index 0000000000000000000000000000000000000000..936606a51711a82bc8d1ac7f42f2127b6f304fc5
--- /dev/null
+++ b/ANE/ANE-zh/KokoroNoise.mlpackage/Data/com.apple.CoreML/model.mlmodel
@@ -0,0 +1,3 @@
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+oid sha256:8674162d48113407da93aeee21d7c2433738494bd3662094ba0249b8d0445e9b
+size 74960
diff --git a/ANE/ANE-zh/KokoroNoise.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/ANE/ANE-zh/KokoroNoise.mlpackage/Data/com.apple.CoreML/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..74ed9a3852eada8b80dc4cb1270f26ef3bd8a786
--- /dev/null
+++ b/ANE/ANE-zh/KokoroNoise.mlpackage/Data/com.apple.CoreML/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e1a1edc7ac86b9899cb908d6e4e65f3def26ee7720d8730444bee29385596ab5
+size 4580160
diff --git a/ANE/ANE-zh/KokoroNoise.mlpackage/Manifest.json b/ANE/ANE-zh/KokoroNoise.mlpackage/Manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..751c265e55378565a6099ce1b2e45656851b5e5d
--- /dev/null
+++ b/ANE/ANE-zh/KokoroNoise.mlpackage/Manifest.json
@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "510CA943-DA71-4F61-A81C-5534318B48C9": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "E69B7771-1977-4192-A042-A8C3807DC461": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "E69B7771-1977-4192-A042-A8C3807DC461"
+}
diff --git a/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/analytics/coremldata.bin b/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/analytics/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..d16ac9e965a22dbc41b77fd3c548355c1f0994f6
--- /dev/null
+++ b/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
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+oid sha256:9ac50dad158a0dc349a240675d84ad920ae3202f282fc70fb2b833d78eb6829f
+size 243
diff --git a/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/coremldata.bin b/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..38db38f39a3e10bd2e4e5b856e5bccbc67e8a6b5
--- /dev/null
+++ b/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9246412170c6118149ccf412efafbb76db5bd47946776a1897b046ca2576f95
+size 556
diff --git a/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/metadata.json b/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..caf0d1374e0610313f0129ca5b3f2d2988f86d2b
--- /dev/null
+++ b/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/metadata.json
@@ -0,0 +1,148 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Mixed (Float16, Palettized (8 bits))",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "duration",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "d",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "t_en",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios17.equal" : 2,
+      "Ios17.reshape" : 3,
+      "Ios17.transpose" : 30,
+      "Split" : 3,
+      "Select" : 10,
+      "Ios17.expandDims" : 3,
+      "Ios17.add" : 6,
+      "Tile" : 1,
+      "Ios16.sigmoid" : 1,
+      "Ios16.reduceSum" : 1,
+      "Shape" : 1,
+      "Ios17.gather" : 2,
+      "Ios17.layerNorm" : 6,
+      "Ios17.lstm" : 5,
+      "Ios17.cast" : 3,
+      "Ios16.constexprLutToDense" : 29,
+      "Ios17.conv" : 3,
+      "Ios17.realDiv" : 2,
+      "Ios17.linear" : 5,
+      "Ios17.concat" : 5,
+      "Ios17.leakyRelu" : 3,
+      "Ios17.mul" : 3
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int16, Int32, UInt16)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-05-03",
+      "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" : "Float16",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 2...512 × 768",
+        "shapeRange" : "[[1, 1], [2, 512], [768, 768]]",
+        "formattedType" : "MultiArray (Float16 1 × 37 × 768)",
+        "type" : "MultiArray",
+        "shape" : "[1, 37, 768]",
+        "name" : "bert_dur",
+        "shortDescription" : ""
+      },
+      {
+        "dataType" : "Int32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 2...512",
+        "shapeRange" : "[[1, 1], [2, 512]]",
+        "formattedType" : "MultiArray (Int32 1 × 37)",
+        "type" : "MultiArray",
+        "shape" : "[1, 37]",
+        "name" : "input_ids",
+        "shortDescription" : ""
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 128)",
+        "shortDescription" : "",
+        "shape" : "[1, 128]",
+        "name" : "style_s",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "speed",
+        "type" : "MultiArray"
+      },
+      {
+        "dataType" : "Int32",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 2...512",
+        "shapeRange" : "[[1, 1], [2, 512]]",
+        "formattedType" : "MultiArray (Int32 1 × 37)",
+        "type" : "MultiArray",
+        "shape" : "[1, 37]",
+        "name" : "attention_mask",
+        "shortDescription" : ""
+      }
+    ],
+    "generatedClassName" : "KokoroPostAlbert",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/model.mil b/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..4bcc31fcb162052e543da9fa60814af82f874963
--- /dev/null
+++ b/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/model.mil
@@ -0,0 +1,277 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})]
+{
+    func main<ios17>(tensor<int32, [1, ?]> attention_mask, tensor<fp16, [1, ?, 768]> bert_dur, tensor<int32, [1, ?]> input_ids, tensor<fp16, [1]> speed, tensor<fp16, [1, 128]> style_s) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"attention_mask", [1, 37]}, {"bert_dur", [1, 37, 768]}, {"input_ids", [1, 37]}}), ("RangeDims", {{"attention_mask", [[1, 1], [2, 512]]}, {"bert_dur", [[1, 1], [2, 512], [768, 768]]}, {"input_ids", [[1, 1], [2, 512]]}})))] {
+            tensor<int32, []> var_11 = const()[name = tensor<string, []>("op_11"), val = tensor<int32, []>(0)];
+            tensor<bool, [1, ?]> m = equal(x = attention_mask, y = var_11)[name = tensor<string, []>("m")];
+            tensor<fp16, [512, 768]> bert_encoder_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [393216]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(393344))), name = tensor<string, []>("bert_encoder_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 768])];
+            tensor<fp16, [512]> bert_encoder_bias_to_fp16 = const()[name = tensor<string, []>("bert_encoder_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(393920)))];
+            tensor<fp16, [1, ?, 512]> linear_0_cast_fp16 = linear(bias = bert_encoder_bias_to_fp16, weight = bert_encoder_weight_to_fp16_palettized, x = bert_dur)[name = tensor<string, []>("linear_0_cast_fp16")];
+            tensor<int32, [3]> transpose_6_perm_0 = const()[name = tensor<string, []>("transpose_6_perm_0"), val = tensor<int32, [3]>([-2, 0, -1])];
+            tensor<int32, []> var_30 = const()[name = tensor<string, []>("op_30"), val = tensor<int32, []>(-1)];
+            tensor<int32, []> var_31 = const()[name = tensor<string, []>("op_31"), val = tensor<int32, []>(1)];
+            tensor<fp16, [?, 1, 512]> transpose_6_cast_fp16 = transpose(perm = transpose_6_perm_0, x = linear_0_cast_fp16)[name = tensor<string, []>("transpose_47")];
+            tensor<int32, [3]> var_48_shape_cast_fp16 = shape(x = transpose_6_cast_fp16)[name = tensor<string, []>("op_48_shape_cast_fp16")];
+            tensor<int32, []> gather_0_axis_0 = const()[name = tensor<string, []>("gather_0_axis_0"), val = tensor<int32, []>(0)];
+            tensor<int32, []> gather_0_batch_dims_0 = const()[name = tensor<string, []>("gather_0_batch_dims_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> gather_0_validate_indices_0 = const()[name = tensor<string, []>("gather_0_validate_indices_0"), val = tensor<bool, []>(false)];
+            tensor<string, []> var_48_shape_cast_fp16_to_int16_dtype_0 = const()[name = tensor<string, []>("op_48_shape_cast_fp16_to_int16_dtype_0"), val = tensor<string, []>("int16")];
+            tensor<uint16, []> gather_0_indices_0_to_uint16 = const()[name = tensor<string, []>("gather_0_indices_0_to_uint16"), val = tensor<uint16, []>(0)];
+            tensor<int16, [3]> var_48_shape_cast_fp16_to_int16 = cast(dtype = var_48_shape_cast_fp16_to_int16_dtype_0, x = var_48_shape_cast_fp16)[name = tensor<string, []>("cast_2")];
+            tensor<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_48_shape_cast_fp16_to_int16)[name = tensor<string, []>("gather_0_cast_uint16")];
+            tensor<string, []> gather_0_cast_uint16_to_int32_dtype_0 = const()[name = tensor<string, []>("gather_0_cast_uint16_to_int32_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1]> var_49_axes_0 = const()[name = tensor<string, []>("op_49_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 1, 128]> var_49_cast_fp16 = expand_dims(axes = var_49_axes_0, x = style_s)[name = tensor<string, []>("op_49_cast_fp16")];
+            tensor<int32, []> concat_0_axis_0 = const()[name = tensor<string, []>("concat_0_axis_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> concat_0_interleave_0 = const()[name = tensor<string, []>("concat_0_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<int32, []> gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = tensor<string, []>("cast_1")];
+            tensor<int32, [3]> concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (gather_0_cast_uint16_to_int32, var_30, var_30))[name = tensor<string, []>("concat_0")];
+            tensor<int32, [3]> shape_0 = const()[name = tensor<string, []>("shape_0"), val = tensor<int32, [3]>([1, 1, 128])];
+            tensor<int32, []> equal_0_y_0 = const()[name = tensor<string, []>("equal_0_y_0"), val = tensor<int32, []>(-1)];
+            tensor<bool, [3]> equal_0 = equal(x = concat_0, y = equal_0_y_0)[name = tensor<string, []>("equal_0")];
+            tensor<int32, [3]> select_0 = select(a = shape_0, b = concat_0, cond = equal_0)[name = tensor<string, []>("select_0")];
+            tensor<int32, [3]> real_div_0 = real_div(x = select_0, y = shape_0)[name = tensor<string, []>("real_div_0")];
+            tensor<fp16, [?, ?, ?]> s_cast_fp16 = tile(reps = real_div_0, x = var_49_cast_fp16)[name = tensor<string, []>("s_cast_fp16")];
+            tensor<bool, []> x_5_interleave_0 = const()[name = tensor<string, []>("x_5_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [?, 1, ?]> x_5_cast_fp16 = concat(axis = var_30, interleave = x_5_interleave_0, values = (transpose_6_cast_fp16, s_cast_fp16))[name = tensor<string, []>("x_5_cast_fp16")];
+            tensor<int32, [1]> var_54_axes_0 = const()[name = tensor<string, []>("op_54_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, [1, ?, 1]> var_54 = expand_dims(axes = var_54_axes_0, x = m)[name = tensor<string, []>("op_54")];
+            tensor<int32, [3]> var_55_perm_0 = const()[name = tensor<string, []>("op_55_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp16, []> var_28_to_fp16 = const()[name = tensor<string, []>("op_28_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<bool, [?, 1, 1]> var_55 = transpose(perm = var_55_perm_0, x = var_54)[name = tensor<string, []>("transpose_46")];
+            tensor<fp16, [?, 1, ?]> x_7_cast_fp16 = select(a = var_28_to_fp16, b = x_5_cast_fp16, cond = var_55)[name = tensor<string, []>("x_7_cast_fp16")];
+            tensor<string, []> input_3_batch_first_direction_0 = const()[name = tensor<string, []>("input_3_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_3_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_3_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_3_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_3_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_3_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_3_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_3_batch_first_activation_0 = const()[name = tensor<string, []>("input_3_batch_first_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp16, [1, 512]> input_3_batch_first_lstm_h0_reshaped_to_fp16 = const()[name = tensor<string, []>("input_3_batch_first_lstm_h0_reshaped_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(395008)))];
+            tensor<fp16, [1024, 640]> concat_5_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(396096))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1051520))), name = tensor<string, []>("concat_5_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
+            tensor<fp16, [1024, 256]> concat_6_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1052096))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1314304))), name = tensor<string, []>("concat_6_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
+            tensor<fp16, [1024]> add_0_to_fp16 = const()[name = tensor<string, []>("add_0_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1314880)))];
+            tensor<fp16, [1024, 640]> concat_7_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1316992))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1972416))), name = tensor<string, []>("concat_7_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
+            tensor<fp16, [1024, 256]> concat_8_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1972992))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2235200))), name = tensor<string, []>("concat_8_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
+            tensor<fp16, [1024]> add_1_to_fp16 = const()[name = tensor<string, []>("add_1_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2235776)))];
+            tensor<fp16, [?, 1, 512]> input_3_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> input_3_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> input_3_batch_first_cast_fp16_2 = lstm(activation = input_3_batch_first_activation_0, bias = add_0_to_fp16, bias_back = add_1_to_fp16, cell_activation = input_3_batch_first_cell_activation_0, direction = input_3_batch_first_direction_0, initial_c = input_3_batch_first_lstm_h0_reshaped_to_fp16, initial_h = input_3_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = input_3_batch_first_output_sequence_0, recurrent_activation = input_3_batch_first_recurrent_activation_0, weight_hh = concat_6_to_fp16_palettized, weight_hh_back = concat_8_to_fp16_palettized, weight_ih = concat_5_to_fp16_palettized, weight_ih_back = concat_7_to_fp16_palettized, x = x_7_cast_fp16)[name = tensor<string, []>("input_3_batch_first_cast_fp16")];
+            tensor<int32, [3]> transpose_17_perm_0 = const()[name = tensor<string, []>("transpose_17_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp16, [1024, 128]> dur_encoder_norms_0_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2237888))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2369024))), name = tensor<string, []>("dur_encoder_norms_0_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 128])];
+            tensor<fp16, [1024]> dur_encoder_norms_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("dur_encoder_norms_0_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2369600)))];
+            tensor<fp16, [1, 1024]> linear_1_cast_fp16 = linear(bias = dur_encoder_norms_0_fc_bias_to_fp16, weight = dur_encoder_norms_0_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_1_cast_fp16")];
+            tensor<int32, [3]> var_89 = const()[name = tensor<string, []>("op_89"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp16, [1, 1024, 1]> h_3_cast_fp16 = reshape(shape = var_89, x = linear_1_cast_fp16)[name = tensor<string, []>("h_3_cast_fp16")];
+            tensor<int32, [2]> var_91_split_sizes_0 = const()[name = tensor<string, []>("op_91_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            tensor<int32, []> var_91_axis_0 = const()[name = tensor<string, []>("op_91_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 512, 1]> var_91_cast_fp16_0, tensor<fp16, [1, 512, 1]> var_91_cast_fp16_1 = split(axis = var_91_axis_0, split_sizes = var_91_split_sizes_0, x = h_3_cast_fp16)[name = tensor<string, []>("op_91_cast_fp16")];
+            tensor<int32, [3]> gamma_3_perm_0 = const()[name = tensor<string, []>("gamma_3_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [3]> beta_3_perm_0 = const()[name = tensor<string, []>("beta_3_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [1]> x_21_axes_0 = const()[name = tensor<string, []>("x_21_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, []> var_20_to_fp16 = const()[name = tensor<string, []>("op_20_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> transpose_17_cast_fp16 = transpose(perm = transpose_17_perm_0, x = input_3_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_45")];
+            tensor<fp16, [1, ?, 512]> x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_20_to_fp16, x = transpose_17_cast_fp16)[name = tensor<string, []>("x_21_cast_fp16")];
+            tensor<fp16, []> var_97_promoted_to_fp16 = const()[name = tensor<string, []>("op_97_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 1, 512]> gamma_3_cast_fp16 = transpose(perm = gamma_3_perm_0, x = var_91_cast_fp16_0)[name = tensor<string, []>("transpose_44")];
+            tensor<fp16, [1, 1, 512]> var_98_cast_fp16 = add(x = gamma_3_cast_fp16, y = var_97_promoted_to_fp16)[name = tensor<string, []>("op_98_cast_fp16")];
+            tensor<fp16, [1, ?, 512]> var_99_cast_fp16 = mul(x = var_98_cast_fp16, y = x_21_cast_fp16)[name = tensor<string, []>("op_99_cast_fp16")];
+            tensor<fp16, [1, 1, 512]> beta_3_cast_fp16 = transpose(perm = beta_3_perm_0, x = var_91_cast_fp16_1)[name = tensor<string, []>("transpose_43")];
+            tensor<fp16, [1, ?, 512]> x_23_cast_fp16 = add(x = var_99_cast_fp16, y = beta_3_cast_fp16)[name = tensor<string, []>("x_23_cast_fp16")];
+            tensor<bool, []> x_27_interleave_0 = const()[name = tensor<string, []>("x_27_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [3]> transpose_12_perm_0 = const()[name = tensor<string, []>("transpose_12_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
+            tensor<int32, [3]> transpose_13_perm_0 = const()[name = tensor<string, []>("transpose_13_perm_0"), val = tensor<int32, [3]>([1, 2, 0])];
+            tensor<fp16, [?, ?, ?]> transpose_13 = transpose(perm = transpose_13_perm_0, x = s_cast_fp16)[name = tensor<string, []>("transpose_41")];
+            tensor<fp16, [1, 512, ?]> transpose_12 = transpose(perm = transpose_12_perm_0, x = x_23_cast_fp16)[name = tensor<string, []>("transpose_42")];
+            tensor<fp16, [1, ?, ?]> x_27_cast_fp16 = concat(axis = var_31, interleave = x_27_interleave_0, values = (transpose_12, transpose_13))[name = tensor<string, []>("x_27_cast_fp16")];
+            tensor<int32, [3]> var_109_perm_0 = const()[name = tensor<string, []>("op_109_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
+            tensor<bool, [1, 1, ?]> var_109 = transpose(perm = var_109_perm_0, x = var_54)[name = tensor<string, []>("transpose_40")];
+            tensor<fp16, [1, ?, ?]> x_29_cast_fp16 = select(a = var_28_to_fp16, b = x_27_cast_fp16, cond = var_109)[name = tensor<string, []>("x_29_cast_fp16")];
+            tensor<int32, [3]> transpose_10_perm_0 = const()[name = tensor<string, []>("transpose_10_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])];
+            tensor<string, []> input_9_batch_first_direction_0 = const()[name = tensor<string, []>("input_9_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_9_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_9_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_9_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_9_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_9_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_9_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_9_batch_first_activation_0 = const()[name = tensor<string, []>("input_9_batch_first_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp16, [1024, 640]> concat_15_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2371712))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3027136))), name = tensor<string, []>("concat_15_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
+            tensor<fp16, [1024, 256]> concat_16_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3027712))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3289920))), name = tensor<string, []>("concat_16_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
+            tensor<fp16, [1024]> add_2_to_fp16 = const()[name = tensor<string, []>("add_2_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3290496)))];
+            tensor<fp16, [1024, 640]> concat_17_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3292608))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3948032))), name = tensor<string, []>("concat_17_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
+            tensor<fp16, [1024, 256]> concat_18_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3948608))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4210816))), name = tensor<string, []>("concat_18_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
+            tensor<fp16, [1024]> add_3_to_fp16 = const()[name = tensor<string, []>("add_3_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4211392)))];
+            tensor<fp16, [?, 1, ?]> transpose_10_cast_fp16 = transpose(perm = transpose_10_perm_0, x = x_29_cast_fp16)[name = tensor<string, []>("transpose_39")];
+            tensor<fp16, [?, 1, 512]> input_9_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> input_9_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> input_9_batch_first_cast_fp16_2 = lstm(activation = input_9_batch_first_activation_0, bias = add_2_to_fp16, bias_back = add_3_to_fp16, cell_activation = input_9_batch_first_cell_activation_0, direction = input_9_batch_first_direction_0, initial_c = input_3_batch_first_lstm_h0_reshaped_to_fp16, initial_h = input_3_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = input_9_batch_first_output_sequence_0, recurrent_activation = input_9_batch_first_recurrent_activation_0, weight_hh = concat_16_to_fp16_palettized, weight_hh_back = concat_18_to_fp16_palettized, weight_ih = concat_15_to_fp16_palettized, weight_ih_back = concat_17_to_fp16_palettized, x = transpose_10_cast_fp16)[name = tensor<string, []>("input_9_batch_first_cast_fp16")];
+            tensor<int32, [3]> transpose_18_perm_0 = const()[name = tensor<string, []>("transpose_18_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp16, [1024, 128]> dur_encoder_norms_1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4213504))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4344640))), name = tensor<string, []>("dur_encoder_norms_1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 128])];
+            tensor<fp16, [1024]> dur_encoder_norms_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("dur_encoder_norms_1_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4345216)))];
+            tensor<fp16, [1, 1024]> linear_2_cast_fp16 = linear(bias = dur_encoder_norms_1_fc_bias_to_fp16, weight = dur_encoder_norms_1_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_2_cast_fp16")];
+            tensor<int32, [3]> var_141 = const()[name = tensor<string, []>("op_141"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp16, [1, 1024, 1]> h_7_cast_fp16 = reshape(shape = var_141, x = linear_2_cast_fp16)[name = tensor<string, []>("h_7_cast_fp16")];
+            tensor<int32, [2]> var_143_split_sizes_0 = const()[name = tensor<string, []>("op_143_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            tensor<int32, []> var_143_axis_0 = const()[name = tensor<string, []>("op_143_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 512, 1]> var_143_cast_fp16_0, tensor<fp16, [1, 512, 1]> var_143_cast_fp16_1 = split(axis = var_143_axis_0, split_sizes = var_143_split_sizes_0, x = h_7_cast_fp16)[name = tensor<string, []>("op_143_cast_fp16")];
+            tensor<int32, [3]> gamma_7_perm_0 = const()[name = tensor<string, []>("gamma_7_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [3]> beta_7_perm_0 = const()[name = tensor<string, []>("beta_7_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [1]> x_39_axes_0 = const()[name = tensor<string, []>("x_39_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 512]> transpose_18_cast_fp16 = transpose(perm = transpose_18_perm_0, x = input_9_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_38")];
+            tensor<fp16, [1, ?, 512]> x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_20_to_fp16, x = transpose_18_cast_fp16)[name = tensor<string, []>("x_39_cast_fp16")];
+            tensor<fp16, []> var_149_promoted_to_fp16 = const()[name = tensor<string, []>("op_149_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 1, 512]> gamma_7_cast_fp16 = transpose(perm = gamma_7_perm_0, x = var_143_cast_fp16_0)[name = tensor<string, []>("transpose_37")];
+            tensor<fp16, [1, 1, 512]> var_150_cast_fp16 = add(x = gamma_7_cast_fp16, y = var_149_promoted_to_fp16)[name = tensor<string, []>("op_150_cast_fp16")];
+            tensor<fp16, [1, ?, 512]> var_151_cast_fp16 = mul(x = var_150_cast_fp16, y = x_39_cast_fp16)[name = tensor<string, []>("op_151_cast_fp16")];
+            tensor<fp16, [1, 1, 512]> beta_7_cast_fp16 = transpose(perm = beta_7_perm_0, x = var_143_cast_fp16_1)[name = tensor<string, []>("transpose_36")];
+            tensor<fp16, [1, ?, 512]> x_41_cast_fp16 = add(x = var_151_cast_fp16, y = beta_7_cast_fp16)[name = tensor<string, []>("x_41_cast_fp16")];
+            tensor<bool, []> x_45_interleave_0 = const()[name = tensor<string, []>("x_45_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [3]> transpose_16_perm_0 = const()[name = tensor<string, []>("transpose_16_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
+            tensor<fp16, [1, 512, ?]> transpose_16 = transpose(perm = transpose_16_perm_0, x = x_41_cast_fp16)[name = tensor<string, []>("transpose_35")];
+            tensor<fp16, [1, ?, ?]> x_45_cast_fp16 = concat(axis = var_31, interleave = x_45_interleave_0, values = (transpose_16, transpose_13))[name = tensor<string, []>("x_45_cast_fp16")];
+            tensor<fp16, [1, ?, ?]> x_47_cast_fp16 = select(a = var_28_to_fp16, b = x_45_cast_fp16, cond = var_109)[name = tensor<string, []>("x_47_cast_fp16")];
+            tensor<int32, [3]> transpose_12_perm_0_1 = const()[name = tensor<string, []>("transpose_12_perm_0_1"), val = tensor<int32, [3]>([-1, 0, -2])];
+            tensor<string, []> input_15_batch_first_direction_0 = const()[name = tensor<string, []>("input_15_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_15_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_15_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_15_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_15_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_15_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_15_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_15_batch_first_activation_0 = const()[name = tensor<string, []>("input_15_batch_first_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp16, [1024, 640]> concat_25_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4347328))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5002752))), name = tensor<string, []>("concat_25_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
+            tensor<fp16, [1024, 256]> concat_26_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5003328))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5265536))), name = tensor<string, []>("concat_26_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
+            tensor<fp16, [1024]> add_4_to_fp16 = const()[name = tensor<string, []>("add_4_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5266112)))];
+            tensor<fp16, [1024, 640]> concat_27_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5268224))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5923648))), name = tensor<string, []>("concat_27_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
+            tensor<fp16, [1024, 256]> concat_28_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5924224))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6186432))), name = tensor<string, []>("concat_28_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
+            tensor<fp16, [1024]> add_5_to_fp16 = const()[name = tensor<string, []>("add_5_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6187008)))];
+            tensor<fp16, [?, 1, ?]> transpose_12_cast_fp16 = transpose(perm = transpose_12_perm_0_1, x = x_47_cast_fp16)[name = tensor<string, []>("transpose_34")];
+            tensor<fp16, [?, 1, 512]> input_15_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> input_15_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> input_15_batch_first_cast_fp16_2 = lstm(activation = input_15_batch_first_activation_0, bias = add_4_to_fp16, bias_back = add_5_to_fp16, cell_activation = input_15_batch_first_cell_activation_0, direction = input_15_batch_first_direction_0, initial_c = input_3_batch_first_lstm_h0_reshaped_to_fp16, initial_h = input_3_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = input_15_batch_first_output_sequence_0, recurrent_activation = input_15_batch_first_recurrent_activation_0, weight_hh = concat_26_to_fp16_palettized, weight_hh_back = concat_28_to_fp16_palettized, weight_ih = concat_25_to_fp16_palettized, weight_ih_back = concat_27_to_fp16_palettized, x = transpose_12_cast_fp16)[name = tensor<string, []>("input_15_batch_first_cast_fp16")];
+            tensor<int32, [3]> transpose_19_perm_0 = const()[name = tensor<string, []>("transpose_19_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp16, [1024, 128]> dur_encoder_norms_2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6189120))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6320256))), name = tensor<string, []>("dur_encoder_norms_2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 128])];
+            tensor<fp16, [1024]> dur_encoder_norms_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("dur_encoder_norms_2_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6320832)))];
+            tensor<fp16, [1, 1024]> linear_3_cast_fp16 = linear(bias = dur_encoder_norms_2_fc_bias_to_fp16, weight = dur_encoder_norms_2_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_3_cast_fp16")];
+            tensor<int32, [3]> var_193 = const()[name = tensor<string, []>("op_193"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp16, [1, 1024, 1]> h_cast_fp16 = reshape(shape = var_193, x = linear_3_cast_fp16)[name = tensor<string, []>("h_cast_fp16")];
+            tensor<int32, [2]> var_195_split_sizes_0 = const()[name = tensor<string, []>("op_195_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            tensor<int32, []> var_195_axis_0 = const()[name = tensor<string, []>("op_195_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 512, 1]> var_195_cast_fp16_0, tensor<fp16, [1, 512, 1]> var_195_cast_fp16_1 = split(axis = var_195_axis_0, split_sizes = var_195_split_sizes_0, x = h_cast_fp16)[name = tensor<string, []>("op_195_cast_fp16")];
+            tensor<int32, [3]> gamma_11_perm_0 = const()[name = tensor<string, []>("gamma_11_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [3]> beta_11_perm_0 = const()[name = tensor<string, []>("beta_11_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [1]> x_57_axes_0 = const()[name = tensor<string, []>("x_57_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, ?, 512]> transpose_19_cast_fp16 = transpose(perm = transpose_19_perm_0, x = input_15_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_33")];
+            tensor<fp16, [1, ?, 512]> x_57_cast_fp16 = layer_norm(axes = x_57_axes_0, epsilon = var_20_to_fp16, x = transpose_19_cast_fp16)[name = tensor<string, []>("x_57_cast_fp16")];
+            tensor<fp16, []> var_201_promoted_to_fp16 = const()[name = tensor<string, []>("op_201_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 1, 512]> gamma_11_cast_fp16 = transpose(perm = gamma_11_perm_0, x = var_195_cast_fp16_0)[name = tensor<string, []>("transpose_32")];
+            tensor<fp16, [1, 1, 512]> var_202_cast_fp16 = add(x = gamma_11_cast_fp16, y = var_201_promoted_to_fp16)[name = tensor<string, []>("op_202_cast_fp16")];
+            tensor<fp16, [1, ?, 512]> var_203_cast_fp16 = mul(x = var_202_cast_fp16, y = x_57_cast_fp16)[name = tensor<string, []>("op_203_cast_fp16")];
+            tensor<fp16, [1, 1, 512]> beta_11_cast_fp16 = transpose(perm = beta_11_perm_0, x = var_195_cast_fp16_1)[name = tensor<string, []>("transpose_31")];
+            tensor<fp16, [1, ?, 512]> x_59_cast_fp16 = add(x = var_203_cast_fp16, y = beta_11_cast_fp16)[name = tensor<string, []>("x_59_cast_fp16")];
+            tensor<bool, []> x_63_interleave_0 = const()[name = tensor<string, []>("x_63_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<int32, [3]> transpose_17_perm_0_1 = const()[name = tensor<string, []>("transpose_17_perm_0_1"), val = tensor<int32, [3]>([0, -1, -2])];
+            tensor<fp16, [1, 512, ?]> transpose_17 = transpose(perm = transpose_17_perm_0_1, x = x_59_cast_fp16)[name = tensor<string, []>("transpose_30")];
+            tensor<fp16, [1, ?, ?]> x_63_cast_fp16 = concat(axis = var_31, interleave = x_63_interleave_0, values = (transpose_17, transpose_13))[name = tensor<string, []>("x_63_cast_fp16")];
+            tensor<fp16, [1, ?, ?]> x_65_cast_fp16 = select(a = var_28_to_fp16, b = x_63_cast_fp16, cond = var_109)[name = tensor<string, []>("x_65_cast_fp16")];
+            tensor<int32, [3]> input_19_perm_0 = const()[name = tensor<string, []>("input_19_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
+            tensor<int32, [3]> input_19_batch_first_transpose_perm_0 = const()[name = tensor<string, []>("input_19_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<string, []> input_21_batch_first_direction_0 = const()[name = tensor<string, []>("input_21_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_21_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_21_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_21_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_21_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_21_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_21_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_21_batch_first_activation_0 = const()[name = tensor<string, []>("input_21_batch_first_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp16, [1024, 640]> concat_35_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6322944))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6978368))), name = tensor<string, []>("concat_35_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
+            tensor<fp16, [1024, 256]> concat_36_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6978944))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7241152))), name = tensor<string, []>("concat_36_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
+            tensor<fp16, [1024]> add_6_to_fp16 = const()[name = tensor<string, []>("add_6_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7241728)))];
+            tensor<fp16, [1024, 640]> concat_37_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7243840))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7899264))), name = tensor<string, []>("concat_37_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
+            tensor<fp16, [1024, 256]> concat_38_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7899840))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8162048))), name = tensor<string, []>("concat_38_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
+            tensor<fp16, [1024]> add_7_to_fp16 = const()[name = tensor<string, []>("add_7_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8162624)))];
+            tensor<fp16, [1, ?, ?]> d = transpose(perm = input_19_perm_0, x = x_65_cast_fp16)[name = tensor<string, []>("transpose_29")];
+            tensor<fp16, [?, 1, ?]> input_19_batch_first_transpose_cast_fp16 = transpose(perm = input_19_batch_first_transpose_perm_0, x = d)[name = tensor<string, []>("transpose_28")];
+            tensor<fp16, [?, 1, 512]> input_21_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> input_21_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> input_21_batch_first_cast_fp16_2 = lstm(activation = input_21_batch_first_activation_0, bias = add_6_to_fp16, bias_back = add_7_to_fp16, cell_activation = input_21_batch_first_cell_activation_0, direction = input_21_batch_first_direction_0, initial_c = input_3_batch_first_lstm_h0_reshaped_to_fp16, initial_h = input_3_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = input_21_batch_first_output_sequence_0, recurrent_activation = input_21_batch_first_recurrent_activation_0, weight_hh = concat_36_to_fp16_palettized, weight_hh_back = concat_38_to_fp16_palettized, weight_ih = concat_35_to_fp16_palettized, weight_ih_back = concat_37_to_fp16_palettized, x = input_19_batch_first_transpose_cast_fp16)[name = tensor<string, []>("input_21_batch_first_cast_fp16")];
+            tensor<int32, [3]> input_21_perm_0 = const()[name = tensor<string, []>("input_21_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp16, [50, 512]> duration_proj_linear_layer_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [25600]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8164736))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8190400))), name = tensor<string, []>("duration_proj_linear_layer_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([50, 512])];
+            tensor<fp16, [50]> duration_proj_linear_layer_bias_to_fp16 = const()[name = tensor<string, []>("duration_proj_linear_layer_bias_to_fp16"), val = tensor<fp16, [50]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8190976)))];
+            tensor<fp16, [1, ?, 512]> input_21_cast_fp16 = transpose(perm = input_21_perm_0, x = input_21_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_27")];
+            tensor<fp16, [1, ?, 50]> linear_4_cast_fp16 = linear(bias = duration_proj_linear_layer_bias_to_fp16, weight = duration_proj_linear_layer_weight_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor<string, []>("linear_4_cast_fp16")];
+            tensor<fp16, [1, ?, 50]> var_248_cast_fp16 = sigmoid(x = linear_4_cast_fp16)[name = tensor<string, []>("op_248_cast_fp16")];
+            tensor<int32, [1]> var_253_axes_0 = const()[name = tensor<string, []>("op_253_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> var_253_keep_dims_0 = const()[name = tensor<string, []>("op_253_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, ?]> var_253_cast_fp16 = reduce_sum(axes = var_253_axes_0, keep_dims = var_253_keep_dims_0, x = var_248_cast_fp16)[name = tensor<string, []>("op_253_cast_fp16")];
+            tensor<fp16, [1, ?]> duration = real_div(x = var_253_cast_fp16, y = speed)[name = tensor<string, []>("op_254_cast_fp16")];
+            tensor<fp32, []> var_263 = const()[name = tensor<string, []>("op_263"), val = tensor<fp32, []>(0x1.99999ap-3)];
+            tensor<int32, []> x_67_axis_0 = const()[name = tensor<string, []>("x_67_axis_0"), val = tensor<int32, []>(0)];
+            tensor<int32, []> x_67_batch_dims_0 = const()[name = tensor<string, []>("x_67_batch_dims_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> x_67_validate_indices_0 = const()[name = tensor<string, []>("x_67_validate_indices_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [178, 512]> text_encoder_embedding_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [91136]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8191168))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8282368))), name = tensor<string, []>("text_encoder_embedding_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([178, 512])];
+            tensor<string, []> input_ids_to_uint16_dtype_0 = const()[name = tensor<string, []>("input_ids_to_uint16_dtype_0"), val = tensor<string, []>("uint16")];
+            tensor<uint16, [1, ?]> input_ids_to_uint16 = cast(dtype = input_ids_to_uint16_dtype_0, x = input_ids)[name = tensor<string, []>("cast_0")];
+            tensor<fp16, [1, ?, 512]> x_67_cast_fp16_cast_uint16 = gather(axis = x_67_axis_0, batch_dims = x_67_batch_dims_0, indices = input_ids_to_uint16, validate_indices = x_67_validate_indices_0, x = text_encoder_embedding_weight_to_fp16_palettized)[name = tensor<string, []>("x_67_cast_fp16_cast_uint16")];
+            tensor<int32, [3]> x_69_perm_0 = const()[name = tensor<string, []>("x_69_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> m_unsq_axes_0 = const()[name = tensor<string, []>("m_unsq_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, ?]> m_unsq = expand_dims(axes = m_unsq_axes_0, x = m)[name = tensor<string, []>("m_unsq")];
+            tensor<fp16, []> var_265_to_fp16 = const()[name = tensor<string, []>("op_265_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 512, ?]> x_69_cast_fp16 = transpose(perm = x_69_perm_0, x = x_67_cast_fp16_cast_uint16)[name = tensor<string, []>("transpose_26")];
+            tensor<fp16, [1, 512, ?]> input_23_cast_fp16 = select(a = var_265_to_fp16, b = x_69_cast_fp16, cond = m_unsq)[name = tensor<string, []>("input_23_cast_fp16")];
+            tensor<string, []> x_71_pad_type_0 = const()[name = tensor<string, []>("x_71_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> x_71_pad_0 = const()[name = tensor<string, []>("x_71_pad_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [1]> x_71_strides_0 = const()[name = tensor<string, []>("x_71_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> x_71_dilations_0 = const()[name = tensor<string, []>("x_71_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_71_groups_0 = const()[name = tensor<string, []>("x_71_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [512, 512, 5]> weight_3_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8282944))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9593728))), name = tensor<string, []>("weight_3_to_fp16_palettized"), shape = tensor<uint32, [3]>([512, 512, 5])];
+            tensor<fp16, [512]> text_encoder_cnn_0_0_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_0_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9594304)))];
+            tensor<fp16, [1, 512, ?]> x_71_cast_fp16 = conv(bias = text_encoder_cnn_0_0_bias_to_fp16, dilations = x_71_dilations_0, groups = x_71_groups_0, pad = x_71_pad_0, pad_type = x_71_pad_type_0, strides = x_71_strides_0, weight = weight_3_to_fp16_palettized, x = input_23_cast_fp16)[name = tensor<string, []>("x_71_cast_fp16")];
+            tensor<int32, [3]> input_25_perm_0 = const()[name = tensor<string, []>("input_25_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [1]> x_73_axes_0 = const()[name = tensor<string, []>("x_73_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> text_encoder_cnn_0_1_gamma_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_0_1_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9595392)))];
+            tensor<fp16, [512]> text_encoder_cnn_0_1_beta_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_0_1_beta_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9596480)))];
+            tensor<fp16, []> var_261_to_fp16 = const()[name = tensor<string, []>("op_261_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_71_cast_fp16)[name = tensor<string, []>("transpose_25")];
+            tensor<fp16, [1, ?, 512]> x_73_cast_fp16 = layer_norm(axes = x_73_axes_0, beta = text_encoder_cnn_0_1_beta_to_fp16, epsilon = var_261_to_fp16, gamma = text_encoder_cnn_0_1_gamma_to_fp16, x = input_25_cast_fp16)[name = tensor<string, []>("x_73_cast_fp16")];
+            tensor<int32, [3]> input_27_perm_0 = const()[name = tensor<string, []>("input_27_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<fp16, [1, 512, ?]> input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = x_73_cast_fp16)[name = tensor<string, []>("transpose_24")];
+            tensor<fp16, [1, 512, ?]> x_75_cast_fp16 = leaky_relu(alpha = var_263, x = input_27_cast_fp16)[name = tensor<string, []>("x_75_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_29_cast_fp16 = select(a = var_265_to_fp16, b = x_75_cast_fp16, cond = m_unsq)[name = tensor<string, []>("input_29_cast_fp16")];
+            tensor<string, []> x_77_pad_type_0 = const()[name = tensor<string, []>("x_77_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> x_77_pad_0 = const()[name = tensor<string, []>("x_77_pad_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [1]> x_77_strides_0 = const()[name = tensor<string, []>("x_77_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> x_77_dilations_0 = const()[name = tensor<string, []>("x_77_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_77_groups_0 = const()[name = tensor<string, []>("x_77_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [512, 512, 5]> weight_7_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9597568))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10908352))), name = tensor<string, []>("weight_7_to_fp16_palettized"), shape = tensor<uint32, [3]>([512, 512, 5])];
+            tensor<fp16, [512]> text_encoder_cnn_1_0_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_1_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10908928)))];
+            tensor<fp16, [1, 512, ?]> x_77_cast_fp16 = conv(bias = text_encoder_cnn_1_0_bias_to_fp16, dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = weight_7_to_fp16_palettized, x = input_29_cast_fp16)[name = tensor<string, []>("x_77_cast_fp16")];
+            tensor<int32, [3]> input_31_perm_0 = const()[name = tensor<string, []>("input_31_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [1]> x_79_axes_0 = const()[name = tensor<string, []>("x_79_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> text_encoder_cnn_1_1_gamma_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_1_1_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10910016)))];
+            tensor<fp16, [512]> text_encoder_cnn_1_1_beta_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_1_1_beta_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10911104)))];
+            tensor<fp16, [1, ?, 512]> input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_77_cast_fp16)[name = tensor<string, []>("transpose_23")];
+            tensor<fp16, [1, ?, 512]> x_79_cast_fp16 = layer_norm(axes = x_79_axes_0, beta = text_encoder_cnn_1_1_beta_to_fp16, epsilon = var_261_to_fp16, gamma = text_encoder_cnn_1_1_gamma_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("x_79_cast_fp16")];
+            tensor<int32, [3]> input_33_perm_0 = const()[name = tensor<string, []>("input_33_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<fp16, [1, 512, ?]> input_33_cast_fp16 = transpose(perm = input_33_perm_0, x = x_79_cast_fp16)[name = tensor<string, []>("transpose_22")];
+            tensor<fp16, [1, 512, ?]> x_81_cast_fp16 = leaky_relu(alpha = var_263, x = input_33_cast_fp16)[name = tensor<string, []>("x_81_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_35_cast_fp16 = select(a = var_265_to_fp16, b = x_81_cast_fp16, cond = m_unsq)[name = tensor<string, []>("input_35_cast_fp16")];
+            tensor<string, []> x_83_pad_type_0 = const()[name = tensor<string, []>("x_83_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> x_83_pad_0 = const()[name = tensor<string, []>("x_83_pad_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [1]> x_83_strides_0 = const()[name = tensor<string, []>("x_83_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> x_83_dilations_0 = const()[name = tensor<string, []>("x_83_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_83_groups_0 = const()[name = tensor<string, []>("x_83_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [512, 512, 5]> weight_11_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1310720]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10912192))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12222976))), name = tensor<string, []>("weight_11_to_fp16_palettized"), shape = tensor<uint32, [3]>([512, 512, 5])];
+            tensor<fp16, [512]> text_encoder_cnn_2_0_bias_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_2_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12223552)))];
+            tensor<fp16, [1, 512, ?]> x_83_cast_fp16 = conv(bias = text_encoder_cnn_2_0_bias_to_fp16, dilations = x_83_dilations_0, groups = x_83_groups_0, pad = x_83_pad_0, pad_type = x_83_pad_type_0, strides = x_83_strides_0, weight = weight_11_to_fp16_palettized, x = input_35_cast_fp16)[name = tensor<string, []>("x_83_cast_fp16")];
+            tensor<int32, [3]> input_37_perm_0 = const()[name = tensor<string, []>("input_37_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<int32, [1]> x_85_axes_0 = const()[name = tensor<string, []>("x_85_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> text_encoder_cnn_2_1_gamma_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_2_1_gamma_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12224640)))];
+            tensor<fp16, [512]> text_encoder_cnn_2_1_beta_to_fp16 = const()[name = tensor<string, []>("text_encoder_cnn_2_1_beta_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12225728)))];
+            tensor<fp16, [1, ?, 512]> input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_83_cast_fp16)[name = tensor<string, []>("transpose_21")];
+            tensor<fp16, [1, ?, 512]> x_85_cast_fp16 = layer_norm(axes = x_85_axes_0, beta = text_encoder_cnn_2_1_beta_to_fp16, epsilon = var_261_to_fp16, gamma = text_encoder_cnn_2_1_gamma_to_fp16, x = input_37_cast_fp16)[name = tensor<string, []>("x_85_cast_fp16")];
+            tensor<int32, [3]> input_39_perm_0 = const()[name = tensor<string, []>("input_39_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
+            tensor<fp16, [1, 512, ?]> input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = x_85_cast_fp16)[name = tensor<string, []>("transpose_20")];
+            tensor<fp16, [1, 512, ?]> x_87_cast_fp16 = leaky_relu(alpha = var_263, x = input_39_cast_fp16)[name = tensor<string, []>("x_87_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> x_89_cast_fp16 = select(a = var_265_to_fp16, b = x_87_cast_fp16, cond = m_unsq)[name = tensor<string, []>("x_89_cast_fp16")];
+            tensor<int32, [3]> transpose_14_perm_0 = const()[name = tensor<string, []>("transpose_14_perm_0"), val = tensor<int32, [3]>([2, 0, 1])];
+            tensor<string, []> x_91_batch_first_direction_0 = const()[name = tensor<string, []>("x_91_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> x_91_batch_first_output_sequence_0 = const()[name = tensor<string, []>("x_91_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> x_91_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("x_91_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> x_91_batch_first_cell_activation_0 = const()[name = tensor<string, []>("x_91_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> x_91_batch_first_activation_0 = const()[name = tensor<string, []>("x_91_batch_first_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp16, [1024, 512]> concat_45_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [524288]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12226816))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12751168))), name = tensor<string, []>("concat_45_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 512])];
+            tensor<fp16, [1024, 256]> concat_46_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12751744))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13013952))), name = tensor<string, []>("concat_46_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
+            tensor<fp16, [1024]> add_8_to_fp16 = const()[name = tensor<string, []>("add_8_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13014528)))];
+            tensor<fp16, [1024, 512]> concat_47_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [524288]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13016640))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13540992))), name = tensor<string, []>("concat_47_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 512])];
+            tensor<fp16, [1024, 256]> concat_48_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13541568))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13803776))), name = tensor<string, []>("concat_48_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
+            tensor<fp16, [1024]> add_9_to_fp16 = const()[name = tensor<string, []>("add_9_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13804352)))];
+            tensor<fp16, [?, 1, 512]> transpose_14_cast_fp16 = transpose(perm = transpose_14_perm_0, x = x_89_cast_fp16)[name = tensor<string, []>("transpose_19")];
+            tensor<fp16, [?, 1, 512]> x_91_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> x_91_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> x_91_batch_first_cast_fp16_2 = lstm(activation = x_91_batch_first_activation_0, bias = add_8_to_fp16, bias_back = add_9_to_fp16, cell_activation = x_91_batch_first_cell_activation_0, direction = x_91_batch_first_direction_0, initial_c = input_3_batch_first_lstm_h0_reshaped_to_fp16, initial_h = input_3_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = x_91_batch_first_output_sequence_0, recurrent_activation = x_91_batch_first_recurrent_activation_0, weight_hh = concat_46_to_fp16_palettized, weight_hh_back = concat_48_to_fp16_palettized, weight_ih = concat_45_to_fp16_palettized, weight_ih_back = concat_47_to_fp16_palettized, x = transpose_14_cast_fp16)[name = tensor<string, []>("x_91_batch_first_cast_fp16")];
+            tensor<int32, [3]> transpose_15_perm_0 = const()[name = tensor<string, []>("transpose_15_perm_0"), val = tensor<int32, [3]>([1, 2, 0])];
+            tensor<fp16, [1, 512, ?]> transpose_15_cast_fp16 = transpose(perm = transpose_15_perm_0, x = x_91_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_18")];
+            tensor<fp16, [1, 512, ?]> t_en = select(a = var_265_to_fp16, b = transpose_15_cast_fp16, cond = m_unsq)[name = tensor<string, []>("t_en_cast_fp16")];
+        } -> (duration, d, t_en);
+}
\ No newline at end of file
diff --git a/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/weights/weight.bin b/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..a17aac3cfd131ba060bace647b2d1cc5aa2a31df
--- /dev/null
+++ b/ANE/ANE-zh/KokoroPostAlbert.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bca73c9f402ccd18052fe8cc418741caa196c3caceeb3637187cdc5d67d8daf7
+size 13806464
diff --git a/ANE/ANE-zh/KokoroPostAlbert.mlpackage/Data/com.apple.CoreML/model.mlmodel b/ANE/ANE-zh/KokoroPostAlbert.mlpackage/Data/com.apple.CoreML/model.mlmodel
new file mode 100644
index 0000000000000000000000000000000000000000..4529d55193135f27f93182e26ea6c1fd16028d81
--- /dev/null
+++ b/ANE/ANE-zh/KokoroPostAlbert.mlpackage/Data/com.apple.CoreML/model.mlmodel
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5669010b4feed54b3e9431ae12983264a3e09be548ce6d08c3e02bbb5c1f6a0
+size 49183
diff --git a/ANE/ANE-zh/KokoroPostAlbert.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/ANE/ANE-zh/KokoroPostAlbert.mlpackage/Data/com.apple.CoreML/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..a17aac3cfd131ba060bace647b2d1cc5aa2a31df
--- /dev/null
+++ b/ANE/ANE-zh/KokoroPostAlbert.mlpackage/Data/com.apple.CoreML/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bca73c9f402ccd18052fe8cc418741caa196c3caceeb3637187cdc5d67d8daf7
+size 13806464
diff --git a/ANE/ANE-zh/KokoroPostAlbert.mlpackage/Manifest.json b/ANE/ANE-zh/KokoroPostAlbert.mlpackage/Manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..31adad3526c47b7844faa7faa966bd985a30ffcf
--- /dev/null
+++ b/ANE/ANE-zh/KokoroPostAlbert.mlpackage/Manifest.json
@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "07F36BCD-E5BB-4939-9D39-45F59CBD8058": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "DB17D18A-94F8-4785-9B53-34E1386398F3": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "DB17D18A-94F8-4785-9B53-34E1386398F3"
+}
diff --git a/ANE/ANE-zh/KokoroProsody.mlmodelc/analytics/coremldata.bin b/ANE/ANE-zh/KokoroProsody.mlmodelc/analytics/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..c53bf87b0e55b280cdee3ee9291a453946874553
--- /dev/null
+++ b/ANE/ANE-zh/KokoroProsody.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c90a008bfe021f8c24f39d26706a894cffb3cc6d5dca1ad131f54547f0554a1
+size 243
diff --git a/ANE/ANE-zh/KokoroProsody.mlmodelc/coremldata.bin b/ANE/ANE-zh/KokoroProsody.mlmodelc/coremldata.bin
new file mode 100644
index 0000000000000000000000000000000000000000..d5b6ef1cc53e756b7bd4d71df2375bce759b2d9c
--- /dev/null
+++ b/ANE/ANE-zh/KokoroProsody.mlmodelc/coremldata.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f1932bc07f52171e0fff898ae0e9c289a8c73aaf0a2ac06b2352b8ef58afc61
+size 422
diff --git a/ANE/ANE-zh/KokoroProsody.mlmodelc/metadata.json b/ANE/ANE-zh/KokoroProsody.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..a324dbf20c732c219736d579abd2df62fb4791a6
--- /dev/null
+++ b/ANE/ANE-zh/KokoroProsody.mlmodelc/metadata.json
@@ -0,0 +1,98 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Mixed (Float16, Palettized (8 bits))",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "F0",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "N",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios17.mul" : 18,
+      "Ios17.linear" : 12,
+      "Ios17.transpose" : 2,
+      "Ios16.constexprLutToDense" : 30,
+      "Ios17.conv" : 16,
+      "Ios17.leakyRelu" : 12,
+      "Ios17.add" : 30,
+      "Ios17.convTranspose" : 2,
+      "Ios17.lstm" : 1,
+      "Ios17.sliceByIndex" : 2,
+      "UpsampleNearestNeighbor" : 2,
+      "Ios17.expandDims" : 2,
+      "Ios17.instanceNorm" : 11,
+      "Ios17.reshape" : 12,
+      "Split" : 12,
+      "Ios17.squeeze" : 4
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-05-03",
+      "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" : "Float16",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 640 × 1...2000",
+        "shapeRange" : "[[1, 1], [640, 640], [1, 2000]]",
+        "formattedType" : "MultiArray (Float16 1 × 640 × 133)",
+        "type" : "MultiArray",
+        "shape" : "[1, 640, 133]",
+        "name" : "en",
+        "shortDescription" : ""
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 128)",
+        "shortDescription" : "",
+        "shape" : "[1, 128]",
+        "name" : "style_s",
+        "type" : "MultiArray"
+      }
+    ],
+    "generatedClassName" : "KokoroProsody",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/ANE/ANE-zh/KokoroProsody.mlmodelc/model.mil b/ANE/ANE-zh/KokoroProsody.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..5a8fe43c018daadc03c0e75b477f69f60271b529
--- /dev/null
+++ b/ANE/ANE-zh/KokoroProsody.mlmodelc/model.mil
@@ -0,0 +1,394 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})]
+{
+    func main<ios17>(tensor<fp16, [1, 640, ?]> en, tensor<fp16, [1, 128]> style_s) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"en", [1, 640, 133]}}), ("RangeDims", {{"en", [[1, 1], [640, 640], [1, 2000]]}})))] {
+            tensor<int32, [3]> transpose_0_perm_0 = const()[name = tensor<string, []>("transpose_0_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])];
+            tensor<string, []> x_batch_first_direction_0 = const()[name = tensor<string, []>("x_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> x_batch_first_output_sequence_0 = const()[name = tensor<string, []>("x_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> x_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("x_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> x_batch_first_cell_activation_0 = const()[name = tensor<string, []>("x_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> x_batch_first_activation_0 = const()[name = tensor<string, []>("x_batch_first_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp16, [1, 512]> x_batch_first_lstm_h0_reshaped_to_fp16 = const()[name = tensor<string, []>("x_batch_first_lstm_h0_reshaped_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp16, [1024, 640]> concat_4_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1152))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(656576))), name = tensor<string, []>("concat_4_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
+            tensor<fp16, [1024, 256]> concat_5_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(657152))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(919360))), name = tensor<string, []>("concat_5_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
+            tensor<fp16, [1024]> add_0_to_fp16 = const()[name = tensor<string, []>("add_0_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(919936)))];
+            tensor<fp16, [1024, 640]> concat_6_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [655360]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(922048))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1577472))), name = tensor<string, []>("concat_6_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 640])];
+            tensor<fp16, [1024, 256]> concat_7_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1578048))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1840256))), name = tensor<string, []>("concat_7_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 256])];
+            tensor<fp16, [1024]> add_1_to_fp16 = const()[name = tensor<string, []>("add_1_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1840832)))];
+            tensor<fp16, [?, 1, 640]> transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = en)[name = tensor<string, []>("transpose_2")];
+            tensor<fp16, [?, 1, 512]> x_batch_first_cast_fp16_0, tensor<fp16, [1, 512]> x_batch_first_cast_fp16_1, tensor<fp16, [1, 512]> x_batch_first_cast_fp16_2 = lstm(activation = x_batch_first_activation_0, bias = add_0_to_fp16, bias_back = add_1_to_fp16, cell_activation = x_batch_first_cell_activation_0, direction = x_batch_first_direction_0, initial_c = x_batch_first_lstm_h0_reshaped_to_fp16, initial_h = x_batch_first_lstm_h0_reshaped_to_fp16, output_sequence = x_batch_first_output_sequence_0, recurrent_activation = x_batch_first_recurrent_activation_0, weight_hh = concat_5_to_fp16_palettized, weight_hh_back = concat_7_to_fp16_palettized, weight_ih = concat_4_to_fp16_palettized, weight_ih_back = concat_6_to_fp16_palettized, x = transpose_0_cast_fp16)[name = tensor<string, []>("x_batch_first_cast_fp16")];
+            tensor<fp32, []> var_53 = const()[name = tensor<string, []>("op_53"), val = tensor<fp32, []>(0x1.99999ap-3)];
+            tensor<fp16, [1024, 128]> F0_0_norm1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1842944))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1974080))), name = tensor<string, []>("F0_0_norm1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 128])];
+            tensor<fp16, [1024]> F0_0_norm1_fc_bias_to_fp16 = const()[name = tensor<string, []>("F0_0_norm1_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1974656)))];
+            tensor<fp16, [1, 1024]> linear_0_cast_fp16 = linear(bias = F0_0_norm1_fc_bias_to_fp16, weight = F0_0_norm1_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_0_cast_fp16")];
+            tensor<int32, [3]> var_79 = const()[name = tensor<string, []>("op_79"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp16, [1, 1024, 1]> h_3_cast_fp16 = reshape(shape = var_79, x = linear_0_cast_fp16)[name = tensor<string, []>("h_3_cast_fp16")];
+            tensor<int32, [2]> var_81_split_sizes_0 = const()[name = tensor<string, []>("op_81_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            tensor<int32, []> var_81_axis_0 = const()[name = tensor<string, []>("op_81_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 512, 1]> var_81_cast_fp16_0, tensor<fp16, [1, 512, 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 = tensor<string, []>("op_81_cast_fp16")];
+            tensor<fp16, []> var_83_promoted_to_fp16 = const()[name = tensor<string, []>("op_83_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 512, 1]> var_84_cast_fp16 = add(x = var_81_cast_fp16_0, y = var_83_promoted_to_fp16)[name = tensor<string, []>("op_84_cast_fp16")];
+            tensor<fp16, [512]> F0_0_norm1_norm_weight_to_fp16 = const()[name = tensor<string, []>("F0_0_norm1_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1976768)))];
+            tensor<fp16, [512]> F0_0_norm1_norm_bias_to_fp16 = const()[name = tensor<string, []>("F0_0_norm1_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1977856)))];
+            tensor<fp16, []> var_56_to_fp16 = const()[name = tensor<string, []>("op_56_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<int32, [3]> transpose_0_perm_0_1 = const()[name = tensor<string, []>("transpose_0_perm_0_1"), val = tensor<int32, [3]>([1, 2, 0])];
+            tensor<fp16, [1, 512, ?]> transpose_0 = transpose(perm = transpose_0_perm_0_1, x = x_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_1")];
+            tensor<fp16, [1, 512, ?]> var_87_cast_fp16 = instance_norm(beta = F0_0_norm1_norm_bias_to_fp16, epsilon = var_56_to_fp16, gamma = F0_0_norm1_norm_weight_to_fp16, x = transpose_0)[name = tensor<string, []>("op_87_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> var_88_cast_fp16 = mul(x = var_84_cast_fp16, y = var_87_cast_fp16)[name = tensor<string, []>("op_88_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_5_cast_fp16 = add(x = var_88_cast_fp16, y = var_81_cast_fp16_1)[name = tensor<string, []>("input_5_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_7_cast_fp16 = leaky_relu(alpha = var_53, x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
+            tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [512, 512, 3]> weight_3_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1978944))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2765440))), name = tensor<string, []>("weight_3_to_fp16_palettized"), shape = tensor<uint32, [3]>([512, 512, 3])];
+            tensor<fp16, [512]> F0_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("F0_0_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2766016)))];
+            tensor<fp16, [1, 512, ?]> input_9_cast_fp16 = conv(bias = F0_0_conv1_bias_to_fp16, 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_3_to_fp16_palettized, x = input_7_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
+            tensor<fp16, [1024, 128]> F0_0_norm2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2767104))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2898240))), name = tensor<string, []>("F0_0_norm2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 128])];
+            tensor<fp16, [1024]> F0_0_norm2_fc_bias_to_fp16 = const()[name = tensor<string, []>("F0_0_norm2_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2898816)))];
+            tensor<fp16, [1, 1024]> linear_1_cast_fp16 = linear(bias = F0_0_norm2_fc_bias_to_fp16, weight = F0_0_norm2_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_1_cast_fp16")];
+            tensor<int32, [3]> var_105 = const()[name = tensor<string, []>("op_105"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp16, [1, 1024, 1]> h_7_cast_fp16 = reshape(shape = var_105, x = linear_1_cast_fp16)[name = tensor<string, []>("h_7_cast_fp16")];
+            tensor<int32, [2]> var_107_split_sizes_0 = const()[name = tensor<string, []>("op_107_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            tensor<int32, []> var_107_axis_0 = const()[name = tensor<string, []>("op_107_axis_0"), val = tensor<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_7_cast_fp16)[name = tensor<string, []>("op_107_cast_fp16")];
+            tensor<fp16, []> var_109_promoted_to_fp16 = const()[name = tensor<string, []>("op_109_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 512, 1]> var_110_cast_fp16 = add(x = var_107_cast_fp16_0, y = var_109_promoted_to_fp16)[name = tensor<string, []>("op_110_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> var_113_cast_fp16 = instance_norm(beta = F0_0_norm1_norm_bias_to_fp16, epsilon = var_56_to_fp16, gamma = F0_0_norm1_norm_weight_to_fp16, x = input_9_cast_fp16)[name = tensor<string, []>("op_113_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> var_114_cast_fp16 = mul(x = var_110_cast_fp16, y = var_113_cast_fp16)[name = tensor<string, []>("op_114_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_11_cast_fp16 = add(x = var_114_cast_fp16, y = var_107_cast_fp16_1)[name = tensor<string, []>("input_11_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_13_cast_fp16 = leaky_relu(alpha = var_53, x = input_11_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
+            tensor<string, []> out_1_pad_type_0 = const()[name = tensor<string, []>("out_1_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> out_1_pad_0 = const()[name = tensor<string, []>("out_1_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_1_strides_0 = const()[name = tensor<string, []>("out_1_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_1_dilations_0 = const()[name = tensor<string, []>("out_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> out_1_groups_0 = const()[name = tensor<string, []>("out_1_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [512, 512, 3]> weight_7_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2900928))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3687424))), name = tensor<string, []>("weight_7_to_fp16_palettized"), shape = tensor<uint32, [3]>([512, 512, 3])];
+            tensor<fp16, [512]> F0_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("F0_0_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3688000)))];
+            tensor<fp16, [1, 512, ?]> out_1_cast_fp16 = conv(bias = F0_0_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 = weight_7_to_fp16_palettized, x = input_13_cast_fp16)[name = tensor<string, []>("out_1_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> var_124_cast_fp16 = add(x = out_1_cast_fp16, y = transpose_0)[name = tensor<string, []>("op_124_cast_fp16")];
+            tensor<fp16, []> var_125_to_fp16 = const()[name = tensor<string, []>("op_125_to_fp16"), val = tensor<fp16, []>(0x1.6ap-1)];
+            tensor<fp16, [1, 512, ?]> input_15_cast_fp16 = mul(x = var_124_cast_fp16, y = var_125_to_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
+            tensor<fp32, []> var_130 = const()[name = tensor<string, []>("op_130"), val = tensor<fp32, []>(0x1.99999ap-3)];
+            tensor<fp16, [1024, 128]> F0_1_norm1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3689088))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3820224))), name = tensor<string, []>("F0_1_norm1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 128])];
+            tensor<fp16, [1024]> F0_1_norm1_fc_bias_to_fp16 = const()[name = tensor<string, []>("F0_1_norm1_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3820800)))];
+            tensor<fp16, [1, 1024]> linear_2_cast_fp16 = linear(bias = F0_1_norm1_fc_bias_to_fp16, weight = F0_1_norm1_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_2_cast_fp16")];
+            tensor<int32, [3]> var_166 = const()[name = tensor<string, []>("op_166"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp16, [1, 1024, 1]> h_11_cast_fp16 = reshape(shape = var_166, x = linear_2_cast_fp16)[name = tensor<string, []>("h_11_cast_fp16")];
+            tensor<int32, [2]> var_168_split_sizes_0 = const()[name = tensor<string, []>("op_168_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            tensor<int32, []> var_168_axis_0 = const()[name = tensor<string, []>("op_168_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 512, 1]> var_168_cast_fp16_0, tensor<fp16, [1, 512, 1]> var_168_cast_fp16_1 = split(axis = var_168_axis_0, split_sizes = var_168_split_sizes_0, x = h_11_cast_fp16)[name = tensor<string, []>("op_168_cast_fp16")];
+            tensor<fp16, []> var_170_promoted_to_fp16 = const()[name = tensor<string, []>("op_170_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 512, 1]> var_171_cast_fp16 = add(x = var_168_cast_fp16_0, y = var_170_promoted_to_fp16)[name = tensor<string, []>("op_171_cast_fp16")];
+            tensor<fp16, []> var_134_to_fp16 = const()[name = tensor<string, []>("op_134_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 512, ?]> var_174_cast_fp16 = instance_norm(beta = F0_0_norm1_norm_bias_to_fp16, epsilon = var_134_to_fp16, gamma = F0_0_norm1_norm_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("op_174_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> var_175_cast_fp16 = mul(x = var_171_cast_fp16, y = var_174_cast_fp16)[name = tensor<string, []>("op_175_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_17_cast_fp16 = add(x = var_175_cast_fp16, y = var_168_cast_fp16_1)[name = tensor<string, []>("input_17_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_19_cast_fp16 = leaky_relu(alpha = var_130, x = input_17_cast_fp16)[name = tensor<string, []>("input_19_cast_fp16")];
+            tensor<string, []> conv_transpose_0_pad_type_0 = const()[name = tensor<string, []>("conv_transpose_0_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> conv_transpose_0_pad_0 = const()[name = tensor<string, []>("conv_transpose_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_transpose_0_strides_0 = const()[name = tensor<string, []>("conv_transpose_0_strides_0"), val = tensor<int32, [1]>([2])];
+            tensor<int32, []> conv_transpose_0_groups_0 = const()[name = tensor<string, []>("conv_transpose_0_groups_0"), val = tensor<int32, []>(512)];
+            tensor<int32, [1]> conv_transpose_0_dilations_0 = const()[name = tensor<string, []>("conv_transpose_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [512, 1, 3]> var_178_to_fp16 = const()[name = tensor<string, []>("op_178_to_fp16"), val = tensor<fp16, [512, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3822912)))];
+            tensor<fp16, [512]> F0_1_pool_bias_to_fp16 = const()[name = tensor<string, []>("F0_1_pool_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3826048)))];
+            tensor<fp16, [1, 512, ?]> conv_transpose_0_cast_fp16 = conv_transpose(bias = F0_1_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 = var_178_to_fp16, x = input_19_cast_fp16)[name = tensor<string, []>("conv_transpose_0_cast_fp16")];
+            tensor<int32, [3]> input_21_begin_0 = const()[name = tensor<string, []>("input_21_begin_0"), val = tensor<int32, [3]>([0, 0, 1])];
+            tensor<int32, [3]> input_21_end_0 = const()[name = tensor<string, []>("input_21_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<bool, [3]> input_21_begin_mask_0 = const()[name = tensor<string, []>("input_21_begin_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<bool, [3]> input_21_end_mask_0 = const()[name = tensor<string, []>("input_21_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            tensor<fp16, [1, 512, ?]> input_21_cast_fp16 = 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_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
+            tensor<string, []> input_23_pad_type_0 = const()[name = tensor<string, []>("input_23_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_23_pad_0 = const()[name = tensor<string, []>("input_23_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_23_strides_0 = const()[name = tensor<string, []>("input_23_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_23_dilations_0 = const()[name = tensor<string, []>("input_23_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_23_groups_0 = const()[name = tensor<string, []>("input_23_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 512, 3]> weight_11_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [393216]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3827136))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4220416))), name = tensor<string, []>("weight_11_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 512, 3])];
+            tensor<fp16, [256]> F0_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("F0_1_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4220992)))];
+            tensor<fp16, [1, 256, ?]> input_23_cast_fp16 = conv(bias = F0_1_conv1_bias_to_fp16, 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_11_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor<string, []>("input_23_cast_fp16")];
+            tensor<fp16, [512, 128]> F0_1_norm2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4221568))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4287168))), name = tensor<string, []>("F0_1_norm2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> F0_1_norm2_fc_bias_to_fp16 = const()[name = tensor<string, []>("F0_1_norm2_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4287744)))];
+            tensor<fp16, [1, 512]> linear_3_cast_fp16 = linear(bias = F0_1_norm2_fc_bias_to_fp16, weight = F0_1_norm2_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_3_cast_fp16")];
+            tensor<int32, [3]> var_199 = const()[name = tensor<string, []>("op_199"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_15_cast_fp16 = reshape(shape = var_199, x = linear_3_cast_fp16)[name = tensor<string, []>("h_15_cast_fp16")];
+            tensor<int32, [2]> var_201_split_sizes_0 = const()[name = tensor<string, []>("op_201_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_201_axis_0 = const()[name = tensor<string, []>("op_201_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_201_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_201_cast_fp16_1 = split(axis = var_201_axis_0, split_sizes = var_201_split_sizes_0, x = h_15_cast_fp16)[name = tensor<string, []>("op_201_cast_fp16")];
+            tensor<fp16, []> var_203_promoted_to_fp16 = const()[name = tensor<string, []>("op_203_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_204_cast_fp16 = add(x = var_201_cast_fp16_0, y = var_203_promoted_to_fp16)[name = tensor<string, []>("op_204_cast_fp16")];
+            tensor<fp16, [256]> F0_1_norm2_norm_weight_to_fp16 = const()[name = tensor<string, []>("F0_1_norm2_norm_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4288832)))];
+            tensor<fp16, [256]> F0_1_norm2_norm_bias_to_fp16 = const()[name = tensor<string, []>("F0_1_norm2_norm_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4289408)))];
+            tensor<fp16, [1, 256, ?]> var_207_cast_fp16 = instance_norm(beta = F0_1_norm2_norm_bias_to_fp16, epsilon = var_134_to_fp16, gamma = F0_1_norm2_norm_weight_to_fp16, x = input_23_cast_fp16)[name = tensor<string, []>("op_207_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_208_cast_fp16 = mul(x = var_204_cast_fp16, y = var_207_cast_fp16)[name = tensor<string, []>("op_208_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_25_cast_fp16 = add(x = var_208_cast_fp16, y = var_201_cast_fp16_1)[name = tensor<string, []>("input_25_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_27_cast_fp16 = leaky_relu(alpha = var_130, x = input_25_cast_fp16)[name = tensor<string, []>("input_27_cast_fp16")];
+            tensor<string, []> out_3_pad_type_0 = const()[name = tensor<string, []>("out_3_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> out_3_pad_0 = const()[name = tensor<string, []>("out_3_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_3_strides_0 = const()[name = tensor<string, []>("out_3_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_3_dilations_0 = const()[name = tensor<string, []>("out_3_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> out_3_groups_0 = const()[name = tensor<string, []>("out_3_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3]> weight_15_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [196608]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4289984))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4486656))), name = tensor<string, []>("weight_15_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 3])];
+            tensor<fp16, [256]> F0_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("F0_1_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4487232)))];
+            tensor<fp16, [1, 256, ?]> out_3_cast_fp16 = conv(bias = F0_1_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 = weight_15_to_fp16_palettized, x = input_27_cast_fp16)[name = tensor<string, []>("out_3_cast_fp16")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = tensor<string, []>("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [1, 512, ?, 1]> expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = input_15_cast_fp16)[name = tensor<string, []>("expand_dims_0_cast_fp16")];
+            tensor<int32, []> upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_0_scale_factor_height_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_0_scale_factor_width_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 512, ?, 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 = tensor<string, []>("upsample_nearest_neighbor_0_cast_fp16")];
+            tensor<int32, [1]> input_29_axes_0 = const()[name = tensor<string, []>("input_29_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [1, 512, ?]> input_29_cast_fp16 = squeeze(axes = input_29_axes_0, x = upsample_nearest_neighbor_0_cast_fp16)[name = tensor<string, []>("input_29_cast_fp16")];
+            tensor<string, []> var_225_pad_type_0 = const()[name = tensor<string, []>("op_225_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> var_225_strides_0 = const()[name = tensor<string, []>("op_225_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_225_pad_0 = const()[name = tensor<string, []>("op_225_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_225_dilations_0 = const()[name = tensor<string, []>("op_225_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> var_225_groups_0 = const()[name = tensor<string, []>("op_225_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 512, 1]> weight_17_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4487808))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4618944))), name = tensor<string, []>("weight_17_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 512, 1])];
+            tensor<fp16, [1, 256, ?]> var_225_cast_fp16 = conv(dilations = var_225_dilations_0, groups = var_225_groups_0, pad = var_225_pad_0, pad_type = var_225_pad_type_0, strides = var_225_strides_0, weight = weight_17_to_fp16_palettized, x = input_29_cast_fp16)[name = tensor<string, []>("op_225_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_226_cast_fp16 = add(x = out_3_cast_fp16, y = var_225_cast_fp16)[name = tensor<string, []>("op_226_cast_fp16")];
+            tensor<fp16, []> var_227_to_fp16 = const()[name = tensor<string, []>("op_227_to_fp16"), val = tensor<fp16, []>(0x1.6ap-1)];
+            tensor<fp16, [1, 256, ?]> input_31_cast_fp16 = mul(x = var_226_cast_fp16, y = var_227_to_fp16)[name = tensor<string, []>("input_31_cast_fp16")];
+            tensor<fp32, []> var_230 = const()[name = tensor<string, []>("op_230"), val = tensor<fp32, []>(0x1.99999ap-3)];
+            tensor<fp16, [512, 128]> F0_2_norm1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4619520))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4685120))), name = tensor<string, []>("F0_2_norm1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> F0_2_norm1_fc_bias_to_fp16 = const()[name = tensor<string, []>("F0_2_norm1_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4685696)))];
+            tensor<fp16, [1, 512]> linear_4_cast_fp16 = linear(bias = F0_2_norm1_fc_bias_to_fp16, weight = F0_2_norm1_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_4_cast_fp16")];
+            tensor<int32, [3]> var_256 = const()[name = tensor<string, []>("op_256"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_19_cast_fp16 = reshape(shape = var_256, x = linear_4_cast_fp16)[name = tensor<string, []>("h_19_cast_fp16")];
+            tensor<int32, [2]> var_258_split_sizes_0 = const()[name = tensor<string, []>("op_258_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_258_axis_0 = const()[name = tensor<string, []>("op_258_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_258_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_258_cast_fp16_1 = split(axis = var_258_axis_0, split_sizes = var_258_split_sizes_0, x = h_19_cast_fp16)[name = tensor<string, []>("op_258_cast_fp16")];
+            tensor<fp16, []> var_260_promoted_to_fp16 = const()[name = tensor<string, []>("op_260_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_261_cast_fp16 = add(x = var_258_cast_fp16_0, y = var_260_promoted_to_fp16)[name = tensor<string, []>("op_261_cast_fp16")];
+            tensor<fp16, []> var_233_to_fp16 = const()[name = tensor<string, []>("op_233_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 256, ?]> var_264_cast_fp16 = instance_norm(beta = F0_1_norm2_norm_bias_to_fp16, epsilon = var_233_to_fp16, gamma = F0_1_norm2_norm_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("op_264_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_265_cast_fp16 = mul(x = var_261_cast_fp16, y = var_264_cast_fp16)[name = tensor<string, []>("op_265_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_33_cast_fp16 = add(x = var_265_cast_fp16, y = var_258_cast_fp16_1)[name = tensor<string, []>("input_33_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_35_cast_fp16 = leaky_relu(alpha = var_230, x = input_33_cast_fp16)[name = tensor<string, []>("input_35_cast_fp16")];
+            tensor<string, []> input_37_pad_type_0 = const()[name = tensor<string, []>("input_37_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_37_pad_0 = const()[name = tensor<string, []>("input_37_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_37_strides_0 = const()[name = tensor<string, []>("input_37_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_37_dilations_0 = const()[name = tensor<string, []>("input_37_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_37_groups_0 = const()[name = tensor<string, []>("input_37_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3]> weight_21_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [196608]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4686784))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4883456))), name = tensor<string, []>("weight_21_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 3])];
+            tensor<fp16, [256]> F0_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("F0_2_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4884032)))];
+            tensor<fp16, [1, 256, ?]> input_37_cast_fp16 = conv(bias = F0_2_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 = weight_21_to_fp16_palettized, x = input_35_cast_fp16)[name = tensor<string, []>("input_37_cast_fp16")];
+            tensor<fp16, [512, 128]> F0_2_norm2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4884608))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4950208))), name = tensor<string, []>("F0_2_norm2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> F0_2_norm2_fc_bias_to_fp16 = const()[name = tensor<string, []>("F0_2_norm2_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4950784)))];
+            tensor<fp16, [1, 512]> linear_5_cast_fp16 = linear(bias = F0_2_norm2_fc_bias_to_fp16, weight = F0_2_norm2_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_5_cast_fp16")];
+            tensor<int32, [3]> var_282 = const()[name = tensor<string, []>("op_282"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_23_cast_fp16 = reshape(shape = var_282, x = linear_5_cast_fp16)[name = tensor<string, []>("h_23_cast_fp16")];
+            tensor<int32, [2]> var_284_split_sizes_0 = const()[name = tensor<string, []>("op_284_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_284_axis_0 = const()[name = tensor<string, []>("op_284_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_284_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_284_cast_fp16_1 = split(axis = var_284_axis_0, split_sizes = var_284_split_sizes_0, x = h_23_cast_fp16)[name = tensor<string, []>("op_284_cast_fp16")];
+            tensor<fp16, []> var_286_promoted_to_fp16 = const()[name = tensor<string, []>("op_286_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_287_cast_fp16 = add(x = var_284_cast_fp16_0, y = var_286_promoted_to_fp16)[name = tensor<string, []>("op_287_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_290_cast_fp16 = instance_norm(beta = F0_1_norm2_norm_bias_to_fp16, epsilon = var_233_to_fp16, gamma = F0_1_norm2_norm_weight_to_fp16, x = input_37_cast_fp16)[name = tensor<string, []>("op_290_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_291_cast_fp16 = mul(x = var_287_cast_fp16, y = var_290_cast_fp16)[name = tensor<string, []>("op_291_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_39_cast_fp16 = add(x = var_291_cast_fp16, y = var_284_cast_fp16_1)[name = tensor<string, []>("input_39_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_41_cast_fp16 = leaky_relu(alpha = var_230, x = input_39_cast_fp16)[name = tensor<string, []>("input_41_cast_fp16")];
+            tensor<string, []> out_5_pad_type_0 = const()[name = tensor<string, []>("out_5_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> out_5_pad_0 = const()[name = tensor<string, []>("out_5_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_5_strides_0 = const()[name = tensor<string, []>("out_5_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_5_dilations_0 = const()[name = tensor<string, []>("out_5_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> out_5_groups_0 = const()[name = tensor<string, []>("out_5_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3]> weight_25_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [196608]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4951872))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5148544))), name = tensor<string, []>("weight_25_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 3])];
+            tensor<fp16, [256]> F0_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("F0_2_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5149120)))];
+            tensor<fp16, [1, 256, ?]> out_5_cast_fp16 = conv(bias = F0_2_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 = weight_25_to_fp16_palettized, x = input_41_cast_fp16)[name = tensor<string, []>("out_5_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_301_cast_fp16 = add(x = out_5_cast_fp16, y = input_31_cast_fp16)[name = tensor<string, []>("op_301_cast_fp16")];
+            tensor<fp16, []> var_302_to_fp16 = const()[name = tensor<string, []>("op_302_to_fp16"), val = tensor<fp16, []>(0x1.6ap-1)];
+            tensor<fp16, [1, 256, ?]> input_43_cast_fp16 = mul(x = var_301_cast_fp16, y = var_302_to_fp16)[name = tensor<string, []>("input_43_cast_fp16")];
+            tensor<string, []> var_314_pad_type_0 = const()[name = tensor<string, []>("op_314_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> var_314_strides_0 = const()[name = tensor<string, []>("op_314_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_314_pad_0 = const()[name = tensor<string, []>("op_314_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_314_dilations_0 = const()[name = tensor<string, []>("op_314_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> var_314_groups_0 = const()[name = tensor<string, []>("op_314_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> F0_proj_weight_to_fp16 = const()[name = tensor<string, []>("F0_proj_weight_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5149696)))];
+            tensor<fp16, [1]> F0_proj_bias_to_fp16 = const()[name = tensor<string, []>("F0_proj_bias_to_fp16"), val = tensor<fp16, [1]>([0x1.f08p-3])];
+            tensor<fp16, [1, 1, ?]> var_314_cast_fp16 = conv(bias = F0_proj_bias_to_fp16, dilations = var_314_dilations_0, groups = var_314_groups_0, pad = var_314_pad_0, pad_type = var_314_pad_type_0, strides = var_314_strides_0, weight = F0_proj_weight_to_fp16, x = input_43_cast_fp16)[name = tensor<string, []>("op_314_cast_fp16")];
+            tensor<int32, [1]> var_316_axes_0 = const()[name = tensor<string, []>("op_316_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, ?]> F0 = squeeze(axes = var_316_axes_0, x = var_314_cast_fp16)[name = tensor<string, []>("op_316_cast_fp16")];
+            tensor<fp32, []> var_321 = const()[name = tensor<string, []>("op_321"), val = tensor<fp32, []>(0x1.99999ap-3)];
+            tensor<fp16, [1024, 128]> N_0_norm1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5150272))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5281408))), name = tensor<string, []>("N_0_norm1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 128])];
+            tensor<fp16, [1024]> N_0_norm1_fc_bias_to_fp16 = const()[name = tensor<string, []>("N_0_norm1_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5281984)))];
+            tensor<fp16, [1, 1024]> linear_6_cast_fp16 = linear(bias = N_0_norm1_fc_bias_to_fp16, weight = N_0_norm1_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_6_cast_fp16")];
+            tensor<int32, [3]> var_347 = const()[name = tensor<string, []>("op_347"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp16, [1, 1024, 1]> h_27_cast_fp16 = reshape(shape = var_347, x = linear_6_cast_fp16)[name = tensor<string, []>("h_27_cast_fp16")];
+            tensor<int32, [2]> var_349_split_sizes_0 = const()[name = tensor<string, []>("op_349_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            tensor<int32, []> var_349_axis_0 = const()[name = tensor<string, []>("op_349_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 512, 1]> var_349_cast_fp16_0, tensor<fp16, [1, 512, 1]> var_349_cast_fp16_1 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = h_27_cast_fp16)[name = tensor<string, []>("op_349_cast_fp16")];
+            tensor<fp16, []> var_351_promoted_to_fp16 = const()[name = tensor<string, []>("op_351_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 512, 1]> var_352_cast_fp16 = add(x = var_349_cast_fp16_0, y = var_351_promoted_to_fp16)[name = tensor<string, []>("op_352_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> var_356_cast_fp16 = mul(x = var_352_cast_fp16, y = var_87_cast_fp16)[name = tensor<string, []>("op_356_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_47_cast_fp16 = add(x = var_356_cast_fp16, y = var_349_cast_fp16_1)[name = tensor<string, []>("input_47_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_49_cast_fp16 = leaky_relu(alpha = var_321, x = input_47_cast_fp16)[name = tensor<string, []>("input_49_cast_fp16")];
+            tensor<string, []> input_51_pad_type_0 = const()[name = tensor<string, []>("input_51_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_51_pad_0 = const()[name = tensor<string, []>("input_51_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_51_strides_0 = const()[name = tensor<string, []>("input_51_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_51_dilations_0 = const()[name = tensor<string, []>("input_51_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_51_groups_0 = const()[name = tensor<string, []>("input_51_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [512, 512, 3]> weight_31_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5284096))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6070592))), name = tensor<string, []>("weight_31_to_fp16_palettized"), shape = tensor<uint32, [3]>([512, 512, 3])];
+            tensor<fp16, [512]> N_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("N_0_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6071168)))];
+            tensor<fp16, [1, 512, ?]> input_51_cast_fp16 = conv(bias = N_0_conv1_bias_to_fp16, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = weight_31_to_fp16_palettized, x = input_49_cast_fp16)[name = tensor<string, []>("input_51_cast_fp16")];
+            tensor<fp16, [1024, 128]> N_0_norm2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6072256))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6203392))), name = tensor<string, []>("N_0_norm2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 128])];
+            tensor<fp16, [1024]> N_0_norm2_fc_bias_to_fp16 = const()[name = tensor<string, []>("N_0_norm2_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6203968)))];
+            tensor<fp16, [1, 1024]> linear_7_cast_fp16 = linear(bias = N_0_norm2_fc_bias_to_fp16, weight = N_0_norm2_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_7_cast_fp16")];
+            tensor<int32, [3]> var_373 = const()[name = tensor<string, []>("op_373"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp16, [1, 1024, 1]> h_31_cast_fp16 = reshape(shape = var_373, x = linear_7_cast_fp16)[name = tensor<string, []>("h_31_cast_fp16")];
+            tensor<int32, [2]> var_375_split_sizes_0 = const()[name = tensor<string, []>("op_375_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            tensor<int32, []> var_375_axis_0 = const()[name = tensor<string, []>("op_375_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 512, 1]> var_375_cast_fp16_0, tensor<fp16, [1, 512, 1]> var_375_cast_fp16_1 = split(axis = var_375_axis_0, split_sizes = var_375_split_sizes_0, x = h_31_cast_fp16)[name = tensor<string, []>("op_375_cast_fp16")];
+            tensor<fp16, []> var_377_promoted_to_fp16 = const()[name = tensor<string, []>("op_377_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 512, 1]> var_378_cast_fp16 = add(x = var_375_cast_fp16_0, y = var_377_promoted_to_fp16)[name = tensor<string, []>("op_378_cast_fp16")];
+            tensor<fp16, []> var_324_to_fp16 = const()[name = tensor<string, []>("op_324_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 512, ?]> var_381_cast_fp16 = instance_norm(beta = F0_0_norm1_norm_bias_to_fp16, epsilon = var_324_to_fp16, gamma = F0_0_norm1_norm_weight_to_fp16, x = input_51_cast_fp16)[name = tensor<string, []>("op_381_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> var_382_cast_fp16 = mul(x = var_378_cast_fp16, y = var_381_cast_fp16)[name = tensor<string, []>("op_382_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_53_cast_fp16 = add(x = var_382_cast_fp16, y = var_375_cast_fp16_1)[name = tensor<string, []>("input_53_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_55_cast_fp16 = leaky_relu(alpha = var_321, x = input_53_cast_fp16)[name = tensor<string, []>("input_55_cast_fp16")];
+            tensor<string, []> out_7_pad_type_0 = const()[name = tensor<string, []>("out_7_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> out_7_pad_0 = const()[name = tensor<string, []>("out_7_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_7_strides_0 = const()[name = tensor<string, []>("out_7_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_7_dilations_0 = const()[name = tensor<string, []>("out_7_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> out_7_groups_0 = const()[name = tensor<string, []>("out_7_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [512, 512, 3]> weight_35_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6206080))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6992576))), name = tensor<string, []>("weight_35_to_fp16_palettized"), shape = tensor<uint32, [3]>([512, 512, 3])];
+            tensor<fp16, [512]> N_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("N_0_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6993152)))];
+            tensor<fp16, [1, 512, ?]> out_7_cast_fp16 = conv(bias = N_0_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 = weight_35_to_fp16_palettized, x = input_55_cast_fp16)[name = tensor<string, []>("out_7_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> var_392_cast_fp16 = add(x = out_7_cast_fp16, y = transpose_0)[name = tensor<string, []>("op_392_cast_fp16")];
+            tensor<fp16, []> var_393_to_fp16 = const()[name = tensor<string, []>("op_393_to_fp16"), val = tensor<fp16, []>(0x1.6ap-1)];
+            tensor<fp16, [1, 512, ?]> input_57_cast_fp16 = mul(x = var_392_cast_fp16, y = var_393_to_fp16)[name = tensor<string, []>("input_57_cast_fp16")];
+            tensor<fp32, []> var_398 = const()[name = tensor<string, []>("op_398"), val = tensor<fp32, []>(0x1.99999ap-3)];
+            tensor<fp16, [1024, 128]> N_1_norm1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6994240))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7125376))), name = tensor<string, []>("N_1_norm1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 128])];
+            tensor<fp16, [1024]> N_1_norm1_fc_bias_to_fp16 = const()[name = tensor<string, []>("N_1_norm1_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7125952)))];
+            tensor<fp16, [1, 1024]> linear_8_cast_fp16 = linear(bias = N_1_norm1_fc_bias_to_fp16, weight = N_1_norm1_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_8_cast_fp16")];
+            tensor<int32, [3]> var_434 = const()[name = tensor<string, []>("op_434"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp16, [1, 1024, 1]> h_35_cast_fp16 = reshape(shape = var_434, x = linear_8_cast_fp16)[name = tensor<string, []>("h_35_cast_fp16")];
+            tensor<int32, [2]> var_436_split_sizes_0 = const()[name = tensor<string, []>("op_436_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            tensor<int32, []> var_436_axis_0 = const()[name = tensor<string, []>("op_436_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 512, 1]> var_436_cast_fp16_0, tensor<fp16, [1, 512, 1]> var_436_cast_fp16_1 = split(axis = var_436_axis_0, split_sizes = var_436_split_sizes_0, x = h_35_cast_fp16)[name = tensor<string, []>("op_436_cast_fp16")];
+            tensor<fp16, []> var_438_promoted_to_fp16 = const()[name = tensor<string, []>("op_438_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 512, 1]> var_439_cast_fp16 = add(x = var_436_cast_fp16_0, y = var_438_promoted_to_fp16)[name = tensor<string, []>("op_439_cast_fp16")];
+            tensor<fp16, []> var_402_to_fp16 = const()[name = tensor<string, []>("op_402_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 512, ?]> var_442_cast_fp16 = instance_norm(beta = F0_0_norm1_norm_bias_to_fp16, epsilon = var_402_to_fp16, gamma = F0_0_norm1_norm_weight_to_fp16, x = input_57_cast_fp16)[name = tensor<string, []>("op_442_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> var_443_cast_fp16 = mul(x = var_439_cast_fp16, y = var_442_cast_fp16)[name = tensor<string, []>("op_443_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_59_cast_fp16 = add(x = var_443_cast_fp16, y = var_436_cast_fp16_1)[name = tensor<string, []>("input_59_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_61_cast_fp16 = leaky_relu(alpha = var_398, x = input_59_cast_fp16)[name = tensor<string, []>("input_61_cast_fp16")];
+            tensor<string, []> conv_transpose_1_pad_type_0 = const()[name = tensor<string, []>("conv_transpose_1_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> conv_transpose_1_pad_0 = const()[name = tensor<string, []>("conv_transpose_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_transpose_1_strides_0 = const()[name = tensor<string, []>("conv_transpose_1_strides_0"), val = tensor<int32, [1]>([2])];
+            tensor<int32, []> conv_transpose_1_groups_0 = const()[name = tensor<string, []>("conv_transpose_1_groups_0"), val = tensor<int32, []>(512)];
+            tensor<int32, [1]> conv_transpose_1_dilations_0 = const()[name = tensor<string, []>("conv_transpose_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [512, 1, 3]> var_446_to_fp16 = const()[name = tensor<string, []>("op_446_to_fp16"), val = tensor<fp16, [512, 1, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7128064)))];
+            tensor<fp16, [512]> N_1_pool_bias_to_fp16 = const()[name = tensor<string, []>("N_1_pool_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7131200)))];
+            tensor<fp16, [1, 512, ?]> conv_transpose_1_cast_fp16 = conv_transpose(bias = N_1_pool_bias_to_fp16, 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_446_to_fp16, x = input_61_cast_fp16)[name = tensor<string, []>("conv_transpose_1_cast_fp16")];
+            tensor<int32, [3]> input_63_begin_0 = const()[name = tensor<string, []>("input_63_begin_0"), val = tensor<int32, [3]>([0, 0, 1])];
+            tensor<int32, [3]> input_63_end_0 = const()[name = tensor<string, []>("input_63_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<bool, [3]> input_63_begin_mask_0 = const()[name = tensor<string, []>("input_63_begin_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<bool, [3]> input_63_end_mask_0 = const()[name = tensor<string, []>("input_63_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            tensor<fp16, [1, 512, ?]> input_63_cast_fp16 = 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_cast_fp16)[name = tensor<string, []>("input_63_cast_fp16")];
+            tensor<string, []> input_65_pad_type_0 = const()[name = tensor<string, []>("input_65_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_65_pad_0 = const()[name = tensor<string, []>("input_65_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_65_strides_0 = const()[name = tensor<string, []>("input_65_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_65_dilations_0 = const()[name = tensor<string, []>("input_65_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_65_groups_0 = const()[name = tensor<string, []>("input_65_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 512, 3]> weight_39_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [393216]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7132288))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7525568))), name = tensor<string, []>("weight_39_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 512, 3])];
+            tensor<fp16, [256]> N_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("N_1_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7526144)))];
+            tensor<fp16, [1, 256, ?]> input_65_cast_fp16 = conv(bias = N_1_conv1_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_39_to_fp16_palettized, x = input_63_cast_fp16)[name = tensor<string, []>("input_65_cast_fp16")];
+            tensor<fp16, [512, 128]> N_1_norm2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7526720))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7592320))), name = tensor<string, []>("N_1_norm2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> N_1_norm2_fc_bias_to_fp16 = const()[name = tensor<string, []>("N_1_norm2_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7592896)))];
+            tensor<fp16, [1, 512]> linear_9_cast_fp16 = linear(bias = N_1_norm2_fc_bias_to_fp16, weight = N_1_norm2_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_9_cast_fp16")];
+            tensor<int32, [3]> var_467 = const()[name = tensor<string, []>("op_467"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_39_cast_fp16 = reshape(shape = var_467, x = linear_9_cast_fp16)[name = tensor<string, []>("h_39_cast_fp16")];
+            tensor<int32, [2]> var_469_split_sizes_0 = const()[name = tensor<string, []>("op_469_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_469_axis_0 = const()[name = tensor<string, []>("op_469_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_469_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_469_cast_fp16_1 = split(axis = var_469_axis_0, split_sizes = var_469_split_sizes_0, x = h_39_cast_fp16)[name = tensor<string, []>("op_469_cast_fp16")];
+            tensor<fp16, []> var_471_promoted_to_fp16 = const()[name = tensor<string, []>("op_471_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_472_cast_fp16 = add(x = var_469_cast_fp16_0, y = var_471_promoted_to_fp16)[name = tensor<string, []>("op_472_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_475_cast_fp16 = instance_norm(beta = F0_1_norm2_norm_bias_to_fp16, epsilon = var_402_to_fp16, gamma = F0_1_norm2_norm_weight_to_fp16, x = input_65_cast_fp16)[name = tensor<string, []>("op_475_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_476_cast_fp16 = mul(x = var_472_cast_fp16, y = var_475_cast_fp16)[name = tensor<string, []>("op_476_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_67_cast_fp16 = add(x = var_476_cast_fp16, y = var_469_cast_fp16_1)[name = tensor<string, []>("input_67_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_69_cast_fp16 = leaky_relu(alpha = var_398, x = input_67_cast_fp16)[name = tensor<string, []>("input_69_cast_fp16")];
+            tensor<string, []> out_9_pad_type_0 = const()[name = tensor<string, []>("out_9_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> out_9_pad_0 = const()[name = tensor<string, []>("out_9_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_9_strides_0 = const()[name = tensor<string, []>("out_9_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_9_dilations_0 = const()[name = tensor<string, []>("out_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> out_9_groups_0 = const()[name = tensor<string, []>("out_9_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3]> weight_43_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [196608]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7593984))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7790656))), name = tensor<string, []>("weight_43_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 3])];
+            tensor<fp16, [256]> N_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("N_1_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7791232)))];
+            tensor<fp16, [1, 256, ?]> out_9_cast_fp16 = conv(bias = N_1_conv2_bias_to_fp16, 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_43_to_fp16_palettized, x = input_69_cast_fp16)[name = tensor<string, []>("out_9_cast_fp16")];
+            tensor<int32, [1]> expand_dims_1_axes_0 = const()[name = tensor<string, []>("expand_dims_1_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [1, 512, ?, 1]> expand_dims_1_cast_fp16 = expand_dims(axes = expand_dims_1_axes_0, x = input_57_cast_fp16)[name = tensor<string, []>("expand_dims_1_cast_fp16")];
+            tensor<int32, []> upsample_nearest_neighbor_1_scale_factor_height_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_1_scale_factor_height_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> upsample_nearest_neighbor_1_scale_factor_width_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_1_scale_factor_width_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 512, ?, 1]> upsample_nearest_neighbor_1_cast_fp16 = 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_cast_fp16)[name = tensor<string, []>("upsample_nearest_neighbor_1_cast_fp16")];
+            tensor<int32, [1]> input_71_axes_0 = const()[name = tensor<string, []>("input_71_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [1, 512, ?]> input_71_cast_fp16 = squeeze(axes = input_71_axes_0, x = upsample_nearest_neighbor_1_cast_fp16)[name = tensor<string, []>("input_71_cast_fp16")];
+            tensor<string, []> var_493_pad_type_0 = const()[name = tensor<string, []>("op_493_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> var_493_strides_0 = const()[name = tensor<string, []>("op_493_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_493_pad_0 = const()[name = tensor<string, []>("op_493_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_493_dilations_0 = const()[name = tensor<string, []>("op_493_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> var_493_groups_0 = const()[name = tensor<string, []>("op_493_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 512, 1]> weight_45_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7791808))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7922944))), name = tensor<string, []>("weight_45_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 512, 1])];
+            tensor<fp16, [1, 256, ?]> var_493_cast_fp16 = conv(dilations = var_493_dilations_0, groups = var_493_groups_0, pad = var_493_pad_0, pad_type = var_493_pad_type_0, strides = var_493_strides_0, weight = weight_45_to_fp16_palettized, x = input_71_cast_fp16)[name = tensor<string, []>("op_493_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_494_cast_fp16 = add(x = out_9_cast_fp16, y = var_493_cast_fp16)[name = tensor<string, []>("op_494_cast_fp16")];
+            tensor<fp16, []> var_495_to_fp16 = const()[name = tensor<string, []>("op_495_to_fp16"), val = tensor<fp16, []>(0x1.6ap-1)];
+            tensor<fp16, [1, 256, ?]> input_73_cast_fp16 = mul(x = var_494_cast_fp16, y = var_495_to_fp16)[name = tensor<string, []>("input_73_cast_fp16")];
+            tensor<fp32, []> var_498 = const()[name = tensor<string, []>("op_498"), val = tensor<fp32, []>(0x1.99999ap-3)];
+            tensor<fp16, [512, 128]> N_2_norm1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7923520))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7989120))), name = tensor<string, []>("N_2_norm1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> N_2_norm1_fc_bias_to_fp16 = const()[name = tensor<string, []>("N_2_norm1_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7989696)))];
+            tensor<fp16, [1, 512]> linear_10_cast_fp16 = linear(bias = N_2_norm1_fc_bias_to_fp16, weight = N_2_norm1_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_10_cast_fp16")];
+            tensor<int32, [3]> var_524 = const()[name = tensor<string, []>("op_524"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_43_cast_fp16 = reshape(shape = var_524, x = linear_10_cast_fp16)[name = tensor<string, []>("h_43_cast_fp16")];
+            tensor<int32, [2]> var_526_split_sizes_0 = const()[name = tensor<string, []>("op_526_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_526_axis_0 = const()[name = tensor<string, []>("op_526_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_526_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_526_cast_fp16_1 = split(axis = var_526_axis_0, split_sizes = var_526_split_sizes_0, x = h_43_cast_fp16)[name = tensor<string, []>("op_526_cast_fp16")];
+            tensor<fp16, []> var_528_promoted_to_fp16 = const()[name = tensor<string, []>("op_528_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_529_cast_fp16 = add(x = var_526_cast_fp16_0, y = var_528_promoted_to_fp16)[name = tensor<string, []>("op_529_cast_fp16")];
+            tensor<fp16, []> var_501_to_fp16 = const()[name = tensor<string, []>("op_501_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 256, ?]> var_532_cast_fp16 = instance_norm(beta = F0_1_norm2_norm_bias_to_fp16, epsilon = var_501_to_fp16, gamma = F0_1_norm2_norm_weight_to_fp16, x = input_73_cast_fp16)[name = tensor<string, []>("op_532_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_533_cast_fp16 = mul(x = var_529_cast_fp16, y = var_532_cast_fp16)[name = tensor<string, []>("op_533_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_75_cast_fp16 = add(x = var_533_cast_fp16, y = var_526_cast_fp16_1)[name = tensor<string, []>("input_75_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_77_cast_fp16 = leaky_relu(alpha = var_498, x = input_75_cast_fp16)[name = tensor<string, []>("input_77_cast_fp16")];
+            tensor<string, []> input_79_pad_type_0 = const()[name = tensor<string, []>("input_79_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_79_pad_0 = const()[name = tensor<string, []>("input_79_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_79_strides_0 = const()[name = tensor<string, []>("input_79_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_79_dilations_0 = const()[name = tensor<string, []>("input_79_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_79_groups_0 = const()[name = tensor<string, []>("input_79_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3]> weight_49_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [196608]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7990784))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8187456))), name = tensor<string, []>("weight_49_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 3])];
+            tensor<fp16, [256]> N_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("N_2_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8188032)))];
+            tensor<fp16, [1, 256, ?]> input_79_cast_fp16 = conv(bias = N_2_conv1_bias_to_fp16, 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_49_to_fp16_palettized, x = input_77_cast_fp16)[name = tensor<string, []>("input_79_cast_fp16")];
+            tensor<fp16, [512, 128]> N_2_norm2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8188608))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8254208))), name = tensor<string, []>("N_2_norm2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> N_2_norm2_fc_bias_to_fp16 = const()[name = tensor<string, []>("N_2_norm2_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8254784)))];
+            tensor<fp16, [1, 512]> linear_11_cast_fp16 = linear(bias = N_2_norm2_fc_bias_to_fp16, weight = N_2_norm2_fc_weight_to_fp16_palettized, x = style_s)[name = tensor<string, []>("linear_11_cast_fp16")];
+            tensor<int32, [3]> var_550 = const()[name = tensor<string, []>("op_550"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_cast_fp16 = reshape(shape = var_550, x = linear_11_cast_fp16)[name = tensor<string, []>("h_cast_fp16")];
+            tensor<int32, [2]> var_552_split_sizes_0 = const()[name = tensor<string, []>("op_552_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_552_axis_0 = const()[name = tensor<string, []>("op_552_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_552_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_552_cast_fp16_1 = split(axis = var_552_axis_0, split_sizes = var_552_split_sizes_0, x = h_cast_fp16)[name = tensor<string, []>("op_552_cast_fp16")];
+            tensor<fp16, []> var_554_promoted_to_fp16 = const()[name = tensor<string, []>("op_554_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_555_cast_fp16 = add(x = var_552_cast_fp16_0, y = var_554_promoted_to_fp16)[name = tensor<string, []>("op_555_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_558_cast_fp16 = instance_norm(beta = F0_1_norm2_norm_bias_to_fp16, epsilon = var_501_to_fp16, gamma = F0_1_norm2_norm_weight_to_fp16, x = input_79_cast_fp16)[name = tensor<string, []>("op_558_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_559_cast_fp16 = mul(x = var_555_cast_fp16, y = var_558_cast_fp16)[name = tensor<string, []>("op_559_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_81_cast_fp16 = add(x = var_559_cast_fp16, y = var_552_cast_fp16_1)[name = tensor<string, []>("input_81_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_83_cast_fp16 = leaky_relu(alpha = var_498, x = input_81_cast_fp16)[name = tensor<string, []>("input_83_cast_fp16")];
+            tensor<string, []> out_pad_type_0 = const()[name = tensor<string, []>("out_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> out_pad_0 = const()[name = tensor<string, []>("out_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_strides_0 = const()[name = tensor<string, []>("out_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_dilations_0 = const()[name = tensor<string, []>("out_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> out_groups_0 = const()[name = tensor<string, []>("out_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3]> weight_53_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [196608]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8255872))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8452544))), name = tensor<string, []>("weight_53_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 3])];
+            tensor<fp16, [256]> N_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("N_2_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8453120)))];
+            tensor<fp16, [1, 256, ?]> out_cast_fp16 = conv(bias = N_2_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 = weight_53_to_fp16_palettized, x = input_83_cast_fp16)[name = tensor<string, []>("out_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_569_cast_fp16 = add(x = out_cast_fp16, y = input_73_cast_fp16)[name = tensor<string, []>("op_569_cast_fp16")];
+            tensor<fp16, []> var_570_to_fp16 = const()[name = tensor<string, []>("op_570_to_fp16"), val = tensor<fp16, []>(0x1.6ap-1)];
+            tensor<fp16, [1, 256, ?]> input_cast_fp16 = mul(x = var_569_cast_fp16, y = var_570_to_fp16)[name = tensor<string, []>("input_cast_fp16")];
+            tensor<string, []> var_582_pad_type_0 = const()[name = tensor<string, []>("op_582_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> var_582_strides_0 = const()[name = tensor<string, []>("op_582_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_582_pad_0 = const()[name = tensor<string, []>("op_582_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_582_dilations_0 = const()[name = tensor<string, []>("op_582_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> var_582_groups_0 = const()[name = tensor<string, []>("op_582_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> N_proj_weight_to_fp16 = const()[name = tensor<string, []>("N_proj_weight_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8453696)))];
+            tensor<fp16, [1]> N_proj_bias_to_fp16 = const()[name = tensor<string, []>("N_proj_bias_to_fp16"), val = tensor<fp16, [1]>([0x1.3fp-5])];
+            tensor<fp16, [1, 1, ?]> var_582_cast_fp16 = conv(bias = N_proj_bias_to_fp16, dilations = var_582_dilations_0, groups = var_582_groups_0, pad = var_582_pad_0, pad_type = var_582_pad_type_0, strides = var_582_strides_0, weight = N_proj_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("op_582_cast_fp16")];
+            tensor<int32, [1]> var_584_axes_0 = const()[name = tensor<string, []>("op_584_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, ?]> N = squeeze(axes = var_584_axes_0, x = var_582_cast_fp16)[name = tensor<string, []>("op_584_cast_fp16")];
+        } -> (F0, N);
+}
\ No newline at end of file
diff --git a/ANE/ANE-zh/KokoroProsody.mlmodelc/weights/weight.bin b/ANE/ANE-zh/KokoroProsody.mlmodelc/weights/weight.bin
new file mode 100644
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--- /dev/null
+++ b/ANE/ANE-zh/KokoroProsody.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
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new file mode 100644
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--- /dev/null
+++ b/ANE/ANE-zh/KokoroProsody.mlpackage/Data/com.apple.CoreML/model.mlmodel
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diff --git a/ANE/ANE-zh/KokoroProsody.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/ANE/ANE-zh/KokoroProsody.mlpackage/Data/com.apple.CoreML/weights/weight.bin
new file mode 100644
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--- /dev/null
+++ b/ANE/ANE-zh/KokoroProsody.mlpackage/Data/com.apple.CoreML/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d7229d31a47e1d5c054c24b7aed8ce0df20460523472a55ff998f5939a75cf8
+size 8454272
diff --git a/ANE/ANE-zh/KokoroProsody.mlpackage/Manifest.json b/ANE/ANE-zh/KokoroProsody.mlpackage/Manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..6644288772e90fa640704d2e99e820672d9ae80d
--- /dev/null
+++ b/ANE/ANE-zh/KokoroProsody.mlpackage/Manifest.json
@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "A584E1D6-B88E-412B-83F8-60F488E36916": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        },
+        "C7F601FE-E520-4ADA-B688-25A6DC60355C": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "A584E1D6-B88E-412B-83F8-60F488E36916"
+}
diff --git a/ANE/ANE-zh/KokoroTail.mlmodelc/analytics/coremldata.bin b/ANE/ANE-zh/KokoroTail.mlmodelc/analytics/coremldata.bin
new file mode 100644
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--- /dev/null
+++ b/ANE/ANE-zh/KokoroTail.mlmodelc/analytics/coremldata.bin
@@ -0,0 +1,3 @@
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diff --git a/ANE/ANE-zh/KokoroTail.mlmodelc/coremldata.bin b/ANE/ANE-zh/KokoroTail.mlmodelc/coremldata.bin
new file mode 100644
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diff --git a/ANE/ANE-zh/KokoroTail.mlmodelc/metadata.json b/ANE/ANE-zh/KokoroTail.mlmodelc/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..69e83aa3c1467fb18dc1d20312e7eb16001a39a0
--- /dev/null
+++ b/ANE/ANE-zh/KokoroTail.mlmodelc/metadata.json
@@ -0,0 +1,70 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float32",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32)",
+        "shortDescription" : "",
+        "shape" : "[]",
+        "name" : "audio",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios17.sin" : 2,
+      "Ios17.convTranspose" : 2,
+      "Ios17.conv" : 1,
+      "Ios17.cos" : 1,
+      "Ios17.sliceByIndex" : 3,
+      "Ios17.mul" : 2,
+      "Ios17.sub" : 1,
+      "Ios17.exp" : 1
+    },
+    "computePrecision" : "Mixed (Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2026-05-03",
+      "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 × 128 × 100...240001",
+        "shapeRange" : "[[1, 1], [128, 128], [100, 240001]]",
+        "formattedType" : "MultiArray (Float32 1 × 128 × 15961)",
+        "type" : "MultiArray",
+        "shape" : "[1, 128, 15961]",
+        "name" : "x_pre",
+        "shortDescription" : ""
+      }
+    ],
+    "generatedClassName" : "KokoroTail",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/ANE/ANE-zh/KokoroTail.mlmodelc/model.mil b/ANE/ANE-zh/KokoroTail.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..58f3f6b0bac4231786156f0b5669b4b6ef96438e
--- /dev/null
+++ b/ANE/ANE-zh/KokoroTail.mlmodelc/model.mil
@@ -0,0 +1,47 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<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<ios17>(tensor<fp32, [1, 128, ?]> x_pre) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"x_pre", [1, 128, 15961]}}), ("RangeDims", {{"x_pre", [[1, 1], [128, 128], [100, 240001]]}})))] {
+            tensor<fp32, [22]> conv_post_bias = const()[name = tensor<string, []>("conv_post_bias"), val = tensor<fp32, [22]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp32, [11, 1, 20]> stft_deconv_real_weight = const()[name = tensor<string, []>("stft_deconv_real_weight"), val = tensor<fp32, [11, 1, 20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(256)))];
+            tensor<fp32, [11, 1, 20]> stft_deconv_imag_weight = const()[name = tensor<string, []>("stft_deconv_imag_weight"), val = tensor<fp32, [11, 1, 20]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1216)))];
+            tensor<fp32, [22, 128, 7]> weight_1 = const()[name = tensor<string, []>("weight_1"), val = tensor<fp32, [22, 128, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2176)))];
+            tensor<string, []> x_pad_type_0 = const()[name = tensor<string, []>("x_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> x_pad_0 = const()[name = tensor<string, []>("x_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> x_strides_0 = const()[name = tensor<string, []>("x_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> x_dilations_0 = const()[name = tensor<string, []>("x_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_groups_0 = const()[name = tensor<string, []>("x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 22, ?]> x = conv(bias = conv_post_bias, dilations = x_dilations_0, groups = x_groups_0, pad = x_pad_0, pad_type = x_pad_type_0, strides = x_strides_0, weight = weight_1, x = x_pre)[name = tensor<string, []>("x")];
+            tensor<int32, [3]> var_33_begin_0 = const()[name = tensor<string, []>("op_33_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> var_33_end_0 = const()[name = tensor<string, []>("op_33_end_0"), val = tensor<int32, [3]>([1, 11, 0])];
+            tensor<bool, [3]> var_33_end_mask_0 = const()[name = tensor<string, []>("op_33_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
+            tensor<fp32, [1, 11, ?]> var_33 = slice_by_index(begin = var_33_begin_0, end = var_33_end_0, end_mask = var_33_end_mask_0, x = x)[name = tensor<string, []>("op_33")];
+            tensor<fp32, [1, 11, ?]> magnitude = exp(x = var_33)[name = tensor<string, []>("magnitude")];
+            tensor<int32, [3]> var_49_begin_0 = const()[name = tensor<string, []>("op_49_begin_0"), val = tensor<int32, [3]>([0, 11, 0])];
+            tensor<int32, [3]> var_49_end_0 = const()[name = tensor<string, []>("op_49_end_0"), val = tensor<int32, [3]>([1, 22, 0])];
+            tensor<bool, [3]> var_49_end_mask_0 = const()[name = tensor<string, []>("op_49_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            tensor<fp32, [1, 11, ?]> var_49 = slice_by_index(begin = var_49_begin_0, end = var_49_end_0, end_mask = var_49_end_mask_0, x = x)[name = tensor<string, []>("op_49")];
+            tensor<fp32, [1, 11, ?]> phase = sin(x = var_49)[name = tensor<string, []>("phase")];
+            tensor<fp32, [1, 11, ?]> var_56 = cos(x = phase)[name = tensor<string, []>("op_56")];
+            tensor<fp32, [1, 11, ?]> input_1 = mul(x = magnitude, y = var_56)[name = tensor<string, []>("input_1")];
+            tensor<fp32, [1, 11, ?]> var_58 = sin(x = phase)[name = tensor<string, []>("op_58")];
+            tensor<fp32, [1, 11, ?]> input = mul(x = magnitude, y = var_58)[name = tensor<string, []>("input")];
+            tensor<string, []> var_71_pad_type_0 = const()[name = tensor<string, []>("op_71_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> var_71_strides_0 = const()[name = tensor<string, []>("op_71_strides_0"), val = tensor<int32, [1]>([5])];
+            tensor<int32, [2]> var_71_pad_0 = const()[name = tensor<string, []>("op_71_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_71_dilations_0 = const()[name = tensor<string, []>("op_71_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> var_71_groups_0 = const()[name = tensor<string, []>("op_71_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 1, ?]> var_71 = conv_transpose(dilations = var_71_dilations_0, groups = var_71_groups_0, pad = var_71_pad_0, pad_type = var_71_pad_type_0, strides = var_71_strides_0, weight = stft_deconv_real_weight, x = input_1)[name = tensor<string, []>("op_71")];
+            tensor<string, []> var_83_pad_type_0 = const()[name = tensor<string, []>("op_83_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> var_83_strides_0 = const()[name = tensor<string, []>("op_83_strides_0"), val = tensor<int32, [1]>([5])];
+            tensor<int32, [2]> var_83_pad_0 = const()[name = tensor<string, []>("op_83_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_83_dilations_0 = const()[name = tensor<string, []>("op_83_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> var_83_groups_0 = const()[name = tensor<string, []>("op_83_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [1, 1, ?]> var_83 = conv_transpose(dilations = var_83_dilations_0, groups = var_83_groups_0, pad = var_83_pad_0, pad_type = var_83_pad_type_0, strides = var_83_strides_0, weight = stft_deconv_imag_weight, x = input)[name = tensor<string, []>("op_83")];
+            tensor<fp32, [1, 1, ?]> waveform = sub(x = var_71, y = var_83)[name = tensor<string, []>("waveform")];
+            tensor<int32, [3]> var_90_begin_0 = const()[name = tensor<string, []>("op_90_begin_0"), val = tensor<int32, [3]>([0, 0, 10])];
+            tensor<int32, [3]> var_90_end_0 = const()[name = tensor<string, []>("op_90_end_0"), val = tensor<int32, [3]>([1, 1, -10])];
+            tensor<bool, [3]> var_90_end_mask_0 = const()[name = tensor<string, []>("op_90_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<fp32, [1, 1, ?]> audio = slice_by_index(begin = var_90_begin_0, end = var_90_end_0, end_mask = var_90_end_mask_0, x = waveform)[name = tensor<string, []>("op_90")];
+        } -> (audio);
+}
\ No newline at end of file
diff --git a/ANE/ANE-zh/KokoroTail.mlmodelc/weights/weight.bin b/ANE/ANE-zh/KokoroTail.mlmodelc/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..7a9757a81761ee47fee86309416625a57f340848
--- /dev/null
+++ b/ANE/ANE-zh/KokoroTail.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
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diff --git a/ANE/ANE-zh/KokoroTail.mlpackage/Data/com.apple.CoreML/model.mlmodel b/ANE/ANE-zh/KokoroTail.mlpackage/Data/com.apple.CoreML/model.mlmodel
new file mode 100644
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--- /dev/null
+++ b/ANE/ANE-zh/KokoroTail.mlpackage/Data/com.apple.CoreML/model.mlmodel
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--- /dev/null
+++ b/ANE/ANE-zh/KokoroTail.mlpackage/Data/com.apple.CoreML/weights/weight.bin
@@ -0,0 +1,3 @@
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diff --git a/ANE/ANE-zh/KokoroTail.mlpackage/Manifest.json b/ANE/ANE-zh/KokoroTail.mlpackage/Manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..d6d66a562959ce7e34d55c24eab2be9eba2fb780
--- /dev/null
+++ b/ANE/ANE-zh/KokoroTail.mlpackage/Manifest.json
@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "18BB1DC6-314D-4189-A295-F891E896DE7B": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "E288AD71-C613-4173-B1DC-4CEC042D94B8": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "E288AD71-C613-4173-B1DC-4CEC042D94B8"
+}
diff --git a/ANE/ANE-zh/KokoroVocoder.mlmodelc/analytics/coremldata.bin b/ANE/ANE-zh/KokoroVocoder.mlmodelc/analytics/coremldata.bin
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diff --git a/ANE/ANE-zh/KokoroVocoder.mlmodelc/coremldata.bin b/ANE/ANE-zh/KokoroVocoder.mlmodelc/coremldata.bin
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diff --git a/ANE/ANE-zh/KokoroVocoder.mlmodelc/metadata.json b/ANE/ANE-zh/KokoroVocoder.mlmodelc/metadata.json
new file mode 100644
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--- /dev/null
+++ b/ANE/ANE-zh/KokoroVocoder.mlmodelc/metadata.json
@@ -0,0 +1,147 @@
+[
+  {
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+        "shape" : "[]",
+        "name" : "x_pre",
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+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
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+      "Ios17.concat" : 6,
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+      "Ios17.expandDims" : 4,
+      "Ios17.instanceNorm" : 42,
+      "Ios17.reshape" : 46,
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+        "shapeRange" : "[[1, 1], [2, 4000]]",
+        "formattedType" : "MultiArray (Float16 1 × 266)",
+        "type" : "MultiArray",
+        "shape" : "[1, 266]",
+        "name" : "F0_curve",
+        "shortDescription" : ""
+      },
+      {
+        "dataType" : "Float16",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 2...4000",
+        "shapeRange" : "[[1, 1], [2, 4000]]",
+        "formattedType" : "MultiArray (Float16 1 × 266)",
+        "type" : "MultiArray",
+        "shape" : "[1, 266]",
+        "name" : "N_pred",
+        "shortDescription" : ""
+      },
+      {
+        "dataType" : "Float16",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 256 × 1...40000",
+        "shapeRange" : "[[1, 1], [256, 256], [1, 40000]]",
+        "formattedType" : "MultiArray (Float16 1 × 256 × 2660)",
+        "type" : "MultiArray",
+        "shape" : "[1, 256, 2660]",
+        "name" : "x_source_0",
+        "shortDescription" : ""
+      },
+      {
+        "dataType" : "Float16",
+        "hasShapeFlexibility" : "1",
+        "isOptional" : "0",
+        "shapeFlexibility" : "1 × 128 × 1...240001",
+        "shapeRange" : "[[1, 1], [128, 128], [1, 240001]]",
+        "formattedType" : "MultiArray (Float16 1 × 128 × 15961)",
+        "type" : "MultiArray",
+        "shape" : "[1, 128, 15961]",
+        "name" : "x_source_1",
+        "shortDescription" : ""
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float16",
+        "formattedType" : "MultiArray (Float16 1 × 128)",
+        "shortDescription" : "",
+        "shape" : "[1, 128]",
+        "name" : "style_timbre",
+        "type" : "MultiArray"
+      }
+    ],
+    "generatedClassName" : "KokoroVocoder",
+    "method" : "predict"
+  }
+]
\ No newline at end of file
diff --git a/ANE/ANE-zh/KokoroVocoder.mlmodelc/model.mil b/ANE/ANE-zh/KokoroVocoder.mlmodelc/model.mil
new file mode 100644
index 0000000000000000000000000000000000000000..964c26ae9f41677e8dd67d3003f3f53d438aee30
--- /dev/null
+++ b/ANE/ANE-zh/KokoroVocoder.mlmodelc/model.mil
@@ -0,0 +1,1540 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})]
+{
+    func main<ios17>(tensor<fp16, [1, ?]> F0_curve, tensor<fp16, [1, ?]> N_pred, tensor<fp16, [1, 512, ?]> asr, tensor<fp16, [1, 128]> style_timbre, tensor<fp16, [1, 256, ?]> x_source_0, tensor<fp16, [1, 128, ?]> x_source_1) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"F0_curve", [1, 266]}, {"N_pred", [1, 266]}, {"asr", [1, 512, 133]}, {"x_source_0", [1, 256, 2660]}, {"x_source_1", [1, 128, 15961]}}), ("RangeDims", {{"F0_curve", [[1, 1], [2, 4000]]}, {"N_pred", [[1, 1], [2, 4000]]}, {"asr", [[1, 1], [512, 512], [1, 2000]]}, {"x_source_0", [[1, 1], [256, 256], [1, 40000]]}, {"x_source_1", [[1, 1], [128, 128], [1, 240001]]}})))] {
+            tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, ?]> input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = F0_curve)[name = tensor<string, []>("input_1_cast_fp16")];
+            tensor<string, []> F0_pad_type_0 = const()[name = tensor<string, []>("F0_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> F0_pad_0 = const()[name = tensor<string, []>("F0_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> F0_strides_0 = const()[name = tensor<string, []>("F0_strides_0"), val = tensor<int32, [1]>([2])];
+            tensor<int32, [1]> F0_dilations_0 = const()[name = tensor<string, []>("F0_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> F0_groups_0 = const()[name = tensor<string, []>("F0_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1, 3]> weight_1_to_fp16 = const()[name = tensor<string, []>("weight_1_to_fp16"), val = tensor<fp16, [1, 1, 3]>([[[0x1.b2cp-5, 0x1.b4cp-5, -0x1.74p-6]]])];
+            tensor<fp16, [1]> F0_conv_bias_to_fp16 = const()[name = tensor<string, []>("F0_conv_bias_to_fp16"), val = tensor<fp16, [1]>([-0x1.108p-2])];
+            tensor<fp16, [1, 1, ?]> F0_cast_fp16 = conv(bias = 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 = weight_1_to_fp16, x = input_1_cast_fp16)[name = tensor<string, []>("F0_cast_fp16")];
+            tensor<int32, [1]> input_3_axes_0 = const()[name = tensor<string, []>("input_3_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, ?]> input_3_cast_fp16 = expand_dims(axes = input_3_axes_0, x = N_pred)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<string, []> N_feat_pad_type_0 = const()[name = tensor<string, []>("N_feat_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> N_feat_pad_0 = const()[name = tensor<string, []>("N_feat_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> N_feat_strides_0 = const()[name = tensor<string, []>("N_feat_strides_0"), val = tensor<int32, [1]>([2])];
+            tensor<int32, [1]> N_feat_dilations_0 = const()[name = tensor<string, []>("N_feat_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> N_feat_groups_0 = const()[name = tensor<string, []>("N_feat_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1, 3]> weight_3_to_fp16 = const()[name = tensor<string, []>("weight_3_to_fp16"), val = tensor<fp16, [1, 1, 3]>([[[0x1.cfcp-2, 0x1.42p-1, 0x1.d8p-2]]])];
+            tensor<fp16, [1]> N_conv_bias_to_fp16 = const()[name = tensor<string, []>("N_conv_bias_to_fp16"), val = tensor<fp16, [1]>([-0x1.e24p-2])];
+            tensor<fp16, [1, 1, ?]> N_feat_cast_fp16 = conv(bias = N_conv_bias_to_fp16, dilations = N_feat_dilations_0, groups = N_feat_groups_0, pad = N_feat_pad_0, pad_type = N_feat_pad_type_0, strides = N_feat_strides_0, weight = weight_3_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("N_feat_cast_fp16")];
+            tensor<int32, []> var_86 = const()[name = tensor<string, []>("op_86"), val = tensor<int32, []>(1)];
+            tensor<bool, []> input_5_interleave_0 = const()[name = tensor<string, []>("input_5_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 514, ?]> input_5_cast_fp16 = concat(axis = var_86, interleave = input_5_interleave_0, values = (asr, F0_cast_fp16, N_feat_cast_fp16))[name = tensor<string, []>("input_5_cast_fp16")];
+            tensor<fp32, []> var_90 = const()[name = tensor<string, []>("op_90"), val = tensor<fp32, []>(0x1.99999ap-3)];
+            tensor<fp16, [1028, 128]> encode_norm1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131584]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131712))), name = tensor<string, []>("encode_norm1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1028, 128])];
+            tensor<fp16, [1028]> encode_norm1_fc_bias_to_fp16 = const()[name = tensor<string, []>("encode_norm1_fc_bias_to_fp16"), val = tensor<fp16, [1028]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132288)))];
+            tensor<fp16, [1, 1028]> linear_0_cast_fp16 = linear(bias = encode_norm1_fc_bias_to_fp16, weight = encode_norm1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_0_cast_fp16")];
+            tensor<int32, [3]> var_120 = const()[name = tensor<string, []>("op_120"), val = tensor<int32, [3]>([1, 1028, 1])];
+            tensor<fp16, [1, 1028, 1]> h_3_cast_fp16 = reshape(shape = var_120, x = linear_0_cast_fp16)[name = tensor<string, []>("h_3_cast_fp16")];
+            tensor<int32, [2]> var_122_split_sizes_0 = const()[name = tensor<string, []>("op_122_split_sizes_0"), val = tensor<int32, [2]>([514, 514])];
+            tensor<int32, []> var_122_axis_0 = const()[name = tensor<string, []>("op_122_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 514, 1]> var_122_cast_fp16_0, tensor<fp16, [1, 514, 1]> var_122_cast_fp16_1 = split(axis = var_122_axis_0, split_sizes = var_122_split_sizes_0, x = h_3_cast_fp16)[name = tensor<string, []>("op_122_cast_fp16")];
+            tensor<fp16, []> var_124_promoted_to_fp16 = const()[name = tensor<string, []>("op_124_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 514, 1]> var_125_cast_fp16 = add(x = var_122_cast_fp16_0, y = var_124_promoted_to_fp16)[name = tensor<string, []>("op_125_cast_fp16")];
+            tensor<fp16, [514]> encode_norm1_norm_weight_to_fp16 = const()[name = tensor<string, []>("encode_norm1_norm_weight_to_fp16"), val = tensor<fp16, [514]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(134464)))];
+            tensor<fp16, [514]> encode_norm1_norm_bias_to_fp16 = const()[name = tensor<string, []>("encode_norm1_norm_bias_to_fp16"), val = tensor<fp16, [514]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135616)))];
+            tensor<fp16, []> var_93_to_fp16 = const()[name = tensor<string, []>("op_93_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 514, ?]> var_128_cast_fp16 = instance_norm(beta = encode_norm1_norm_bias_to_fp16, epsilon = var_93_to_fp16, gamma = encode_norm1_norm_weight_to_fp16, x = input_5_cast_fp16)[name = tensor<string, []>("op_128_cast_fp16")];
+            tensor<fp16, [1, 514, ?]> var_129_cast_fp16 = mul(x = var_125_cast_fp16, y = var_128_cast_fp16)[name = tensor<string, []>("op_129_cast_fp16")];
+            tensor<fp16, [1, 514, ?]> input_7_cast_fp16 = add(x = var_129_cast_fp16, y = var_122_cast_fp16_1)[name = tensor<string, []>("input_7_cast_fp16")];
+            tensor<fp16, [1, 514, ?]> input_9_cast_fp16 = leaky_relu(alpha = var_90, x = input_7_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")];
+            tensor<string, []> input_13_pad_type_0 = const()[name = tensor<string, []>("input_13_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_13_pad_0 = const()[name = tensor<string, []>("input_13_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_13_strides_0 = const()[name = tensor<string, []>("input_13_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_13_dilations_0 = const()[name = tensor<string, []>("input_13_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_13_groups_0 = const()[name = tensor<string, []>("input_13_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 514, 3]> weight_7_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1579008]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(136768))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1715840))), name = tensor<string, []>("weight_7_to_fp16_palettized"), shape = tensor<uint32, [3]>([1024, 514, 3])];
+            tensor<fp16, [1024]> encode_conv1_bias_to_fp16 = const()[name = tensor<string, []>("encode_conv1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1716416)))];
+            tensor<fp16, [1, 1024, ?]> input_13_cast_fp16 = conv(bias = 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 = weight_7_to_fp16_palettized, x = input_9_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
+            tensor<fp16, [2048, 128]> encode_norm2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1718528))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1980736))), name = tensor<string, []>("encode_norm2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([2048, 128])];
+            tensor<fp16, [2048]> encode_norm2_fc_bias_to_fp16 = const()[name = tensor<string, []>("encode_norm2_fc_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1981312)))];
+            tensor<fp16, [1, 2048]> linear_1_cast_fp16 = linear(bias = encode_norm2_fc_bias_to_fp16, weight = encode_norm2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_1_cast_fp16")];
+            tensor<int32, [3]> var_147 = const()[name = tensor<string, []>("op_147"), val = tensor<int32, [3]>([1, 2048, 1])];
+            tensor<fp16, [1, 2048, 1]> h_7_cast_fp16 = reshape(shape = var_147, x = linear_1_cast_fp16)[name = tensor<string, []>("h_7_cast_fp16")];
+            tensor<int32, [2]> var_149_split_sizes_0 = const()[name = tensor<string, []>("op_149_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
+            tensor<int32, []> var_149_axis_0 = const()[name = tensor<string, []>("op_149_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 1]> var_149_cast_fp16_0, tensor<fp16, [1, 1024, 1]> var_149_cast_fp16_1 = split(axis = var_149_axis_0, split_sizes = var_149_split_sizes_0, x = h_7_cast_fp16)[name = tensor<string, []>("op_149_cast_fp16")];
+            tensor<fp16, []> var_151_promoted_to_fp16 = const()[name = tensor<string, []>("op_151_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 1024, 1]> var_152_cast_fp16 = add(x = var_149_cast_fp16_0, y = var_151_promoted_to_fp16)[name = tensor<string, []>("op_152_cast_fp16")];
+            tensor<fp16, [1024]> encode_norm2_norm_weight_to_fp16 = const()[name = tensor<string, []>("encode_norm2_norm_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1985472)))];
+            tensor<fp16, [1024]> encode_norm2_norm_bias_to_fp16 = const()[name = tensor<string, []>("encode_norm2_norm_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1987584)))];
+            tensor<fp16, [1, 1024, ?]> var_155_cast_fp16 = instance_norm(beta = encode_norm2_norm_bias_to_fp16, epsilon = var_93_to_fp16, gamma = encode_norm2_norm_weight_to_fp16, x = input_13_cast_fp16)[name = tensor<string, []>("op_155_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_156_cast_fp16 = mul(x = var_152_cast_fp16, y = var_155_cast_fp16)[name = tensor<string, []>("op_156_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_15_cast_fp16 = add(x = var_156_cast_fp16, y = var_149_cast_fp16_1)[name = tensor<string, []>("input_15_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_17_cast_fp16 = leaky_relu(alpha = var_90, x = input_15_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
+            tensor<string, []> out_1_pad_type_0 = const()[name = tensor<string, []>("out_1_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> out_1_pad_0 = const()[name = tensor<string, []>("out_1_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_1_strides_0 = const()[name = tensor<string, []>("out_1_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_1_dilations_0 = const()[name = tensor<string, []>("out_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> out_1_groups_0 = const()[name = tensor<string, []>("out_1_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 3]> weight_11_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1989696))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5135488))), name = tensor<string, []>("weight_11_to_fp16_palettized"), shape = tensor<uint32, [3]>([1024, 1024, 3])];
+            tensor<fp16, [1024]> encode_conv2_bias_to_fp16 = const()[name = tensor<string, []>("encode_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5136064)))];
+            tensor<fp16, [1, 1024, ?]> out_1_cast_fp16 = conv(bias = 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 = weight_11_to_fp16_palettized, x = input_17_cast_fp16)[name = tensor<string, []>("out_1_cast_fp16")];
+            tensor<string, []> var_172_pad_type_0 = const()[name = tensor<string, []>("op_172_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> var_172_strides_0 = const()[name = tensor<string, []>("op_172_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_172_pad_0 = const()[name = tensor<string, []>("op_172_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_172_dilations_0 = const()[name = tensor<string, []>("op_172_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> var_172_groups_0 = const()[name = tensor<string, []>("op_172_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 514, 1]> weight_13_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [526336]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5138176))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5664576))), name = tensor<string, []>("weight_13_to_fp16_palettized"), shape = tensor<uint32, [3]>([1024, 514, 1])];
+            tensor<fp16, [1, 1024, ?]> var_172_cast_fp16 = conv(dilations = var_172_dilations_0, groups = var_172_groups_0, pad = var_172_pad_0, pad_type = var_172_pad_type_0, strides = var_172_strides_0, weight = weight_13_to_fp16_palettized, x = input_5_cast_fp16)[name = tensor<string, []>("op_172_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_173_cast_fp16 = add(x = out_1_cast_fp16, y = var_172_cast_fp16)[name = tensor<string, []>("op_173_cast_fp16")];
+            tensor<fp16, []> var_174_to_fp16 = const()[name = tensor<string, []>("op_174_to_fp16"), val = tensor<fp16, []>(0x1.6ap-1)];
+            tensor<fp16, [1, 1024, ?]> x_1_cast_fp16 = mul(x = var_173_cast_fp16, y = var_174_to_fp16)[name = tensor<string, []>("x_1_cast_fp16")];
+            tensor<string, []> asr_res_1_pad_type_0 = const()[name = tensor<string, []>("asr_res_1_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> asr_res_1_strides_0 = const()[name = tensor<string, []>("asr_res_1_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> asr_res_1_pad_0 = const()[name = tensor<string, []>("asr_res_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> asr_res_1_dilations_0 = const()[name = tensor<string, []>("asr_res_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> asr_res_1_groups_0 = const()[name = tensor<string, []>("asr_res_1_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [64, 512, 1]> weight_15_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5665152))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5697984))), name = tensor<string, []>("weight_15_to_fp16_palettized"), shape = tensor<uint32, [3]>([64, 512, 1])];
+            tensor<fp16, [64]> asr_res_0_bias_to_fp16 = const()[name = tensor<string, []>("asr_res_0_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5698560)))];
+            tensor<fp16, [1, 64, ?]> asr_res_1_cast_fp16 = conv(bias = 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 = weight_15_to_fp16_palettized, x = asr)[name = tensor<string, []>("asr_res_1_cast_fp16")];
+            tensor<int32, []> var_193 = const()[name = tensor<string, []>("op_193"), val = tensor<int32, []>(1)];
+            tensor<bool, []> input_21_interleave_0 = const()[name = tensor<string, []>("input_21_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 1090, ?]> input_21_cast_fp16 = concat(axis = var_193, interleave = input_21_interleave_0, values = (x_1_cast_fp16, asr_res_1_cast_fp16, F0_cast_fp16, N_feat_cast_fp16))[name = tensor<string, []>("input_21_cast_fp16")];
+            tensor<fp32, []> var_197 = const()[name = tensor<string, []>("op_197"), val = tensor<fp32, []>(0x1.99999ap-3)];
+            tensor<fp16, [2180, 128]> decode_0_norm1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [279040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5698752))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5977856))), name = tensor<string, []>("decode_0_norm1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([2180, 128])];
+            tensor<fp16, [2180]> decode_0_norm1_fc_bias_to_fp16 = const()[name = tensor<string, []>("decode_0_norm1_fc_bias_to_fp16"), val = tensor<fp16, [2180]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5978432)))];
+            tensor<fp16, [1, 2180]> linear_2_cast_fp16 = linear(bias = decode_0_norm1_fc_bias_to_fp16, weight = decode_0_norm1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_2_cast_fp16")];
+            tensor<int32, [3]> var_227 = const()[name = tensor<string, []>("op_227"), val = tensor<int32, [3]>([1, 2180, 1])];
+            tensor<fp16, [1, 2180, 1]> h_11_cast_fp16 = reshape(shape = var_227, x = linear_2_cast_fp16)[name = tensor<string, []>("h_11_cast_fp16")];
+            tensor<int32, [2]> var_229_split_sizes_0 = const()[name = tensor<string, []>("op_229_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
+            tensor<int32, []> var_229_axis_0 = const()[name = tensor<string, []>("op_229_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1090, 1]> var_229_cast_fp16_0, tensor<fp16, [1, 1090, 1]> var_229_cast_fp16_1 = split(axis = var_229_axis_0, split_sizes = var_229_split_sizes_0, x = h_11_cast_fp16)[name = tensor<string, []>("op_229_cast_fp16")];
+            tensor<fp16, []> var_231_promoted_to_fp16 = const()[name = tensor<string, []>("op_231_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 1090, 1]> var_232_cast_fp16 = add(x = var_229_cast_fp16_0, y = var_231_promoted_to_fp16)[name = tensor<string, []>("op_232_cast_fp16")];
+            tensor<fp16, [1090]> decode_0_norm1_norm_weight_to_fp16 = const()[name = tensor<string, []>("decode_0_norm1_norm_weight_to_fp16"), val = tensor<fp16, [1090]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5982912)))];
+            tensor<fp16, [1090]> decode_0_norm1_norm_bias_to_fp16 = const()[name = tensor<string, []>("decode_0_norm1_norm_bias_to_fp16"), val = tensor<fp16, [1090]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5985216)))];
+            tensor<fp16, []> var_200_to_fp16 = const()[name = tensor<string, []>("op_200_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 1090, ?]> var_235_cast_fp16 = instance_norm(beta = decode_0_norm1_norm_bias_to_fp16, epsilon = var_200_to_fp16, gamma = decode_0_norm1_norm_weight_to_fp16, x = input_21_cast_fp16)[name = tensor<string, []>("op_235_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> var_236_cast_fp16 = mul(x = var_232_cast_fp16, y = var_235_cast_fp16)[name = tensor<string, []>("op_236_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_23_cast_fp16 = add(x = var_236_cast_fp16, y = var_229_cast_fp16_1)[name = tensor<string, []>("input_23_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_25_cast_fp16 = leaky_relu(alpha = var_197, x = input_23_cast_fp16)[name = tensor<string, []>("input_25_cast_fp16")];
+            tensor<string, []> input_29_pad_type_0 = const()[name = tensor<string, []>("input_29_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_29_pad_0 = const()[name = tensor<string, []>("input_29_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_29_strides_0 = const()[name = tensor<string, []>("input_29_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_29_dilations_0 = const()[name = tensor<string, []>("input_29_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_29_groups_0 = const()[name = tensor<string, []>("input_29_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1090, 3]> weight_19_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3348480]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5987520))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9336064))), name = tensor<string, []>("weight_19_to_fp16_palettized"), shape = tensor<uint32, [3]>([1024, 1090, 3])];
+            tensor<fp16, [1024]> decode_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decode_0_conv1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9336640)))];
+            tensor<fp16, [1, 1024, ?]> input_29_cast_fp16 = conv(bias = decode_0_conv1_bias_to_fp16, dilations = input_29_dilations_0, groups = input_29_groups_0, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = input_29_strides_0, weight = weight_19_to_fp16_palettized, x = input_25_cast_fp16)[name = tensor<string, []>("input_29_cast_fp16")];
+            tensor<fp16, [2048, 128]> decode_0_norm2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9338752))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9600960))), name = tensor<string, []>("decode_0_norm2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([2048, 128])];
+            tensor<fp16, [2048]> decode_0_norm2_fc_bias_to_fp16 = const()[name = tensor<string, []>("decode_0_norm2_fc_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9601536)))];
+            tensor<fp16, [1, 2048]> linear_3_cast_fp16 = linear(bias = decode_0_norm2_fc_bias_to_fp16, weight = decode_0_norm2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_3_cast_fp16")];
+            tensor<int32, [3]> var_254 = const()[name = tensor<string, []>("op_254"), val = tensor<int32, [3]>([1, 2048, 1])];
+            tensor<fp16, [1, 2048, 1]> h_15_cast_fp16 = reshape(shape = var_254, x = linear_3_cast_fp16)[name = tensor<string, []>("h_15_cast_fp16")];
+            tensor<int32, [2]> var_256_split_sizes_0 = const()[name = tensor<string, []>("op_256_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
+            tensor<int32, []> var_256_axis_0 = const()[name = tensor<string, []>("op_256_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 1]> var_256_cast_fp16_0, tensor<fp16, [1, 1024, 1]> var_256_cast_fp16_1 = split(axis = var_256_axis_0, split_sizes = var_256_split_sizes_0, x = h_15_cast_fp16)[name = tensor<string, []>("op_256_cast_fp16")];
+            tensor<fp16, []> var_258_promoted_to_fp16 = const()[name = tensor<string, []>("op_258_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 1024, 1]> var_259_cast_fp16 = add(x = var_256_cast_fp16_0, y = var_258_promoted_to_fp16)[name = tensor<string, []>("op_259_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_262_cast_fp16 = instance_norm(beta = encode_norm2_norm_bias_to_fp16, epsilon = var_200_to_fp16, gamma = encode_norm2_norm_weight_to_fp16, x = input_29_cast_fp16)[name = tensor<string, []>("op_262_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_263_cast_fp16 = mul(x = var_259_cast_fp16, y = var_262_cast_fp16)[name = tensor<string, []>("op_263_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_31_cast_fp16 = add(x = var_263_cast_fp16, y = var_256_cast_fp16_1)[name = tensor<string, []>("input_31_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_33_cast_fp16 = leaky_relu(alpha = var_197, x = input_31_cast_fp16)[name = tensor<string, []>("input_33_cast_fp16")];
+            tensor<string, []> out_3_pad_type_0 = const()[name = tensor<string, []>("out_3_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> out_3_pad_0 = const()[name = tensor<string, []>("out_3_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_3_strides_0 = const()[name = tensor<string, []>("out_3_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_3_dilations_0 = const()[name = tensor<string, []>("out_3_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> out_3_groups_0 = const()[name = tensor<string, []>("out_3_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 3]> weight_23_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9605696))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12751488))), name = tensor<string, []>("weight_23_to_fp16_palettized"), shape = tensor<uint32, [3]>([1024, 1024, 3])];
+            tensor<fp16, [1024]> decode_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decode_0_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12752064)))];
+            tensor<fp16, [1, 1024, ?]> out_3_cast_fp16 = conv(bias = 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 = weight_23_to_fp16_palettized, x = input_33_cast_fp16)[name = tensor<string, []>("out_3_cast_fp16")];
+            tensor<string, []> var_279_pad_type_0 = const()[name = tensor<string, []>("op_279_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> var_279_strides_0 = const()[name = tensor<string, []>("op_279_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_279_pad_0 = const()[name = tensor<string, []>("op_279_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_279_dilations_0 = const()[name = tensor<string, []>("op_279_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> var_279_groups_0 = const()[name = tensor<string, []>("op_279_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1090, 1]> weight_25_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1116160]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12754176))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13870400))), name = tensor<string, []>("weight_25_to_fp16_palettized"), shape = tensor<uint32, [3]>([1024, 1090, 1])];
+            tensor<fp16, [1, 1024, ?]> var_279_cast_fp16 = conv(dilations = var_279_dilations_0, groups = var_279_groups_0, pad = var_279_pad_0, pad_type = var_279_pad_type_0, strides = var_279_strides_0, weight = weight_25_to_fp16_palettized, x = input_21_cast_fp16)[name = tensor<string, []>("op_279_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_280_cast_fp16 = add(x = out_3_cast_fp16, y = var_279_cast_fp16)[name = tensor<string, []>("op_280_cast_fp16")];
+            tensor<fp16, []> var_281_to_fp16 = const()[name = tensor<string, []>("op_281_to_fp16"), val = tensor<fp16, []>(0x1.6ap-1)];
+            tensor<fp16, [1, 1024, ?]> x_3_cast_fp16 = mul(x = var_280_cast_fp16, y = var_281_to_fp16)[name = tensor<string, []>("x_3_cast_fp16")];
+            tensor<int32, []> var_284 = const()[name = tensor<string, []>("op_284"), val = tensor<int32, []>(1)];
+            tensor<bool, []> input_37_interleave_0 = const()[name = tensor<string, []>("input_37_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 1090, ?]> input_37_cast_fp16 = concat(axis = var_284, interleave = input_37_interleave_0, values = (x_3_cast_fp16, asr_res_1_cast_fp16, F0_cast_fp16, N_feat_cast_fp16))[name = tensor<string, []>("input_37_cast_fp16")];
+            tensor<fp32, []> var_288 = const()[name = tensor<string, []>("op_288"), val = tensor<fp32, []>(0x1.99999ap-3)];
+            tensor<fp16, [2180, 128]> decode_1_norm1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [279040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13870976))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14150080))), name = tensor<string, []>("decode_1_norm1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([2180, 128])];
+            tensor<fp16, [2180]> decode_1_norm1_fc_bias_to_fp16 = const()[name = tensor<string, []>("decode_1_norm1_fc_bias_to_fp16"), val = tensor<fp16, [2180]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14150656)))];
+            tensor<fp16, [1, 2180]> linear_4_cast_fp16 = linear(bias = decode_1_norm1_fc_bias_to_fp16, weight = decode_1_norm1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_4_cast_fp16")];
+            tensor<int32, [3]> var_318 = const()[name = tensor<string, []>("op_318"), val = tensor<int32, [3]>([1, 2180, 1])];
+            tensor<fp16, [1, 2180, 1]> h_19_cast_fp16 = reshape(shape = var_318, x = linear_4_cast_fp16)[name = tensor<string, []>("h_19_cast_fp16")];
+            tensor<int32, [2]> var_320_split_sizes_0 = const()[name = tensor<string, []>("op_320_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
+            tensor<int32, []> var_320_axis_0 = const()[name = tensor<string, []>("op_320_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1090, 1]> var_320_cast_fp16_0, tensor<fp16, [1, 1090, 1]> var_320_cast_fp16_1 = split(axis = var_320_axis_0, split_sizes = var_320_split_sizes_0, x = h_19_cast_fp16)[name = tensor<string, []>("op_320_cast_fp16")];
+            tensor<fp16, []> var_322_promoted_to_fp16 = const()[name = tensor<string, []>("op_322_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 1090, 1]> var_323_cast_fp16 = add(x = var_320_cast_fp16_0, y = var_322_promoted_to_fp16)[name = tensor<string, []>("op_323_cast_fp16")];
+            tensor<fp16, []> var_291_to_fp16 = const()[name = tensor<string, []>("op_291_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 1090, ?]> var_326_cast_fp16 = instance_norm(beta = decode_0_norm1_norm_bias_to_fp16, epsilon = var_291_to_fp16, gamma = decode_0_norm1_norm_weight_to_fp16, x = input_37_cast_fp16)[name = tensor<string, []>("op_326_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> var_327_cast_fp16 = mul(x = var_323_cast_fp16, y = var_326_cast_fp16)[name = tensor<string, []>("op_327_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_39_cast_fp16 = add(x = var_327_cast_fp16, y = var_320_cast_fp16_1)[name = tensor<string, []>("input_39_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_41_cast_fp16 = leaky_relu(alpha = var_288, x = input_39_cast_fp16)[name = tensor<string, []>("input_41_cast_fp16")];
+            tensor<string, []> input_45_pad_type_0 = const()[name = tensor<string, []>("input_45_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_45_pad_0 = const()[name = tensor<string, []>("input_45_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_45_strides_0 = const()[name = tensor<string, []>("input_45_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_45_dilations_0 = const()[name = tensor<string, []>("input_45_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_45_groups_0 = const()[name = tensor<string, []>("input_45_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1090, 3]> weight_29_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3348480]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14155136))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17503680))), name = tensor<string, []>("weight_29_to_fp16_palettized"), shape = tensor<uint32, [3]>([1024, 1090, 3])];
+            tensor<fp16, [1024]> decode_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decode_1_conv1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17504256)))];
+            tensor<fp16, [1, 1024, ?]> input_45_cast_fp16 = conv(bias = decode_1_conv1_bias_to_fp16, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = weight_29_to_fp16_palettized, x = input_41_cast_fp16)[name = tensor<string, []>("input_45_cast_fp16")];
+            tensor<fp16, [2048, 128]> decode_1_norm2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17506368))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17768576))), name = tensor<string, []>("decode_1_norm2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([2048, 128])];
+            tensor<fp16, [2048]> decode_1_norm2_fc_bias_to_fp16 = const()[name = tensor<string, []>("decode_1_norm2_fc_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17769152)))];
+            tensor<fp16, [1, 2048]> linear_5_cast_fp16 = linear(bias = decode_1_norm2_fc_bias_to_fp16, weight = decode_1_norm2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_5_cast_fp16")];
+            tensor<int32, [3]> var_345 = const()[name = tensor<string, []>("op_345"), val = tensor<int32, [3]>([1, 2048, 1])];
+            tensor<fp16, [1, 2048, 1]> h_23_cast_fp16 = reshape(shape = var_345, x = linear_5_cast_fp16)[name = tensor<string, []>("h_23_cast_fp16")];
+            tensor<int32, [2]> var_347_split_sizes_0 = const()[name = tensor<string, []>("op_347_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
+            tensor<int32, []> var_347_axis_0 = const()[name = tensor<string, []>("op_347_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 1]> var_347_cast_fp16_0, tensor<fp16, [1, 1024, 1]> var_347_cast_fp16_1 = split(axis = var_347_axis_0, split_sizes = var_347_split_sizes_0, x = h_23_cast_fp16)[name = tensor<string, []>("op_347_cast_fp16")];
+            tensor<fp16, []> var_349_promoted_to_fp16 = const()[name = tensor<string, []>("op_349_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 1024, 1]> var_350_cast_fp16 = add(x = var_347_cast_fp16_0, y = var_349_promoted_to_fp16)[name = tensor<string, []>("op_350_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_353_cast_fp16 = instance_norm(beta = encode_norm2_norm_bias_to_fp16, epsilon = var_291_to_fp16, gamma = encode_norm2_norm_weight_to_fp16, x = input_45_cast_fp16)[name = tensor<string, []>("op_353_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_354_cast_fp16 = mul(x = var_350_cast_fp16, y = var_353_cast_fp16)[name = tensor<string, []>("op_354_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_47_cast_fp16 = add(x = var_354_cast_fp16, y = var_347_cast_fp16_1)[name = tensor<string, []>("input_47_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_49_cast_fp16 = leaky_relu(alpha = var_288, x = input_47_cast_fp16)[name = tensor<string, []>("input_49_cast_fp16")];
+            tensor<string, []> out_5_pad_type_0 = const()[name = tensor<string, []>("out_5_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> out_5_pad_0 = const()[name = tensor<string, []>("out_5_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_5_strides_0 = const()[name = tensor<string, []>("out_5_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_5_dilations_0 = const()[name = tensor<string, []>("out_5_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> out_5_groups_0 = const()[name = tensor<string, []>("out_5_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 3]> weight_33_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17773312))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20919104))), name = tensor<string, []>("weight_33_to_fp16_palettized"), shape = tensor<uint32, [3]>([1024, 1024, 3])];
+            tensor<fp16, [1024]> decode_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decode_1_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20919680)))];
+            tensor<fp16, [1, 1024, ?]> out_5_cast_fp16 = conv(bias = 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 = weight_33_to_fp16_palettized, x = input_49_cast_fp16)[name = tensor<string, []>("out_5_cast_fp16")];
+            tensor<string, []> var_370_pad_type_0 = const()[name = tensor<string, []>("op_370_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> var_370_strides_0 = const()[name = tensor<string, []>("op_370_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_370_pad_0 = const()[name = tensor<string, []>("op_370_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_370_dilations_0 = const()[name = tensor<string, []>("op_370_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> var_370_groups_0 = const()[name = tensor<string, []>("op_370_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1090, 1]> weight_35_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1116160]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20921792))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22038016))), name = tensor<string, []>("weight_35_to_fp16_palettized"), shape = tensor<uint32, [3]>([1024, 1090, 1])];
+            tensor<fp16, [1, 1024, ?]> var_370_cast_fp16 = conv(dilations = var_370_dilations_0, groups = var_370_groups_0, pad = var_370_pad_0, pad_type = var_370_pad_type_0, strides = var_370_strides_0, weight = weight_35_to_fp16_palettized, x = input_37_cast_fp16)[name = tensor<string, []>("op_370_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_371_cast_fp16 = add(x = out_5_cast_fp16, y = var_370_cast_fp16)[name = tensor<string, []>("op_371_cast_fp16")];
+            tensor<fp16, []> var_372_to_fp16 = const()[name = tensor<string, []>("op_372_to_fp16"), val = tensor<fp16, []>(0x1.6ap-1)];
+            tensor<fp16, [1, 1024, ?]> x_5_cast_fp16 = mul(x = var_371_cast_fp16, y = var_372_to_fp16)[name = tensor<string, []>("x_5_cast_fp16")];
+            tensor<int32, []> var_375 = const()[name = tensor<string, []>("op_375"), val = tensor<int32, []>(1)];
+            tensor<bool, []> input_53_interleave_0 = const()[name = tensor<string, []>("input_53_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 1090, ?]> input_53_cast_fp16 = concat(axis = var_375, interleave = input_53_interleave_0, values = (x_5_cast_fp16, asr_res_1_cast_fp16, F0_cast_fp16, N_feat_cast_fp16))[name = tensor<string, []>("input_53_cast_fp16")];
+            tensor<fp32, []> var_379 = const()[name = tensor<string, []>("op_379"), val = tensor<fp32, []>(0x1.99999ap-3)];
+            tensor<fp16, [2180, 128]> decode_2_norm1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [279040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22038592))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22317696))), name = tensor<string, []>("decode_2_norm1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([2180, 128])];
+            tensor<fp16, [2180]> decode_2_norm1_fc_bias_to_fp16 = const()[name = tensor<string, []>("decode_2_norm1_fc_bias_to_fp16"), val = tensor<fp16, [2180]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22318272)))];
+            tensor<fp16, [1, 2180]> linear_6_cast_fp16 = linear(bias = decode_2_norm1_fc_bias_to_fp16, weight = decode_2_norm1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_6_cast_fp16")];
+            tensor<int32, [3]> var_409 = const()[name = tensor<string, []>("op_409"), val = tensor<int32, [3]>([1, 2180, 1])];
+            tensor<fp16, [1, 2180, 1]> h_27_cast_fp16 = reshape(shape = var_409, x = linear_6_cast_fp16)[name = tensor<string, []>("h_27_cast_fp16")];
+            tensor<int32, [2]> var_411_split_sizes_0 = const()[name = tensor<string, []>("op_411_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
+            tensor<int32, []> var_411_axis_0 = const()[name = tensor<string, []>("op_411_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1090, 1]> var_411_cast_fp16_0, tensor<fp16, [1, 1090, 1]> var_411_cast_fp16_1 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = h_27_cast_fp16)[name = tensor<string, []>("op_411_cast_fp16")];
+            tensor<fp16, []> var_413_promoted_to_fp16 = const()[name = tensor<string, []>("op_413_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 1090, 1]> var_414_cast_fp16 = add(x = var_411_cast_fp16_0, y = var_413_promoted_to_fp16)[name = tensor<string, []>("op_414_cast_fp16")];
+            tensor<fp16, []> var_382_to_fp16 = const()[name = tensor<string, []>("op_382_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 1090, ?]> var_417_cast_fp16 = instance_norm(beta = decode_0_norm1_norm_bias_to_fp16, epsilon = var_382_to_fp16, gamma = decode_0_norm1_norm_weight_to_fp16, x = input_53_cast_fp16)[name = tensor<string, []>("op_417_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> var_418_cast_fp16 = mul(x = var_414_cast_fp16, y = var_417_cast_fp16)[name = tensor<string, []>("op_418_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_55_cast_fp16 = add(x = var_418_cast_fp16, y = var_411_cast_fp16_1)[name = tensor<string, []>("input_55_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_57_cast_fp16 = leaky_relu(alpha = var_379, x = input_55_cast_fp16)[name = tensor<string, []>("input_57_cast_fp16")];
+            tensor<string, []> input_61_pad_type_0 = const()[name = tensor<string, []>("input_61_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_61_pad_0 = const()[name = tensor<string, []>("input_61_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_61_strides_0 = const()[name = tensor<string, []>("input_61_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_61_dilations_0 = const()[name = tensor<string, []>("input_61_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_61_groups_0 = const()[name = tensor<string, []>("input_61_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1090, 3]> weight_39_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3348480]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22322752))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25671296))), name = tensor<string, []>("weight_39_to_fp16_palettized"), shape = tensor<uint32, [3]>([1024, 1090, 3])];
+            tensor<fp16, [1024]> decode_2_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decode_2_conv1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25671872)))];
+            tensor<fp16, [1, 1024, ?]> input_61_cast_fp16 = conv(bias = decode_2_conv1_bias_to_fp16, dilations = input_61_dilations_0, groups = input_61_groups_0, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = input_61_strides_0, weight = weight_39_to_fp16_palettized, x = input_57_cast_fp16)[name = tensor<string, []>("input_61_cast_fp16")];
+            tensor<fp16, [2048, 128]> decode_2_norm2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [262144]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25673984))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25936192))), name = tensor<string, []>("decode_2_norm2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([2048, 128])];
+            tensor<fp16, [2048]> decode_2_norm2_fc_bias_to_fp16 = const()[name = tensor<string, []>("decode_2_norm2_fc_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25936768)))];
+            tensor<fp16, [1, 2048]> linear_7_cast_fp16 = linear(bias = decode_2_norm2_fc_bias_to_fp16, weight = decode_2_norm2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_7_cast_fp16")];
+            tensor<int32, [3]> var_436 = const()[name = tensor<string, []>("op_436"), val = tensor<int32, [3]>([1, 2048, 1])];
+            tensor<fp16, [1, 2048, 1]> h_31_cast_fp16 = reshape(shape = var_436, x = linear_7_cast_fp16)[name = tensor<string, []>("h_31_cast_fp16")];
+            tensor<int32, [2]> var_438_split_sizes_0 = const()[name = tensor<string, []>("op_438_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
+            tensor<int32, []> var_438_axis_0 = const()[name = tensor<string, []>("op_438_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1024, 1]> var_438_cast_fp16_0, tensor<fp16, [1, 1024, 1]> var_438_cast_fp16_1 = split(axis = var_438_axis_0, split_sizes = var_438_split_sizes_0, x = h_31_cast_fp16)[name = tensor<string, []>("op_438_cast_fp16")];
+            tensor<fp16, []> var_440_promoted_to_fp16 = const()[name = tensor<string, []>("op_440_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 1024, 1]> var_441_cast_fp16 = add(x = var_438_cast_fp16_0, y = var_440_promoted_to_fp16)[name = tensor<string, []>("op_441_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_444_cast_fp16 = instance_norm(beta = encode_norm2_norm_bias_to_fp16, epsilon = var_382_to_fp16, gamma = encode_norm2_norm_weight_to_fp16, x = input_61_cast_fp16)[name = tensor<string, []>("op_444_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_445_cast_fp16 = mul(x = var_441_cast_fp16, y = var_444_cast_fp16)[name = tensor<string, []>("op_445_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_63_cast_fp16 = add(x = var_445_cast_fp16, y = var_438_cast_fp16_1)[name = tensor<string, []>("input_63_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> input_65_cast_fp16 = leaky_relu(alpha = var_379, x = input_63_cast_fp16)[name = tensor<string, []>("input_65_cast_fp16")];
+            tensor<string, []> out_7_pad_type_0 = const()[name = tensor<string, []>("out_7_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> out_7_pad_0 = const()[name = tensor<string, []>("out_7_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_7_strides_0 = const()[name = tensor<string, []>("out_7_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_7_dilations_0 = const()[name = tensor<string, []>("out_7_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> out_7_groups_0 = const()[name = tensor<string, []>("out_7_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1024, 3]> weight_43_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3145728]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25940928))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29086720))), name = tensor<string, []>("weight_43_to_fp16_palettized"), shape = tensor<uint32, [3]>([1024, 1024, 3])];
+            tensor<fp16, [1024]> decode_2_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decode_2_conv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29087296)))];
+            tensor<fp16, [1, 1024, ?]> out_7_cast_fp16 = conv(bias = 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 = weight_43_to_fp16_palettized, x = input_65_cast_fp16)[name = tensor<string, []>("out_7_cast_fp16")];
+            tensor<string, []> var_461_pad_type_0 = const()[name = tensor<string, []>("op_461_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> var_461_strides_0 = const()[name = tensor<string, []>("op_461_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_461_pad_0 = const()[name = tensor<string, []>("op_461_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_461_dilations_0 = const()[name = tensor<string, []>("op_461_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> var_461_groups_0 = const()[name = tensor<string, []>("op_461_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1024, 1090, 1]> weight_45_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1116160]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29089408))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30205632))), name = tensor<string, []>("weight_45_to_fp16_palettized"), shape = tensor<uint32, [3]>([1024, 1090, 1])];
+            tensor<fp16, [1, 1024, ?]> var_461_cast_fp16 = conv(dilations = var_461_dilations_0, groups = var_461_groups_0, pad = var_461_pad_0, pad_type = var_461_pad_type_0, strides = var_461_strides_0, weight = weight_45_to_fp16_palettized, x = input_53_cast_fp16)[name = tensor<string, []>("op_461_cast_fp16")];
+            tensor<fp16, [1, 1024, ?]> var_462_cast_fp16 = add(x = out_7_cast_fp16, y = var_461_cast_fp16)[name = tensor<string, []>("op_462_cast_fp16")];
+            tensor<fp16, []> var_463_to_fp16 = const()[name = tensor<string, []>("op_463_to_fp16"), val = tensor<fp16, []>(0x1.6ap-1)];
+            tensor<fp16, [1, 1024, ?]> x_7_cast_fp16 = mul(x = var_462_cast_fp16, y = var_463_to_fp16)[name = tensor<string, []>("x_7_cast_fp16")];
+            tensor<int32, []> var_466 = const()[name = tensor<string, []>("op_466"), val = tensor<int32, []>(1)];
+            tensor<bool, []> input_69_interleave_0 = const()[name = tensor<string, []>("input_69_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 1090, ?]> input_69_cast_fp16 = concat(axis = var_466, interleave = input_69_interleave_0, values = (x_7_cast_fp16, asr_res_1_cast_fp16, F0_cast_fp16, N_feat_cast_fp16))[name = tensor<string, []>("input_69_cast_fp16")];
+            tensor<fp32, []> var_472 = const()[name = tensor<string, []>("op_472"), val = tensor<fp32, []>(0x1.99999ap-3)];
+            tensor<fp16, [2180, 128]> decode_3_norm1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [279040]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30206208))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30485312))), name = tensor<string, []>("decode_3_norm1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([2180, 128])];
+            tensor<fp16, [2180]> decode_3_norm1_fc_bias_to_fp16 = const()[name = tensor<string, []>("decode_3_norm1_fc_bias_to_fp16"), val = tensor<fp16, [2180]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30485888)))];
+            tensor<fp16, [1, 2180]> linear_8_cast_fp16 = linear(bias = decode_3_norm1_fc_bias_to_fp16, weight = decode_3_norm1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_8_cast_fp16")];
+            tensor<int32, [3]> var_508 = const()[name = tensor<string, []>("op_508"), val = tensor<int32, [3]>([1, 2180, 1])];
+            tensor<fp16, [1, 2180, 1]> h_35_cast_fp16 = reshape(shape = var_508, x = linear_8_cast_fp16)[name = tensor<string, []>("h_35_cast_fp16")];
+            tensor<int32, [2]> var_510_split_sizes_0 = const()[name = tensor<string, []>("op_510_split_sizes_0"), val = tensor<int32, [2]>([1090, 1090])];
+            tensor<int32, []> var_510_axis_0 = const()[name = tensor<string, []>("op_510_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 1090, 1]> var_510_cast_fp16_0, tensor<fp16, [1, 1090, 1]> var_510_cast_fp16_1 = split(axis = var_510_axis_0, split_sizes = var_510_split_sizes_0, x = h_35_cast_fp16)[name = tensor<string, []>("op_510_cast_fp16")];
+            tensor<fp16, []> var_512_promoted_to_fp16 = const()[name = tensor<string, []>("op_512_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 1090, 1]> var_513_cast_fp16 = add(x = var_510_cast_fp16_0, y = var_512_promoted_to_fp16)[name = tensor<string, []>("op_513_cast_fp16")];
+            tensor<fp16, []> var_476_to_fp16 = const()[name = tensor<string, []>("op_476_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 1090, ?]> var_516_cast_fp16 = instance_norm(beta = decode_0_norm1_norm_bias_to_fp16, epsilon = var_476_to_fp16, gamma = decode_0_norm1_norm_weight_to_fp16, x = input_69_cast_fp16)[name = tensor<string, []>("op_516_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> var_517_cast_fp16 = mul(x = var_513_cast_fp16, y = var_516_cast_fp16)[name = tensor<string, []>("op_517_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_71_cast_fp16 = add(x = var_517_cast_fp16, y = var_510_cast_fp16_1)[name = tensor<string, []>("input_71_cast_fp16")];
+            tensor<fp16, [1, 1090, ?]> input_73_cast_fp16 = leaky_relu(alpha = var_472, x = input_71_cast_fp16)[name = tensor<string, []>("input_73_cast_fp16")];
+            tensor<string, []> conv_transpose_0_pad_type_0 = const()[name = tensor<string, []>("conv_transpose_0_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> conv_transpose_0_pad_0 = const()[name = tensor<string, []>("conv_transpose_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_transpose_0_strides_0 = const()[name = tensor<string, []>("conv_transpose_0_strides_0"), val = tensor<int32, [1]>([2])];
+            tensor<int32, []> conv_transpose_0_groups_0 = const()[name = tensor<string, []>("conv_transpose_0_groups_0"), val = tensor<int32, []>(1090)];
+            tensor<int32, [1]> conv_transpose_0_dilations_0 = const()[name = tensor<string, []>("conv_transpose_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1090, 1, 3]> op_520_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [3270]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30490368))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30493760))), name = tensor<string, []>("op_520_to_fp16_palettized"), shape = tensor<uint32, [3]>([1090, 1, 3])];
+            tensor<fp16, [1090]> decode_3_pool_bias_to_fp16 = const()[name = tensor<string, []>("decode_3_pool_bias_to_fp16"), val = tensor<fp16, [1090]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30494336)))];
+            tensor<fp16, [1, 1090, ?]> conv_transpose_0_cast_fp16 = conv_transpose(bias = 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 = op_520_to_fp16_palettized, x = input_73_cast_fp16)[name = tensor<string, []>("conv_transpose_0_cast_fp16")];
+            tensor<int32, [3]> input_75_begin_0 = const()[name = tensor<string, []>("input_75_begin_0"), val = tensor<int32, [3]>([0, 0, 1])];
+            tensor<int32, [3]> input_75_end_0 = const()[name = tensor<string, []>("input_75_end_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<bool, [3]> input_75_begin_mask_0 = const()[name = tensor<string, []>("input_75_begin_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<bool, [3]> input_75_end_mask_0 = const()[name = tensor<string, []>("input_75_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
+            tensor<fp16, [1, 1090, ?]> input_75_cast_fp16 = slice_by_index(begin = input_75_begin_0, begin_mask = input_75_begin_mask_0, end = input_75_end_0, end_mask = input_75_end_mask_0, x = conv_transpose_0_cast_fp16)[name = tensor<string, []>("input_75_cast_fp16")];
+            tensor<string, []> input_79_pad_type_0 = const()[name = tensor<string, []>("input_79_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_79_pad_0 = const()[name = tensor<string, []>("input_79_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_79_strides_0 = const()[name = tensor<string, []>("input_79_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_79_dilations_0 = const()[name = tensor<string, []>("input_79_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_79_groups_0 = const()[name = tensor<string, []>("input_79_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [512, 1090, 3]> weight_49_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [1674240]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30496640))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32170944))), name = tensor<string, []>("weight_49_to_fp16_palettized"), shape = tensor<uint32, [3]>([512, 1090, 3])];
+            tensor<fp16, [512]> decode_3_conv1_bias_to_fp16 = const()[name = tensor<string, []>("decode_3_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32171520)))];
+            tensor<fp16, [1, 512, ?]> input_79_cast_fp16 = conv(bias = decode_3_conv1_bias_to_fp16, 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_49_to_fp16_palettized, x = input_75_cast_fp16)[name = tensor<string, []>("input_79_cast_fp16")];
+            tensor<fp16, [1024, 128]> decode_3_norm2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [131072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32172608))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32303744))), name = tensor<string, []>("decode_3_norm2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([1024, 128])];
+            tensor<fp16, [1024]> decode_3_norm2_fc_bias_to_fp16 = const()[name = tensor<string, []>("decode_3_norm2_fc_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32304320)))];
+            tensor<fp16, [1, 1024]> linear_9_cast_fp16 = linear(bias = decode_3_norm2_fc_bias_to_fp16, weight = decode_3_norm2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_9_cast_fp16")];
+            tensor<int32, [3]> var_542 = const()[name = tensor<string, []>("op_542"), val = tensor<int32, [3]>([1, 1024, 1])];
+            tensor<fp16, [1, 1024, 1]> h_39_cast_fp16 = reshape(shape = var_542, x = linear_9_cast_fp16)[name = tensor<string, []>("h_39_cast_fp16")];
+            tensor<int32, [2]> var_544_split_sizes_0 = const()[name = tensor<string, []>("op_544_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
+            tensor<int32, []> var_544_axis_0 = const()[name = tensor<string, []>("op_544_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 512, 1]> var_544_cast_fp16_0, tensor<fp16, [1, 512, 1]> var_544_cast_fp16_1 = split(axis = var_544_axis_0, split_sizes = var_544_split_sizes_0, x = h_39_cast_fp16)[name = tensor<string, []>("op_544_cast_fp16")];
+            tensor<fp16, []> var_546_promoted_to_fp16 = const()[name = tensor<string, []>("op_546_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 512, 1]> var_547_cast_fp16 = add(x = var_544_cast_fp16_0, y = var_546_promoted_to_fp16)[name = tensor<string, []>("op_547_cast_fp16")];
+            tensor<fp16, [512]> decode_3_norm2_norm_weight_to_fp16 = const()[name = tensor<string, []>("decode_3_norm2_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32306432)))];
+            tensor<fp16, [512]> decode_3_norm2_norm_bias_to_fp16 = const()[name = tensor<string, []>("decode_3_norm2_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32307520)))];
+            tensor<fp16, [1, 512, ?]> var_550_cast_fp16 = instance_norm(beta = decode_3_norm2_norm_bias_to_fp16, epsilon = var_476_to_fp16, gamma = decode_3_norm2_norm_weight_to_fp16, x = input_79_cast_fp16)[name = tensor<string, []>("op_550_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> var_551_cast_fp16 = mul(x = var_547_cast_fp16, y = var_550_cast_fp16)[name = tensor<string, []>("op_551_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_81_cast_fp16 = add(x = var_551_cast_fp16, y = var_544_cast_fp16_1)[name = tensor<string, []>("input_81_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_83_cast_fp16 = leaky_relu(alpha = var_472, x = input_81_cast_fp16)[name = tensor<string, []>("input_83_cast_fp16")];
+            tensor<string, []> out_pad_type_0 = const()[name = tensor<string, []>("out_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> out_pad_0 = const()[name = tensor<string, []>("out_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> out_strides_0 = const()[name = tensor<string, []>("out_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> out_dilations_0 = const()[name = tensor<string, []>("out_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> out_groups_0 = const()[name = tensor<string, []>("out_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [512, 512, 3]> weight_53_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [786432]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32308608))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33095104))), name = tensor<string, []>("weight_53_to_fp16_palettized"), shape = tensor<uint32, [3]>([512, 512, 3])];
+            tensor<fp16, [512]> decode_3_conv2_bias_to_fp16 = const()[name = tensor<string, []>("decode_3_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33095680)))];
+            tensor<fp16, [1, 512, ?]> out_cast_fp16 = conv(bias = 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 = weight_53_to_fp16_palettized, x = input_83_cast_fp16)[name = tensor<string, []>("out_cast_fp16")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = tensor<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_69_cast_fp16)[name = tensor<string, []>("expand_dims_0_cast_fp16")];
+            tensor<int32, []> upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_0_scale_factor_height_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_0_scale_factor_width_0"), val = tensor<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 = tensor<string, []>("upsample_nearest_neighbor_0_cast_fp16")];
+            tensor<int32, [1]> input_87_axes_0 = const()[name = tensor<string, []>("input_87_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp16, [1, 1090, ?]> input_87_cast_fp16 = squeeze(axes = input_87_axes_0, x = upsample_nearest_neighbor_0_cast_fp16)[name = tensor<string, []>("input_87_cast_fp16")];
+            tensor<string, []> var_569_pad_type_0 = const()[name = tensor<string, []>("op_569_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> var_569_strides_0 = const()[name = tensor<string, []>("op_569_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_569_pad_0 = const()[name = tensor<string, []>("op_569_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_569_dilations_0 = const()[name = tensor<string, []>("op_569_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> var_569_groups_0 = const()[name = tensor<string, []>("op_569_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [512, 1090, 1]> weight_55_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [558080]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33096768))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33654912))), name = tensor<string, []>("weight_55_to_fp16_palettized"), shape = tensor<uint32, [3]>([512, 1090, 1])];
+            tensor<fp16, [1, 512, ?]> var_569_cast_fp16 = conv(dilations = var_569_dilations_0, groups = var_569_groups_0, pad = var_569_pad_0, pad_type = var_569_pad_type_0, strides = var_569_strides_0, weight = weight_55_to_fp16_palettized, x = input_87_cast_fp16)[name = tensor<string, []>("op_569_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> var_570_cast_fp16 = add(x = out_cast_fp16, y = var_569_cast_fp16)[name = tensor<string, []>("op_570_cast_fp16")];
+            tensor<fp16, []> var_571_to_fp16 = const()[name = tensor<string, []>("op_571_to_fp16"), val = tensor<fp16, []>(0x1.6ap-1)];
+            tensor<fp16, [1, 512, ?]> input_89_cast_fp16 = mul(x = var_570_cast_fp16, y = var_571_to_fp16)[name = tensor<string, []>("input_89_cast_fp16")];
+            tensor<fp32, []> var_573 = const()[name = tensor<string, []>("op_573"), val = tensor<fp32, []>(0x1.99999ap-4)];
+            tensor<fp16, [1, 512, ?]> input_91_cast_fp16 = leaky_relu(alpha = var_573, x = input_89_cast_fp16)[name = tensor<string, []>("input_91_cast_fp16")];
+            tensor<string, []> x_9_pad_type_0 = const()[name = tensor<string, []>("x_9_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> x_9_pad_0 = const()[name = tensor<string, []>("x_9_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> x_9_strides_0 = const()[name = tensor<string, []>("x_9_strides_0"), val = tensor<int32, [1]>([10])];
+            tensor<int32, [1]> x_9_dilations_0 = const()[name = tensor<string, []>("x_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_9_groups_0 = const()[name = tensor<string, []>("x_9_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [512, 256, 20]> op_576_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [2621440]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33655488))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36276992))), name = tensor<string, []>("op_576_to_fp16_palettized"), shape = tensor<uint32, [3]>([512, 256, 20])];
+            tensor<fp16, [256]> ups_0_bias_to_fp16 = const()[name = tensor<string, []>("ups_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36277568)))];
+            tensor<fp16, [1, 256, ?]> x_9_cast_fp16 = conv_transpose(bias = ups_0_bias_to_fp16, dilations = x_9_dilations_0, groups = x_9_groups_0, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = x_9_strides_0, weight = op_576_to_fp16_palettized, x = input_91_cast_fp16)[name = tensor<string, []>("x_9_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_93_cast_fp16 = add(x = x_9_cast_fp16, y = x_source_0)[name = tensor<string, []>("input_93_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_0_adain1_0_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36278144))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36343744))), name = tensor<string, []>("resblocks_0_adain1_0_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_0_adain1_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_0_adain1_0_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36344320)))];
+            tensor<fp16, [1, 512]> linear_10_cast_fp16 = linear(bias = resblocks_0_adain1_0_fc_bias_to_fp16, weight = resblocks_0_adain1_0_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_10_cast_fp16")];
+            tensor<int32, [3]> var_679 = const()[name = tensor<string, []>("op_679"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_43_cast_fp16 = reshape(shape = var_679, x = linear_10_cast_fp16)[name = tensor<string, []>("h_43_cast_fp16")];
+            tensor<int32, [2]> var_681_split_sizes_0 = const()[name = tensor<string, []>("op_681_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_681_axis_0 = const()[name = tensor<string, []>("op_681_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_681_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_681_cast_fp16_1 = split(axis = var_681_axis_0, split_sizes = var_681_split_sizes_0, x = h_43_cast_fp16)[name = tensor<string, []>("op_681_cast_fp16")];
+            tensor<fp16, []> var_683_promoted_to_fp16 = const()[name = tensor<string, []>("op_683_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_684_cast_fp16 = add(x = var_681_cast_fp16_0, y = var_683_promoted_to_fp16)[name = tensor<string, []>("op_684_cast_fp16")];
+            tensor<fp16, [256]> resblocks_0_adain1_0_norm_weight_to_fp16 = const()[name = tensor<string, []>("resblocks_0_adain1_0_norm_weight_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36345408)))];
+            tensor<fp16, [256]> resblocks_0_adain1_0_norm_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_0_adain1_0_norm_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36345984)))];
+            tensor<fp16, []> var_596_to_fp16 = const()[name = tensor<string, []>("op_596_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 256, ?]> var_687_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_596_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_93_cast_fp16)[name = tensor<string, []>("op_687_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_688_cast_fp16 = mul(x = var_684_cast_fp16, y = var_687_cast_fp16)[name = tensor<string, []>("op_688_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_1_cast_fp16 = add(x = var_688_cast_fp16, y = var_681_cast_fp16_1)[name = tensor<string, []>("xt_1_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_691_to_fp16 = const()[name = tensor<string, []>("op_691_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36346560)))];
+            tensor<fp16, [1, 256, ?]> var_692_cast_fp16 = mul(x = xt_1_cast_fp16, y = var_691_to_fp16)[name = tensor<string, []>("op_692_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_1_cast_fp16 = cos(x = var_692_cast_fp16)[name = tensor<string, []>("cv_1_cast_fp16")];
+            tensor<fp16, []> var_694_to_fp16 = const()[name = tensor<string, []>("op_694_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_695_cast_fp16 = mul(x = cv_1_cast_fp16, y = var_694_to_fp16)[name = tensor<string, []>("op_695_cast_fp16")];
+            tensor<fp16, []> var_696_to_fp16 = const()[name = tensor<string, []>("op_696_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_697_cast_fp16 = add(x = var_695_cast_fp16, y = var_696_to_fp16)[name = tensor<string, []>("op_697_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_698_to_fp16 = const()[name = tensor<string, []>("op_698_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36347136)))];
+            tensor<fp16, [1, 256, ?]> var_701_cast_fp16 = mul(x = var_697_cast_fp16, y = var_698_to_fp16)[name = tensor<string, []>("op_701_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_95_cast_fp16 = add(x = xt_1_cast_fp16, y = var_701_cast_fp16)[name = tensor<string, []>("input_95_cast_fp16")];
+            tensor<string, []> input_97_pad_type_0 = const()[name = tensor<string, []>("input_97_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_97_pad_0 = const()[name = tensor<string, []>("input_97_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_97_strides_0 = const()[name = tensor<string, []>("input_97_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_97_dilations_0 = const()[name = tensor<string, []>("input_97_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_97_groups_0 = const()[name = tensor<string, []>("input_97_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3]> weight_59_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [196608]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36347712))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36544384))), name = tensor<string, []>("weight_59_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 3])];
+            tensor<fp16, [256]> resblocks_0_convs1_0_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_0_convs1_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36544960)))];
+            tensor<fp16, [1, 256, ?]> input_97_cast_fp16 = conv(bias = resblocks_0_convs1_0_bias_to_fp16, dilations = input_97_dilations_0, groups = input_97_groups_0, pad = input_97_pad_0, pad_type = input_97_pad_type_0, strides = input_97_strides_0, weight = weight_59_to_fp16_palettized, x = input_95_cast_fp16)[name = tensor<string, []>("input_97_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_0_adain2_0_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36545536))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36611136))), name = tensor<string, []>("resblocks_0_adain2_0_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_0_adain2_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_0_adain2_0_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36611712)))];
+            tensor<fp16, [1, 512]> linear_11_cast_fp16 = linear(bias = resblocks_0_adain2_0_fc_bias_to_fp16, weight = resblocks_0_adain2_0_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_11_cast_fp16")];
+            tensor<int32, [3]> var_717 = const()[name = tensor<string, []>("op_717"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_47_cast_fp16 = reshape(shape = var_717, x = linear_11_cast_fp16)[name = tensor<string, []>("h_47_cast_fp16")];
+            tensor<int32, [2]> var_719_split_sizes_0 = const()[name = tensor<string, []>("op_719_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_719_axis_0 = const()[name = tensor<string, []>("op_719_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_719_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_719_cast_fp16_1 = split(axis = var_719_axis_0, split_sizes = var_719_split_sizes_0, x = h_47_cast_fp16)[name = tensor<string, []>("op_719_cast_fp16")];
+            tensor<fp16, []> var_721_promoted_to_fp16 = const()[name = tensor<string, []>("op_721_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_722_cast_fp16 = add(x = var_719_cast_fp16_0, y = var_721_promoted_to_fp16)[name = tensor<string, []>("op_722_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_725_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_596_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_97_cast_fp16)[name = tensor<string, []>("op_725_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_726_cast_fp16 = mul(x = var_722_cast_fp16, y = var_725_cast_fp16)[name = tensor<string, []>("op_726_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_3_cast_fp16 = add(x = var_726_cast_fp16, y = var_719_cast_fp16_1)[name = tensor<string, []>("xt_3_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_729_to_fp16 = const()[name = tensor<string, []>("op_729_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36612800)))];
+            tensor<fp16, [1, 256, ?]> var_730_cast_fp16 = mul(x = xt_3_cast_fp16, y = var_729_to_fp16)[name = tensor<string, []>("op_730_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_3_cast_fp16 = cos(x = var_730_cast_fp16)[name = tensor<string, []>("cv_3_cast_fp16")];
+            tensor<fp16, []> var_732_to_fp16 = const()[name = tensor<string, []>("op_732_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_733_cast_fp16 = mul(x = cv_3_cast_fp16, y = var_732_to_fp16)[name = tensor<string, []>("op_733_cast_fp16")];
+            tensor<fp16, []> var_734_to_fp16 = const()[name = tensor<string, []>("op_734_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_735_cast_fp16 = add(x = var_733_cast_fp16, y = var_734_to_fp16)[name = tensor<string, []>("op_735_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_736_to_fp16 = const()[name = tensor<string, []>("op_736_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36613376)))];
+            tensor<fp16, [1, 256, ?]> var_739_cast_fp16 = mul(x = var_735_cast_fp16, y = var_736_to_fp16)[name = tensor<string, []>("op_739_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_99_cast_fp16 = add(x = xt_3_cast_fp16, y = var_739_cast_fp16)[name = tensor<string, []>("input_99_cast_fp16")];
+            tensor<string, []> xt_5_pad_type_0 = const()[name = tensor<string, []>("xt_5_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_5_pad_0 = const()[name = tensor<string, []>("xt_5_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> xt_5_strides_0 = const()[name = tensor<string, []>("xt_5_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_5_dilations_0 = const()[name = tensor<string, []>("xt_5_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_5_groups_0 = const()[name = tensor<string, []>("xt_5_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3]> weight_63_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [196608]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36613952))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36810624))), name = tensor<string, []>("weight_63_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 3])];
+            tensor<fp16, [256]> resblocks_0_convs2_0_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_0_convs2_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36811200)))];
+            tensor<fp16, [1, 256, ?]> xt_5_cast_fp16 = conv(bias = resblocks_0_convs2_0_bias_to_fp16, dilations = xt_5_dilations_0, groups = xt_5_groups_0, pad = xt_5_pad_0, pad_type = xt_5_pad_type_0, strides = xt_5_strides_0, weight = weight_63_to_fp16_palettized, x = input_99_cast_fp16)[name = tensor<string, []>("xt_5_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_101_cast_fp16 = add(x = xt_5_cast_fp16, y = input_93_cast_fp16)[name = tensor<string, []>("input_101_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_0_adain1_1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36811776))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36877376))), name = tensor<string, []>("resblocks_0_adain1_1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_0_adain1_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_0_adain1_1_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36877952)))];
+            tensor<fp16, [1, 512]> linear_12_cast_fp16 = linear(bias = resblocks_0_adain1_1_fc_bias_to_fp16, weight = resblocks_0_adain1_1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_12_cast_fp16")];
+            tensor<int32, [3]> var_756 = const()[name = tensor<string, []>("op_756"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_51_cast_fp16 = reshape(shape = var_756, x = linear_12_cast_fp16)[name = tensor<string, []>("h_51_cast_fp16")];
+            tensor<int32, [2]> var_758_split_sizes_0 = const()[name = tensor<string, []>("op_758_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_758_axis_0 = const()[name = tensor<string, []>("op_758_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_758_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_758_cast_fp16_1 = split(axis = var_758_axis_0, split_sizes = var_758_split_sizes_0, x = h_51_cast_fp16)[name = tensor<string, []>("op_758_cast_fp16")];
+            tensor<fp16, []> var_760_promoted_to_fp16 = const()[name = tensor<string, []>("op_760_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_761_cast_fp16 = add(x = var_758_cast_fp16_0, y = var_760_promoted_to_fp16)[name = tensor<string, []>("op_761_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_764_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_596_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_101_cast_fp16)[name = tensor<string, []>("op_764_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_765_cast_fp16 = mul(x = var_761_cast_fp16, y = var_764_cast_fp16)[name = tensor<string, []>("op_765_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_7_cast_fp16 = add(x = var_765_cast_fp16, y = var_758_cast_fp16_1)[name = tensor<string, []>("xt_7_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_768_to_fp16 = const()[name = tensor<string, []>("op_768_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36879040)))];
+            tensor<fp16, [1, 256, ?]> var_769_cast_fp16 = mul(x = xt_7_cast_fp16, y = var_768_to_fp16)[name = tensor<string, []>("op_769_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_5_cast_fp16 = cos(x = var_769_cast_fp16)[name = tensor<string, []>("cv_5_cast_fp16")];
+            tensor<fp16, []> var_771_to_fp16 = const()[name = tensor<string, []>("op_771_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_772_cast_fp16 = mul(x = cv_5_cast_fp16, y = var_771_to_fp16)[name = tensor<string, []>("op_772_cast_fp16")];
+            tensor<fp16, []> var_773_to_fp16 = const()[name = tensor<string, []>("op_773_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_774_cast_fp16 = add(x = var_772_cast_fp16, y = var_773_to_fp16)[name = tensor<string, []>("op_774_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_775_to_fp16 = const()[name = tensor<string, []>("op_775_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36879616)))];
+            tensor<fp16, [1, 256, ?]> var_778_cast_fp16 = mul(x = var_774_cast_fp16, y = var_775_to_fp16)[name = tensor<string, []>("op_778_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_103_cast_fp16 = add(x = xt_7_cast_fp16, y = var_778_cast_fp16)[name = tensor<string, []>("input_103_cast_fp16")];
+            tensor<string, []> input_105_pad_type_0 = const()[name = tensor<string, []>("input_105_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_105_pad_0 = const()[name = tensor<string, []>("input_105_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> input_105_dilations_0 = const()[name = tensor<string, []>("input_105_dilations_0"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> input_105_strides_0 = const()[name = tensor<string, []>("input_105_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_105_groups_0 = const()[name = tensor<string, []>("input_105_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3]> weight_67_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [196608]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36880192))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37076864))), name = tensor<string, []>("weight_67_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 3])];
+            tensor<fp16, [256]> resblocks_0_convs1_1_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_0_convs1_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37077440)))];
+            tensor<fp16, [1, 256, ?]> input_105_cast_fp16 = conv(bias = resblocks_0_convs1_1_bias_to_fp16, dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = weight_67_to_fp16_palettized, x = input_103_cast_fp16)[name = tensor<string, []>("input_105_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_0_adain2_1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37078016))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37143616))), name = tensor<string, []>("resblocks_0_adain2_1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_0_adain2_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_0_adain2_1_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37144192)))];
+            tensor<fp16, [1, 512]> linear_13_cast_fp16 = linear(bias = resblocks_0_adain2_1_fc_bias_to_fp16, weight = resblocks_0_adain2_1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_13_cast_fp16")];
+            tensor<int32, [3]> var_794 = const()[name = tensor<string, []>("op_794"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_55_cast_fp16 = reshape(shape = var_794, x = linear_13_cast_fp16)[name = tensor<string, []>("h_55_cast_fp16")];
+            tensor<int32, [2]> var_796_split_sizes_0 = const()[name = tensor<string, []>("op_796_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_796_axis_0 = const()[name = tensor<string, []>("op_796_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_796_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_796_cast_fp16_1 = split(axis = var_796_axis_0, split_sizes = var_796_split_sizes_0, x = h_55_cast_fp16)[name = tensor<string, []>("op_796_cast_fp16")];
+            tensor<fp16, []> var_798_promoted_to_fp16 = const()[name = tensor<string, []>("op_798_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_799_cast_fp16 = add(x = var_796_cast_fp16_0, y = var_798_promoted_to_fp16)[name = tensor<string, []>("op_799_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_802_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_596_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_105_cast_fp16)[name = tensor<string, []>("op_802_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_803_cast_fp16 = mul(x = var_799_cast_fp16, y = var_802_cast_fp16)[name = tensor<string, []>("op_803_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_9_cast_fp16 = add(x = var_803_cast_fp16, y = var_796_cast_fp16_1)[name = tensor<string, []>("xt_9_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_806_to_fp16 = const()[name = tensor<string, []>("op_806_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37145280)))];
+            tensor<fp16, [1, 256, ?]> var_807_cast_fp16 = mul(x = xt_9_cast_fp16, y = var_806_to_fp16)[name = tensor<string, []>("op_807_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_7_cast_fp16 = cos(x = var_807_cast_fp16)[name = tensor<string, []>("cv_7_cast_fp16")];
+            tensor<fp16, []> var_809_to_fp16 = const()[name = tensor<string, []>("op_809_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_810_cast_fp16 = mul(x = cv_7_cast_fp16, y = var_809_to_fp16)[name = tensor<string, []>("op_810_cast_fp16")];
+            tensor<fp16, []> var_811_to_fp16 = const()[name = tensor<string, []>("op_811_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_812_cast_fp16 = add(x = var_810_cast_fp16, y = var_811_to_fp16)[name = tensor<string, []>("op_812_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_813_to_fp16 = const()[name = tensor<string, []>("op_813_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37145856)))];
+            tensor<fp16, [1, 256, ?]> var_816_cast_fp16 = mul(x = var_812_cast_fp16, y = var_813_to_fp16)[name = tensor<string, []>("op_816_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_107_cast_fp16 = add(x = xt_9_cast_fp16, y = var_816_cast_fp16)[name = tensor<string, []>("input_107_cast_fp16")];
+            tensor<string, []> xt_11_pad_type_0 = const()[name = tensor<string, []>("xt_11_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_11_pad_0 = const()[name = tensor<string, []>("xt_11_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> xt_11_strides_0 = const()[name = tensor<string, []>("xt_11_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_11_dilations_0 = const()[name = tensor<string, []>("xt_11_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_11_groups_0 = const()[name = tensor<string, []>("xt_11_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3]> weight_71_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [196608]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37146432))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37343104))), name = tensor<string, []>("weight_71_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 3])];
+            tensor<fp16, [256]> resblocks_0_convs2_1_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_0_convs2_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37343680)))];
+            tensor<fp16, [1, 256, ?]> xt_11_cast_fp16 = conv(bias = resblocks_0_convs2_1_bias_to_fp16, dilations = xt_11_dilations_0, groups = xt_11_groups_0, pad = xt_11_pad_0, pad_type = xt_11_pad_type_0, strides = xt_11_strides_0, weight = weight_71_to_fp16_palettized, x = input_107_cast_fp16)[name = tensor<string, []>("xt_11_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_109_cast_fp16 = add(x = xt_11_cast_fp16, y = input_101_cast_fp16)[name = tensor<string, []>("input_109_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_0_adain1_2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37344256))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37409856))), name = tensor<string, []>("resblocks_0_adain1_2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_0_adain1_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_0_adain1_2_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37410432)))];
+            tensor<fp16, [1, 512]> linear_14_cast_fp16 = linear(bias = resblocks_0_adain1_2_fc_bias_to_fp16, weight = resblocks_0_adain1_2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_14_cast_fp16")];
+            tensor<int32, [3]> var_833 = const()[name = tensor<string, []>("op_833"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_59_cast_fp16 = reshape(shape = var_833, x = linear_14_cast_fp16)[name = tensor<string, []>("h_59_cast_fp16")];
+            tensor<int32, [2]> var_835_split_sizes_0 = const()[name = tensor<string, []>("op_835_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_835_axis_0 = const()[name = tensor<string, []>("op_835_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_835_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_835_cast_fp16_1 = split(axis = var_835_axis_0, split_sizes = var_835_split_sizes_0, x = h_59_cast_fp16)[name = tensor<string, []>("op_835_cast_fp16")];
+            tensor<fp16, []> var_837_promoted_to_fp16 = const()[name = tensor<string, []>("op_837_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_838_cast_fp16 = add(x = var_835_cast_fp16_0, y = var_837_promoted_to_fp16)[name = tensor<string, []>("op_838_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_841_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_596_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_109_cast_fp16)[name = tensor<string, []>("op_841_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_842_cast_fp16 = mul(x = var_838_cast_fp16, y = var_841_cast_fp16)[name = tensor<string, []>("op_842_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_13_cast_fp16 = add(x = var_842_cast_fp16, y = var_835_cast_fp16_1)[name = tensor<string, []>("xt_13_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_845_to_fp16 = const()[name = tensor<string, []>("op_845_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37411520)))];
+            tensor<fp16, [1, 256, ?]> var_846_cast_fp16 = mul(x = xt_13_cast_fp16, y = var_845_to_fp16)[name = tensor<string, []>("op_846_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_9_cast_fp16 = cos(x = var_846_cast_fp16)[name = tensor<string, []>("cv_9_cast_fp16")];
+            tensor<fp16, []> var_848_to_fp16 = const()[name = tensor<string, []>("op_848_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_849_cast_fp16 = mul(x = cv_9_cast_fp16, y = var_848_to_fp16)[name = tensor<string, []>("op_849_cast_fp16")];
+            tensor<fp16, []> var_850_to_fp16 = const()[name = tensor<string, []>("op_850_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_851_cast_fp16 = add(x = var_849_cast_fp16, y = var_850_to_fp16)[name = tensor<string, []>("op_851_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_852_to_fp16 = const()[name = tensor<string, []>("op_852_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37412096)))];
+            tensor<fp16, [1, 256, ?]> var_855_cast_fp16 = mul(x = var_851_cast_fp16, y = var_852_to_fp16)[name = tensor<string, []>("op_855_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_111_cast_fp16 = add(x = xt_13_cast_fp16, y = var_855_cast_fp16)[name = tensor<string, []>("input_111_cast_fp16")];
+            tensor<string, []> input_113_pad_type_0 = const()[name = tensor<string, []>("input_113_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_113_pad_0 = const()[name = tensor<string, []>("input_113_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> input_113_dilations_0 = const()[name = tensor<string, []>("input_113_dilations_0"), val = tensor<int32, [1]>([5])];
+            tensor<int32, [1]> input_113_strides_0 = const()[name = tensor<string, []>("input_113_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_113_groups_0 = const()[name = tensor<string, []>("input_113_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3]> weight_75_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [196608]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37412672))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37609344))), name = tensor<string, []>("weight_75_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 3])];
+            tensor<fp16, [256]> resblocks_0_convs1_2_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_0_convs1_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37609920)))];
+            tensor<fp16, [1, 256, ?]> input_113_cast_fp16 = conv(bias = resblocks_0_convs1_2_bias_to_fp16, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = weight_75_to_fp16_palettized, x = input_111_cast_fp16)[name = tensor<string, []>("input_113_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_0_adain2_2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37610496))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37676096))), name = tensor<string, []>("resblocks_0_adain2_2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_0_adain2_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_0_adain2_2_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37676672)))];
+            tensor<fp16, [1, 512]> linear_15_cast_fp16 = linear(bias = resblocks_0_adain2_2_fc_bias_to_fp16, weight = resblocks_0_adain2_2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_15_cast_fp16")];
+            tensor<int32, [3]> var_871 = const()[name = tensor<string, []>("op_871"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_63_cast_fp16 = reshape(shape = var_871, x = linear_15_cast_fp16)[name = tensor<string, []>("h_63_cast_fp16")];
+            tensor<int32, [2]> var_873_split_sizes_0 = const()[name = tensor<string, []>("op_873_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_873_axis_0 = const()[name = tensor<string, []>("op_873_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_873_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_873_cast_fp16_1 = split(axis = var_873_axis_0, split_sizes = var_873_split_sizes_0, x = h_63_cast_fp16)[name = tensor<string, []>("op_873_cast_fp16")];
+            tensor<fp16, []> var_875_promoted_to_fp16 = const()[name = tensor<string, []>("op_875_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_876_cast_fp16 = add(x = var_873_cast_fp16_0, y = var_875_promoted_to_fp16)[name = tensor<string, []>("op_876_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_879_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_596_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_113_cast_fp16)[name = tensor<string, []>("op_879_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_880_cast_fp16 = mul(x = var_876_cast_fp16, y = var_879_cast_fp16)[name = tensor<string, []>("op_880_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_15_cast_fp16 = add(x = var_880_cast_fp16, y = var_873_cast_fp16_1)[name = tensor<string, []>("xt_15_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_883_to_fp16 = const()[name = tensor<string, []>("op_883_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37677760)))];
+            tensor<fp16, [1, 256, ?]> var_884_cast_fp16 = mul(x = xt_15_cast_fp16, y = var_883_to_fp16)[name = tensor<string, []>("op_884_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_11_cast_fp16 = cos(x = var_884_cast_fp16)[name = tensor<string, []>("cv_11_cast_fp16")];
+            tensor<fp16, []> var_886_to_fp16 = const()[name = tensor<string, []>("op_886_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_887_cast_fp16 = mul(x = cv_11_cast_fp16, y = var_886_to_fp16)[name = tensor<string, []>("op_887_cast_fp16")];
+            tensor<fp16, []> var_888_to_fp16 = const()[name = tensor<string, []>("op_888_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_889_cast_fp16 = add(x = var_887_cast_fp16, y = var_888_to_fp16)[name = tensor<string, []>("op_889_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_890_to_fp16 = const()[name = tensor<string, []>("op_890_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37678336)))];
+            tensor<fp16, [1, 256, ?]> var_893_cast_fp16 = mul(x = var_889_cast_fp16, y = var_890_to_fp16)[name = tensor<string, []>("op_893_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_115_cast_fp16 = add(x = xt_15_cast_fp16, y = var_893_cast_fp16)[name = tensor<string, []>("input_115_cast_fp16")];
+            tensor<string, []> xt_17_pad_type_0 = const()[name = tensor<string, []>("xt_17_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_17_pad_0 = const()[name = tensor<string, []>("xt_17_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> xt_17_strides_0 = const()[name = tensor<string, []>("xt_17_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_17_dilations_0 = const()[name = tensor<string, []>("xt_17_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_17_groups_0 = const()[name = tensor<string, []>("xt_17_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 3]> weight_79_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [196608]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37678912))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37875584))), name = tensor<string, []>("weight_79_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 3])];
+            tensor<fp16, [256]> resblocks_0_convs2_2_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_0_convs2_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37876160)))];
+            tensor<fp16, [1, 256, ?]> xt_17_cast_fp16 = conv(bias = resblocks_0_convs2_2_bias_to_fp16, dilations = xt_17_dilations_0, groups = xt_17_groups_0, pad = xt_17_pad_0, pad_type = xt_17_pad_type_0, strides = xt_17_strides_0, weight = weight_79_to_fp16_palettized, x = input_115_cast_fp16)[name = tensor<string, []>("xt_17_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xs_1_cast_fp16 = add(x = xt_17_cast_fp16, y = input_109_cast_fp16)[name = tensor<string, []>("xs_1_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_1_adain1_0_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37876736))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37942336))), name = tensor<string, []>("resblocks_1_adain1_0_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_1_adain1_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_1_adain1_0_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37942912)))];
+            tensor<fp16, [1, 512]> linear_16_cast_fp16 = linear(bias = resblocks_1_adain1_0_fc_bias_to_fp16, weight = resblocks_1_adain1_0_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_16_cast_fp16")];
+            tensor<int32, [3]> var_993 = const()[name = tensor<string, []>("op_993"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_67_cast_fp16 = reshape(shape = var_993, x = linear_16_cast_fp16)[name = tensor<string, []>("h_67_cast_fp16")];
+            tensor<int32, [2]> var_995_split_sizes_0 = const()[name = tensor<string, []>("op_995_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_995_axis_0 = const()[name = tensor<string, []>("op_995_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_995_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_995_cast_fp16_1 = split(axis = var_995_axis_0, split_sizes = var_995_split_sizes_0, x = h_67_cast_fp16)[name = tensor<string, []>("op_995_cast_fp16")];
+            tensor<fp16, []> var_997_promoted_to_fp16 = const()[name = tensor<string, []>("op_997_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_998_cast_fp16 = add(x = var_995_cast_fp16_0, y = var_997_promoted_to_fp16)[name = tensor<string, []>("op_998_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1002_cast_fp16 = mul(x = var_998_cast_fp16, y = var_687_cast_fp16)[name = tensor<string, []>("op_1002_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_19_cast_fp16 = add(x = var_1002_cast_fp16, y = var_995_cast_fp16_1)[name = tensor<string, []>("xt_19_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1005_to_fp16 = const()[name = tensor<string, []>("op_1005_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37944000)))];
+            tensor<fp16, [1, 256, ?]> var_1006_cast_fp16 = mul(x = xt_19_cast_fp16, y = var_1005_to_fp16)[name = tensor<string, []>("op_1006_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_13_cast_fp16 = cos(x = var_1006_cast_fp16)[name = tensor<string, []>("cv_13_cast_fp16")];
+            tensor<fp16, []> var_1008_to_fp16 = const()[name = tensor<string, []>("op_1008_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1009_cast_fp16 = mul(x = cv_13_cast_fp16, y = var_1008_to_fp16)[name = tensor<string, []>("op_1009_cast_fp16")];
+            tensor<fp16, []> var_1010_to_fp16 = const()[name = tensor<string, []>("op_1010_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1011_cast_fp16 = add(x = var_1009_cast_fp16, y = var_1010_to_fp16)[name = tensor<string, []>("op_1011_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1012_to_fp16 = const()[name = tensor<string, []>("op_1012_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37944576)))];
+            tensor<fp16, [1, 256, ?]> var_1015_cast_fp16 = mul(x = var_1011_cast_fp16, y = var_1012_to_fp16)[name = tensor<string, []>("op_1015_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_117_cast_fp16 = add(x = xt_19_cast_fp16, y = var_1015_cast_fp16)[name = tensor<string, []>("input_117_cast_fp16")];
+            tensor<string, []> input_119_pad_type_0 = const()[name = tensor<string, []>("input_119_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_119_pad_0 = const()[name = tensor<string, []>("input_119_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> input_119_strides_0 = const()[name = tensor<string, []>("input_119_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_119_dilations_0 = const()[name = tensor<string, []>("input_119_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_119_groups_0 = const()[name = tensor<string, []>("input_119_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 7]> weight_83_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [458752]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37945152))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38403968))), name = tensor<string, []>("weight_83_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 7])];
+            tensor<fp16, [256]> resblocks_1_convs1_0_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_1_convs1_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38404544)))];
+            tensor<fp16, [1, 256, ?]> input_119_cast_fp16 = conv(bias = resblocks_1_convs1_0_bias_to_fp16, dilations = input_119_dilations_0, groups = input_119_groups_0, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = input_119_strides_0, weight = weight_83_to_fp16_palettized, x = input_117_cast_fp16)[name = tensor<string, []>("input_119_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_1_adain2_0_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38405120))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38470720))), name = tensor<string, []>("resblocks_1_adain2_0_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_1_adain2_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_1_adain2_0_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38471296)))];
+            tensor<fp16, [1, 512]> linear_17_cast_fp16 = linear(bias = resblocks_1_adain2_0_fc_bias_to_fp16, weight = resblocks_1_adain2_0_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_17_cast_fp16")];
+            tensor<int32, [3]> var_1031 = const()[name = tensor<string, []>("op_1031"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_71_cast_fp16 = reshape(shape = var_1031, x = linear_17_cast_fp16)[name = tensor<string, []>("h_71_cast_fp16")];
+            tensor<int32, [2]> var_1033_split_sizes_0 = const()[name = tensor<string, []>("op_1033_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_1033_axis_0 = const()[name = tensor<string, []>("op_1033_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_1033_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_1033_cast_fp16_1 = split(axis = var_1033_axis_0, split_sizes = var_1033_split_sizes_0, x = h_71_cast_fp16)[name = tensor<string, []>("op_1033_cast_fp16")];
+            tensor<fp16, []> var_1035_promoted_to_fp16 = const()[name = tensor<string, []>("op_1035_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_1036_cast_fp16 = add(x = var_1033_cast_fp16_0, y = var_1035_promoted_to_fp16)[name = tensor<string, []>("op_1036_cast_fp16")];
+            tensor<fp16, []> var_910_to_fp16 = const()[name = tensor<string, []>("op_910_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 256, ?]> var_1039_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_910_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_119_cast_fp16)[name = tensor<string, []>("op_1039_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1040_cast_fp16 = mul(x = var_1036_cast_fp16, y = var_1039_cast_fp16)[name = tensor<string, []>("op_1040_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_21_cast_fp16 = add(x = var_1040_cast_fp16, y = var_1033_cast_fp16_1)[name = tensor<string, []>("xt_21_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1043_to_fp16 = const()[name = tensor<string, []>("op_1043_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38472384)))];
+            tensor<fp16, [1, 256, ?]> var_1044_cast_fp16 = mul(x = xt_21_cast_fp16, y = var_1043_to_fp16)[name = tensor<string, []>("op_1044_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_15_cast_fp16 = cos(x = var_1044_cast_fp16)[name = tensor<string, []>("cv_15_cast_fp16")];
+            tensor<fp16, []> var_1046_to_fp16 = const()[name = tensor<string, []>("op_1046_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1047_cast_fp16 = mul(x = cv_15_cast_fp16, y = var_1046_to_fp16)[name = tensor<string, []>("op_1047_cast_fp16")];
+            tensor<fp16, []> var_1048_to_fp16 = const()[name = tensor<string, []>("op_1048_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1049_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_to_fp16)[name = tensor<string, []>("op_1049_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1050_to_fp16 = const()[name = tensor<string, []>("op_1050_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38472960)))];
+            tensor<fp16, [1, 256, ?]> var_1053_cast_fp16 = mul(x = var_1049_cast_fp16, y = var_1050_to_fp16)[name = tensor<string, []>("op_1053_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_121_cast_fp16 = add(x = xt_21_cast_fp16, y = var_1053_cast_fp16)[name = tensor<string, []>("input_121_cast_fp16")];
+            tensor<string, []> xt_23_pad_type_0 = const()[name = tensor<string, []>("xt_23_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_23_pad_0 = const()[name = tensor<string, []>("xt_23_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> xt_23_strides_0 = const()[name = tensor<string, []>("xt_23_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_23_dilations_0 = const()[name = tensor<string, []>("xt_23_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_23_groups_0 = const()[name = tensor<string, []>("xt_23_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 7]> weight_87_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [458752]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38473536))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38932352))), name = tensor<string, []>("weight_87_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 7])];
+            tensor<fp16, [256]> resblocks_1_convs2_0_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_1_convs2_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38932928)))];
+            tensor<fp16, [1, 256, ?]> xt_23_cast_fp16 = conv(bias = resblocks_1_convs2_0_bias_to_fp16, dilations = xt_23_dilations_0, groups = xt_23_groups_0, pad = xt_23_pad_0, pad_type = xt_23_pad_type_0, strides = xt_23_strides_0, weight = weight_87_to_fp16_palettized, x = input_121_cast_fp16)[name = tensor<string, []>("xt_23_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_123_cast_fp16 = add(x = xt_23_cast_fp16, y = input_93_cast_fp16)[name = tensor<string, []>("input_123_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_1_adain1_1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38933504))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38999104))), name = tensor<string, []>("resblocks_1_adain1_1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_1_adain1_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_1_adain1_1_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38999680)))];
+            tensor<fp16, [1, 512]> linear_18_cast_fp16 = linear(bias = resblocks_1_adain1_1_fc_bias_to_fp16, weight = resblocks_1_adain1_1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_18_cast_fp16")];
+            tensor<int32, [3]> var_1070 = const()[name = tensor<string, []>("op_1070"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_75_cast_fp16 = reshape(shape = var_1070, x = linear_18_cast_fp16)[name = tensor<string, []>("h_75_cast_fp16")];
+            tensor<int32, [2]> var_1072_split_sizes_0 = const()[name = tensor<string, []>("op_1072_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_1072_axis_0 = const()[name = tensor<string, []>("op_1072_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_1072_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_1072_cast_fp16_1 = split(axis = var_1072_axis_0, split_sizes = var_1072_split_sizes_0, x = h_75_cast_fp16)[name = tensor<string, []>("op_1072_cast_fp16")];
+            tensor<fp16, []> var_1074_promoted_to_fp16 = const()[name = tensor<string, []>("op_1074_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_1075_cast_fp16 = add(x = var_1072_cast_fp16_0, y = var_1074_promoted_to_fp16)[name = tensor<string, []>("op_1075_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1078_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_910_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_123_cast_fp16)[name = tensor<string, []>("op_1078_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1079_cast_fp16 = mul(x = var_1075_cast_fp16, y = var_1078_cast_fp16)[name = tensor<string, []>("op_1079_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_25_cast_fp16 = add(x = var_1079_cast_fp16, y = var_1072_cast_fp16_1)[name = tensor<string, []>("xt_25_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1082_to_fp16 = const()[name = tensor<string, []>("op_1082_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39000768)))];
+            tensor<fp16, [1, 256, ?]> var_1083_cast_fp16 = mul(x = xt_25_cast_fp16, y = var_1082_to_fp16)[name = tensor<string, []>("op_1083_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_17_cast_fp16 = cos(x = var_1083_cast_fp16)[name = tensor<string, []>("cv_17_cast_fp16")];
+            tensor<fp16, []> var_1085_to_fp16 = const()[name = tensor<string, []>("op_1085_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1086_cast_fp16 = mul(x = cv_17_cast_fp16, y = var_1085_to_fp16)[name = tensor<string, []>("op_1086_cast_fp16")];
+            tensor<fp16, []> var_1087_to_fp16 = const()[name = tensor<string, []>("op_1087_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1088_cast_fp16 = add(x = var_1086_cast_fp16, y = var_1087_to_fp16)[name = tensor<string, []>("op_1088_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1089_to_fp16 = const()[name = tensor<string, []>("op_1089_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39001344)))];
+            tensor<fp16, [1, 256, ?]> var_1092_cast_fp16 = mul(x = var_1088_cast_fp16, y = var_1089_to_fp16)[name = tensor<string, []>("op_1092_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_125_cast_fp16 = add(x = xt_25_cast_fp16, y = var_1092_cast_fp16)[name = tensor<string, []>("input_125_cast_fp16")];
+            tensor<string, []> input_127_pad_type_0 = const()[name = tensor<string, []>("input_127_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_127_pad_0 = const()[name = tensor<string, []>("input_127_pad_0"), val = tensor<int32, [2]>([9, 9])];
+            tensor<int32, [1]> input_127_dilations_0 = const()[name = tensor<string, []>("input_127_dilations_0"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> input_127_strides_0 = const()[name = tensor<string, []>("input_127_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_127_groups_0 = const()[name = tensor<string, []>("input_127_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 7]> weight_91_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [458752]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39001920))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39460736))), name = tensor<string, []>("weight_91_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 7])];
+            tensor<fp16, [256]> resblocks_1_convs1_1_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_1_convs1_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39461312)))];
+            tensor<fp16, [1, 256, ?]> input_127_cast_fp16 = conv(bias = resblocks_1_convs1_1_bias_to_fp16, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = weight_91_to_fp16_palettized, x = input_125_cast_fp16)[name = tensor<string, []>("input_127_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_1_adain2_1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39461888))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39527488))), name = tensor<string, []>("resblocks_1_adain2_1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_1_adain2_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_1_adain2_1_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39528064)))];
+            tensor<fp16, [1, 512]> linear_19_cast_fp16 = linear(bias = resblocks_1_adain2_1_fc_bias_to_fp16, weight = resblocks_1_adain2_1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_19_cast_fp16")];
+            tensor<int32, [3]> var_1108 = const()[name = tensor<string, []>("op_1108"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_79_cast_fp16 = reshape(shape = var_1108, x = linear_19_cast_fp16)[name = tensor<string, []>("h_79_cast_fp16")];
+            tensor<int32, [2]> var_1110_split_sizes_0 = const()[name = tensor<string, []>("op_1110_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_1110_axis_0 = const()[name = tensor<string, []>("op_1110_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_1110_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_1110_cast_fp16_1 = split(axis = var_1110_axis_0, split_sizes = var_1110_split_sizes_0, x = h_79_cast_fp16)[name = tensor<string, []>("op_1110_cast_fp16")];
+            tensor<fp16, []> var_1112_promoted_to_fp16 = const()[name = tensor<string, []>("op_1112_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_1113_cast_fp16 = add(x = var_1110_cast_fp16_0, y = var_1112_promoted_to_fp16)[name = tensor<string, []>("op_1113_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1116_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_910_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_127_cast_fp16)[name = tensor<string, []>("op_1116_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1117_cast_fp16 = mul(x = var_1113_cast_fp16, y = var_1116_cast_fp16)[name = tensor<string, []>("op_1117_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_27_cast_fp16 = add(x = var_1117_cast_fp16, y = var_1110_cast_fp16_1)[name = tensor<string, []>("xt_27_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1120_to_fp16 = const()[name = tensor<string, []>("op_1120_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39529152)))];
+            tensor<fp16, [1, 256, ?]> var_1121_cast_fp16 = mul(x = xt_27_cast_fp16, y = var_1120_to_fp16)[name = tensor<string, []>("op_1121_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_19_cast_fp16 = cos(x = var_1121_cast_fp16)[name = tensor<string, []>("cv_19_cast_fp16")];
+            tensor<fp16, []> var_1123_to_fp16 = const()[name = tensor<string, []>("op_1123_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1124_cast_fp16 = mul(x = cv_19_cast_fp16, y = var_1123_to_fp16)[name = tensor<string, []>("op_1124_cast_fp16")];
+            tensor<fp16, []> var_1125_to_fp16 = const()[name = tensor<string, []>("op_1125_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1126_cast_fp16 = add(x = var_1124_cast_fp16, y = var_1125_to_fp16)[name = tensor<string, []>("op_1126_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1127_to_fp16 = const()[name = tensor<string, []>("op_1127_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39529728)))];
+            tensor<fp16, [1, 256, ?]> var_1130_cast_fp16 = mul(x = var_1126_cast_fp16, y = var_1127_to_fp16)[name = tensor<string, []>("op_1130_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_129_cast_fp16 = add(x = xt_27_cast_fp16, y = var_1130_cast_fp16)[name = tensor<string, []>("input_129_cast_fp16")];
+            tensor<string, []> xt_29_pad_type_0 = const()[name = tensor<string, []>("xt_29_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_29_pad_0 = const()[name = tensor<string, []>("xt_29_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> xt_29_strides_0 = const()[name = tensor<string, []>("xt_29_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_29_dilations_0 = const()[name = tensor<string, []>("xt_29_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_29_groups_0 = const()[name = tensor<string, []>("xt_29_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 7]> weight_95_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [458752]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39530304))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39989120))), name = tensor<string, []>("weight_95_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 7])];
+            tensor<fp16, [256]> resblocks_1_convs2_1_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_1_convs2_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39989696)))];
+            tensor<fp16, [1, 256, ?]> xt_29_cast_fp16 = conv(bias = resblocks_1_convs2_1_bias_to_fp16, dilations = xt_29_dilations_0, groups = xt_29_groups_0, pad = xt_29_pad_0, pad_type = xt_29_pad_type_0, strides = xt_29_strides_0, weight = weight_95_to_fp16_palettized, x = input_129_cast_fp16)[name = tensor<string, []>("xt_29_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_131_cast_fp16 = add(x = xt_29_cast_fp16, y = input_123_cast_fp16)[name = tensor<string, []>("input_131_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_1_adain1_2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39990272))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40055872))), name = tensor<string, []>("resblocks_1_adain1_2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_1_adain1_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_1_adain1_2_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40056448)))];
+            tensor<fp16, [1, 512]> linear_20_cast_fp16 = linear(bias = resblocks_1_adain1_2_fc_bias_to_fp16, weight = resblocks_1_adain1_2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_20_cast_fp16")];
+            tensor<int32, [3]> var_1147 = const()[name = tensor<string, []>("op_1147"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_83_cast_fp16 = reshape(shape = var_1147, x = linear_20_cast_fp16)[name = tensor<string, []>("h_83_cast_fp16")];
+            tensor<int32, [2]> var_1149_split_sizes_0 = const()[name = tensor<string, []>("op_1149_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_1149_axis_0 = const()[name = tensor<string, []>("op_1149_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_1149_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_1149_cast_fp16_1 = split(axis = var_1149_axis_0, split_sizes = var_1149_split_sizes_0, x = h_83_cast_fp16)[name = tensor<string, []>("op_1149_cast_fp16")];
+            tensor<fp16, []> var_1151_promoted_to_fp16 = const()[name = tensor<string, []>("op_1151_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_1152_cast_fp16 = add(x = var_1149_cast_fp16_0, y = var_1151_promoted_to_fp16)[name = tensor<string, []>("op_1152_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1155_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_910_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_131_cast_fp16)[name = tensor<string, []>("op_1155_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1156_cast_fp16 = mul(x = var_1152_cast_fp16, y = var_1155_cast_fp16)[name = tensor<string, []>("op_1156_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_31_cast_fp16 = add(x = var_1156_cast_fp16, y = var_1149_cast_fp16_1)[name = tensor<string, []>("xt_31_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1159_to_fp16 = const()[name = tensor<string, []>("op_1159_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40057536)))];
+            tensor<fp16, [1, 256, ?]> var_1160_cast_fp16 = mul(x = xt_31_cast_fp16, y = var_1159_to_fp16)[name = tensor<string, []>("op_1160_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_21_cast_fp16 = cos(x = var_1160_cast_fp16)[name = tensor<string, []>("cv_21_cast_fp16")];
+            tensor<fp16, []> var_1162_to_fp16 = const()[name = tensor<string, []>("op_1162_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1163_cast_fp16 = mul(x = cv_21_cast_fp16, y = var_1162_to_fp16)[name = tensor<string, []>("op_1163_cast_fp16")];
+            tensor<fp16, []> var_1164_to_fp16 = const()[name = tensor<string, []>("op_1164_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1165_cast_fp16 = add(x = var_1163_cast_fp16, y = var_1164_to_fp16)[name = tensor<string, []>("op_1165_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1166_to_fp16 = const()[name = tensor<string, []>("op_1166_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40058112)))];
+            tensor<fp16, [1, 256, ?]> var_1169_cast_fp16 = mul(x = var_1165_cast_fp16, y = var_1166_to_fp16)[name = tensor<string, []>("op_1169_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_133_cast_fp16 = add(x = xt_31_cast_fp16, y = var_1169_cast_fp16)[name = tensor<string, []>("input_133_cast_fp16")];
+            tensor<string, []> input_135_pad_type_0 = const()[name = tensor<string, []>("input_135_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_135_pad_0 = const()[name = tensor<string, []>("input_135_pad_0"), val = tensor<int32, [2]>([15, 15])];
+            tensor<int32, [1]> input_135_dilations_0 = const()[name = tensor<string, []>("input_135_dilations_0"), val = tensor<int32, [1]>([5])];
+            tensor<int32, [1]> input_135_strides_0 = const()[name = tensor<string, []>("input_135_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_135_groups_0 = const()[name = tensor<string, []>("input_135_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 7]> weight_99_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [458752]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40058688))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40517504))), name = tensor<string, []>("weight_99_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 7])];
+            tensor<fp16, [256]> resblocks_1_convs1_2_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_1_convs1_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40518080)))];
+            tensor<fp16, [1, 256, ?]> input_135_cast_fp16 = conv(bias = resblocks_1_convs1_2_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = weight_99_to_fp16_palettized, x = input_133_cast_fp16)[name = tensor<string, []>("input_135_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_1_adain2_2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40518656))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40584256))), name = tensor<string, []>("resblocks_1_adain2_2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_1_adain2_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_1_adain2_2_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40584832)))];
+            tensor<fp16, [1, 512]> linear_21_cast_fp16 = linear(bias = resblocks_1_adain2_2_fc_bias_to_fp16, weight = resblocks_1_adain2_2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_21_cast_fp16")];
+            tensor<int32, [3]> var_1185 = const()[name = tensor<string, []>("op_1185"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_87_cast_fp16 = reshape(shape = var_1185, x = linear_21_cast_fp16)[name = tensor<string, []>("h_87_cast_fp16")];
+            tensor<int32, [2]> var_1187_split_sizes_0 = const()[name = tensor<string, []>("op_1187_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_1187_axis_0 = const()[name = tensor<string, []>("op_1187_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_1187_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_1187_cast_fp16_1 = split(axis = var_1187_axis_0, split_sizes = var_1187_split_sizes_0, x = h_87_cast_fp16)[name = tensor<string, []>("op_1187_cast_fp16")];
+            tensor<fp16, []> var_1189_promoted_to_fp16 = const()[name = tensor<string, []>("op_1189_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_1190_cast_fp16 = add(x = var_1187_cast_fp16_0, y = var_1189_promoted_to_fp16)[name = tensor<string, []>("op_1190_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1193_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_910_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_135_cast_fp16)[name = tensor<string, []>("op_1193_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1194_cast_fp16 = mul(x = var_1190_cast_fp16, y = var_1193_cast_fp16)[name = tensor<string, []>("op_1194_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_33_cast_fp16 = add(x = var_1194_cast_fp16, y = var_1187_cast_fp16_1)[name = tensor<string, []>("xt_33_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1197_to_fp16 = const()[name = tensor<string, []>("op_1197_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40585920)))];
+            tensor<fp16, [1, 256, ?]> var_1198_cast_fp16 = mul(x = xt_33_cast_fp16, y = var_1197_to_fp16)[name = tensor<string, []>("op_1198_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_23_cast_fp16 = cos(x = var_1198_cast_fp16)[name = tensor<string, []>("cv_23_cast_fp16")];
+            tensor<fp16, []> var_1200_to_fp16 = const()[name = tensor<string, []>("op_1200_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1201_cast_fp16 = mul(x = cv_23_cast_fp16, y = var_1200_to_fp16)[name = tensor<string, []>("op_1201_cast_fp16")];
+            tensor<fp16, []> var_1202_to_fp16 = const()[name = tensor<string, []>("op_1202_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1203_cast_fp16 = add(x = var_1201_cast_fp16, y = var_1202_to_fp16)[name = tensor<string, []>("op_1203_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1204_to_fp16 = const()[name = tensor<string, []>("op_1204_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40586496)))];
+            tensor<fp16, [1, 256, ?]> var_1207_cast_fp16 = mul(x = var_1203_cast_fp16, y = var_1204_to_fp16)[name = tensor<string, []>("op_1207_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_137_cast_fp16 = add(x = xt_33_cast_fp16, y = var_1207_cast_fp16)[name = tensor<string, []>("input_137_cast_fp16")];
+            tensor<string, []> xt_35_pad_type_0 = const()[name = tensor<string, []>("xt_35_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_35_pad_0 = const()[name = tensor<string, []>("xt_35_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> xt_35_strides_0 = const()[name = tensor<string, []>("xt_35_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_35_dilations_0 = const()[name = tensor<string, []>("xt_35_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_35_groups_0 = const()[name = tensor<string, []>("xt_35_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 7]> weight_103_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [458752]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40587072))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41045888))), name = tensor<string, []>("weight_103_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 7])];
+            tensor<fp16, [256]> resblocks_1_convs2_2_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_1_convs2_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41046464)))];
+            tensor<fp16, [1, 256, ?]> xt_35_cast_fp16 = conv(bias = resblocks_1_convs2_2_bias_to_fp16, dilations = xt_35_dilations_0, groups = xt_35_groups_0, pad = xt_35_pad_0, pad_type = xt_35_pad_type_0, strides = xt_35_strides_0, weight = weight_103_to_fp16_palettized, x = input_137_cast_fp16)[name = tensor<string, []>("xt_35_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1216_cast_fp16 = add(x = xt_35_cast_fp16, y = input_131_cast_fp16)[name = tensor<string, []>("op_1216_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xs_3_cast_fp16 = add(x = xs_1_cast_fp16, y = var_1216_cast_fp16)[name = tensor<string, []>("xs_3_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_2_adain1_0_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41047040))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41112640))), name = tensor<string, []>("resblocks_2_adain1_0_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_2_adain1_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_2_adain1_0_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41113216)))];
+            tensor<fp16, [1, 512]> linear_22_cast_fp16 = linear(bias = resblocks_2_adain1_0_fc_bias_to_fp16, weight = resblocks_2_adain1_0_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_22_cast_fp16")];
+            tensor<int32, [3]> var_1309 = const()[name = tensor<string, []>("op_1309"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_91_cast_fp16 = reshape(shape = var_1309, x = linear_22_cast_fp16)[name = tensor<string, []>("h_91_cast_fp16")];
+            tensor<int32, [2]> var_1311_split_sizes_0 = const()[name = tensor<string, []>("op_1311_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_1311_axis_0 = const()[name = tensor<string, []>("op_1311_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_1311_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_1311_cast_fp16_1 = split(axis = var_1311_axis_0, split_sizes = var_1311_split_sizes_0, x = h_91_cast_fp16)[name = tensor<string, []>("op_1311_cast_fp16")];
+            tensor<fp16, []> var_1313_promoted_to_fp16 = const()[name = tensor<string, []>("op_1313_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_1314_cast_fp16 = add(x = var_1311_cast_fp16_0, y = var_1313_promoted_to_fp16)[name = tensor<string, []>("op_1314_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1318_cast_fp16 = mul(x = var_1314_cast_fp16, y = var_687_cast_fp16)[name = tensor<string, []>("op_1318_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_37_cast_fp16 = add(x = var_1318_cast_fp16, y = var_1311_cast_fp16_1)[name = tensor<string, []>("xt_37_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1321_to_fp16 = const()[name = tensor<string, []>("op_1321_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41114304)))];
+            tensor<fp16, [1, 256, ?]> var_1322_cast_fp16 = mul(x = xt_37_cast_fp16, y = var_1321_to_fp16)[name = tensor<string, []>("op_1322_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_25_cast_fp16 = cos(x = var_1322_cast_fp16)[name = tensor<string, []>("cv_25_cast_fp16")];
+            tensor<fp16, []> var_1324_to_fp16 = const()[name = tensor<string, []>("op_1324_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1325_cast_fp16 = mul(x = cv_25_cast_fp16, y = var_1324_to_fp16)[name = tensor<string, []>("op_1325_cast_fp16")];
+            tensor<fp16, []> var_1326_to_fp16 = const()[name = tensor<string, []>("op_1326_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1327_cast_fp16 = add(x = var_1325_cast_fp16, y = var_1326_to_fp16)[name = tensor<string, []>("op_1327_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1328_to_fp16 = const()[name = tensor<string, []>("op_1328_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41114880)))];
+            tensor<fp16, [1, 256, ?]> var_1331_cast_fp16 = mul(x = var_1327_cast_fp16, y = var_1328_to_fp16)[name = tensor<string, []>("op_1331_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_139_cast_fp16 = add(x = xt_37_cast_fp16, y = var_1331_cast_fp16)[name = tensor<string, []>("input_139_cast_fp16")];
+            tensor<string, []> input_141_pad_type_0 = const()[name = tensor<string, []>("input_141_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_141_pad_0 = const()[name = tensor<string, []>("input_141_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> input_141_strides_0 = const()[name = tensor<string, []>("input_141_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_141_dilations_0 = const()[name = tensor<string, []>("input_141_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_141_groups_0 = const()[name = tensor<string, []>("input_141_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 11]> weight_107_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [720896]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41115456))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41836416))), name = tensor<string, []>("weight_107_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 11])];
+            tensor<fp16, [256]> resblocks_2_convs1_0_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_2_convs1_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41836992)))];
+            tensor<fp16, [1, 256, ?]> input_141_cast_fp16 = conv(bias = resblocks_2_convs1_0_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = weight_107_to_fp16_palettized, x = input_139_cast_fp16)[name = tensor<string, []>("input_141_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_2_adain2_0_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41837568))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41903168))), name = tensor<string, []>("resblocks_2_adain2_0_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_2_adain2_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_2_adain2_0_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41903744)))];
+            tensor<fp16, [1, 512]> linear_23_cast_fp16 = linear(bias = resblocks_2_adain2_0_fc_bias_to_fp16, weight = resblocks_2_adain2_0_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_23_cast_fp16")];
+            tensor<int32, [3]> var_1347 = const()[name = tensor<string, []>("op_1347"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_95_cast_fp16 = reshape(shape = var_1347, x = linear_23_cast_fp16)[name = tensor<string, []>("h_95_cast_fp16")];
+            tensor<int32, [2]> var_1349_split_sizes_0 = const()[name = tensor<string, []>("op_1349_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_1349_axis_0 = const()[name = tensor<string, []>("op_1349_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_1349_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_1349_cast_fp16_1 = split(axis = var_1349_axis_0, split_sizes = var_1349_split_sizes_0, x = h_95_cast_fp16)[name = tensor<string, []>("op_1349_cast_fp16")];
+            tensor<fp16, []> var_1351_promoted_to_fp16 = const()[name = tensor<string, []>("op_1351_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_1352_cast_fp16 = add(x = var_1349_cast_fp16_0, y = var_1351_promoted_to_fp16)[name = tensor<string, []>("op_1352_cast_fp16")];
+            tensor<fp16, []> var_1226_to_fp16 = const()[name = tensor<string, []>("op_1226_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 256, ?]> var_1355_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_1226_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_141_cast_fp16)[name = tensor<string, []>("op_1355_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1356_cast_fp16 = mul(x = var_1352_cast_fp16, y = var_1355_cast_fp16)[name = tensor<string, []>("op_1356_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_39_cast_fp16 = add(x = var_1356_cast_fp16, y = var_1349_cast_fp16_1)[name = tensor<string, []>("xt_39_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1359_to_fp16 = const()[name = tensor<string, []>("op_1359_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41904832)))];
+            tensor<fp16, [1, 256, ?]> var_1360_cast_fp16 = mul(x = xt_39_cast_fp16, y = var_1359_to_fp16)[name = tensor<string, []>("op_1360_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_27_cast_fp16 = cos(x = var_1360_cast_fp16)[name = tensor<string, []>("cv_27_cast_fp16")];
+            tensor<fp16, []> var_1362_to_fp16 = const()[name = tensor<string, []>("op_1362_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1363_cast_fp16 = mul(x = cv_27_cast_fp16, y = var_1362_to_fp16)[name = tensor<string, []>("op_1363_cast_fp16")];
+            tensor<fp16, []> var_1364_to_fp16 = const()[name = tensor<string, []>("op_1364_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1365_cast_fp16 = add(x = var_1363_cast_fp16, y = var_1364_to_fp16)[name = tensor<string, []>("op_1365_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1366_to_fp16 = const()[name = tensor<string, []>("op_1366_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41905408)))];
+            tensor<fp16, [1, 256, ?]> var_1369_cast_fp16 = mul(x = var_1365_cast_fp16, y = var_1366_to_fp16)[name = tensor<string, []>("op_1369_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_143_cast_fp16 = add(x = xt_39_cast_fp16, y = var_1369_cast_fp16)[name = tensor<string, []>("input_143_cast_fp16")];
+            tensor<string, []> xt_41_pad_type_0 = const()[name = tensor<string, []>("xt_41_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_41_pad_0 = const()[name = tensor<string, []>("xt_41_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> xt_41_strides_0 = const()[name = tensor<string, []>("xt_41_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_41_dilations_0 = const()[name = tensor<string, []>("xt_41_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_41_groups_0 = const()[name = tensor<string, []>("xt_41_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 11]> weight_111_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [720896]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41905984))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42626944))), name = tensor<string, []>("weight_111_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 11])];
+            tensor<fp16, [256]> resblocks_2_convs2_0_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_2_convs2_0_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42627520)))];
+            tensor<fp16, [1, 256, ?]> xt_41_cast_fp16 = conv(bias = resblocks_2_convs2_0_bias_to_fp16, dilations = xt_41_dilations_0, groups = xt_41_groups_0, pad = xt_41_pad_0, pad_type = xt_41_pad_type_0, strides = xt_41_strides_0, weight = weight_111_to_fp16_palettized, x = input_143_cast_fp16)[name = tensor<string, []>("xt_41_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_145_cast_fp16 = add(x = xt_41_cast_fp16, y = input_93_cast_fp16)[name = tensor<string, []>("input_145_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_2_adain1_1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42628096))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42693696))), name = tensor<string, []>("resblocks_2_adain1_1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_2_adain1_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_2_adain1_1_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42694272)))];
+            tensor<fp16, [1, 512]> linear_24_cast_fp16 = linear(bias = resblocks_2_adain1_1_fc_bias_to_fp16, weight = resblocks_2_adain1_1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_24_cast_fp16")];
+            tensor<int32, [3]> var_1386 = const()[name = tensor<string, []>("op_1386"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_99_cast_fp16 = reshape(shape = var_1386, x = linear_24_cast_fp16)[name = tensor<string, []>("h_99_cast_fp16")];
+            tensor<int32, [2]> var_1388_split_sizes_0 = const()[name = tensor<string, []>("op_1388_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_1388_axis_0 = const()[name = tensor<string, []>("op_1388_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_1388_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_1388_cast_fp16_1 = split(axis = var_1388_axis_0, split_sizes = var_1388_split_sizes_0, x = h_99_cast_fp16)[name = tensor<string, []>("op_1388_cast_fp16")];
+            tensor<fp16, []> var_1390_promoted_to_fp16 = const()[name = tensor<string, []>("op_1390_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_1391_cast_fp16 = add(x = var_1388_cast_fp16_0, y = var_1390_promoted_to_fp16)[name = tensor<string, []>("op_1391_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1394_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_1226_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_145_cast_fp16)[name = tensor<string, []>("op_1394_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1395_cast_fp16 = mul(x = var_1391_cast_fp16, y = var_1394_cast_fp16)[name = tensor<string, []>("op_1395_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_43_cast_fp16 = add(x = var_1395_cast_fp16, y = var_1388_cast_fp16_1)[name = tensor<string, []>("xt_43_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1398_to_fp16 = const()[name = tensor<string, []>("op_1398_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42695360)))];
+            tensor<fp16, [1, 256, ?]> var_1399_cast_fp16 = mul(x = xt_43_cast_fp16, y = var_1398_to_fp16)[name = tensor<string, []>("op_1399_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_29_cast_fp16 = cos(x = var_1399_cast_fp16)[name = tensor<string, []>("cv_29_cast_fp16")];
+            tensor<fp16, []> var_1401_to_fp16 = const()[name = tensor<string, []>("op_1401_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1402_cast_fp16 = mul(x = cv_29_cast_fp16, y = var_1401_to_fp16)[name = tensor<string, []>("op_1402_cast_fp16")];
+            tensor<fp16, []> var_1403_to_fp16 = const()[name = tensor<string, []>("op_1403_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1404_cast_fp16 = add(x = var_1402_cast_fp16, y = var_1403_to_fp16)[name = tensor<string, []>("op_1404_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1405_to_fp16 = const()[name = tensor<string, []>("op_1405_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42695936)))];
+            tensor<fp16, [1, 256, ?]> var_1408_cast_fp16 = mul(x = var_1404_cast_fp16, y = var_1405_to_fp16)[name = tensor<string, []>("op_1408_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_147_cast_fp16 = add(x = xt_43_cast_fp16, y = var_1408_cast_fp16)[name = tensor<string, []>("input_147_cast_fp16")];
+            tensor<string, []> input_149_pad_type_0 = const()[name = tensor<string, []>("input_149_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_149_pad_0 = const()[name = tensor<string, []>("input_149_pad_0"), val = tensor<int32, [2]>([15, 15])];
+            tensor<int32, [1]> input_149_dilations_0 = const()[name = tensor<string, []>("input_149_dilations_0"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> input_149_strides_0 = const()[name = tensor<string, []>("input_149_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_149_groups_0 = const()[name = tensor<string, []>("input_149_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 11]> weight_115_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [720896]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42696512))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43417472))), name = tensor<string, []>("weight_115_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 11])];
+            tensor<fp16, [256]> resblocks_2_convs1_1_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_2_convs1_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43418048)))];
+            tensor<fp16, [1, 256, ?]> input_149_cast_fp16 = conv(bias = resblocks_2_convs1_1_bias_to_fp16, dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = weight_115_to_fp16_palettized, x = input_147_cast_fp16)[name = tensor<string, []>("input_149_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_2_adain2_1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43418624))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43484224))), name = tensor<string, []>("resblocks_2_adain2_1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_2_adain2_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_2_adain2_1_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43484800)))];
+            tensor<fp16, [1, 512]> linear_25_cast_fp16 = linear(bias = resblocks_2_adain2_1_fc_bias_to_fp16, weight = resblocks_2_adain2_1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_25_cast_fp16")];
+            tensor<int32, [3]> var_1424 = const()[name = tensor<string, []>("op_1424"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_103_cast_fp16 = reshape(shape = var_1424, x = linear_25_cast_fp16)[name = tensor<string, []>("h_103_cast_fp16")];
+            tensor<int32, [2]> var_1426_split_sizes_0 = const()[name = tensor<string, []>("op_1426_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_1426_axis_0 = const()[name = tensor<string, []>("op_1426_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_1426_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_1426_cast_fp16_1 = split(axis = var_1426_axis_0, split_sizes = var_1426_split_sizes_0, x = h_103_cast_fp16)[name = tensor<string, []>("op_1426_cast_fp16")];
+            tensor<fp16, []> var_1428_promoted_to_fp16 = const()[name = tensor<string, []>("op_1428_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_1429_cast_fp16 = add(x = var_1426_cast_fp16_0, y = var_1428_promoted_to_fp16)[name = tensor<string, []>("op_1429_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1432_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_1226_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_149_cast_fp16)[name = tensor<string, []>("op_1432_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1433_cast_fp16 = mul(x = var_1429_cast_fp16, y = var_1432_cast_fp16)[name = tensor<string, []>("op_1433_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_45_cast_fp16 = add(x = var_1433_cast_fp16, y = var_1426_cast_fp16_1)[name = tensor<string, []>("xt_45_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1436_to_fp16 = const()[name = tensor<string, []>("op_1436_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43485888)))];
+            tensor<fp16, [1, 256, ?]> var_1437_cast_fp16 = mul(x = xt_45_cast_fp16, y = var_1436_to_fp16)[name = tensor<string, []>("op_1437_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_31_cast_fp16 = cos(x = var_1437_cast_fp16)[name = tensor<string, []>("cv_31_cast_fp16")];
+            tensor<fp16, []> var_1439_to_fp16 = const()[name = tensor<string, []>("op_1439_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1440_cast_fp16 = mul(x = cv_31_cast_fp16, y = var_1439_to_fp16)[name = tensor<string, []>("op_1440_cast_fp16")];
+            tensor<fp16, []> var_1441_to_fp16 = const()[name = tensor<string, []>("op_1441_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1442_cast_fp16 = add(x = var_1440_cast_fp16, y = var_1441_to_fp16)[name = tensor<string, []>("op_1442_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1443_to_fp16 = const()[name = tensor<string, []>("op_1443_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43486464)))];
+            tensor<fp16, [1, 256, ?]> var_1446_cast_fp16 = mul(x = var_1442_cast_fp16, y = var_1443_to_fp16)[name = tensor<string, []>("op_1446_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_151_cast_fp16 = add(x = xt_45_cast_fp16, y = var_1446_cast_fp16)[name = tensor<string, []>("input_151_cast_fp16")];
+            tensor<string, []> xt_47_pad_type_0 = const()[name = tensor<string, []>("xt_47_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_47_pad_0 = const()[name = tensor<string, []>("xt_47_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> xt_47_strides_0 = const()[name = tensor<string, []>("xt_47_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_47_dilations_0 = const()[name = tensor<string, []>("xt_47_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_47_groups_0 = const()[name = tensor<string, []>("xt_47_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 11]> weight_119_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [720896]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(43487040))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44208000))), name = tensor<string, []>("weight_119_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 11])];
+            tensor<fp16, [256]> resblocks_2_convs2_1_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_2_convs2_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44208576)))];
+            tensor<fp16, [1, 256, ?]> xt_47_cast_fp16 = conv(bias = resblocks_2_convs2_1_bias_to_fp16, dilations = xt_47_dilations_0, groups = xt_47_groups_0, pad = xt_47_pad_0, pad_type = xt_47_pad_type_0, strides = xt_47_strides_0, weight = weight_119_to_fp16_palettized, x = input_151_cast_fp16)[name = tensor<string, []>("xt_47_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_153_cast_fp16 = add(x = xt_47_cast_fp16, y = input_145_cast_fp16)[name = tensor<string, []>("input_153_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_2_adain1_2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44209152))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44274752))), name = tensor<string, []>("resblocks_2_adain1_2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_2_adain1_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_2_adain1_2_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44275328)))];
+            tensor<fp16, [1, 512]> linear_26_cast_fp16 = linear(bias = resblocks_2_adain1_2_fc_bias_to_fp16, weight = resblocks_2_adain1_2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_26_cast_fp16")];
+            tensor<int32, [3]> var_1463 = const()[name = tensor<string, []>("op_1463"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_107_cast_fp16 = reshape(shape = var_1463, x = linear_26_cast_fp16)[name = tensor<string, []>("h_107_cast_fp16")];
+            tensor<int32, [2]> var_1465_split_sizes_0 = const()[name = tensor<string, []>("op_1465_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_1465_axis_0 = const()[name = tensor<string, []>("op_1465_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_1465_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_1465_cast_fp16_1 = split(axis = var_1465_axis_0, split_sizes = var_1465_split_sizes_0, x = h_107_cast_fp16)[name = tensor<string, []>("op_1465_cast_fp16")];
+            tensor<fp16, []> var_1467_promoted_to_fp16 = const()[name = tensor<string, []>("op_1467_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_1468_cast_fp16 = add(x = var_1465_cast_fp16_0, y = var_1467_promoted_to_fp16)[name = tensor<string, []>("op_1468_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1471_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_1226_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_153_cast_fp16)[name = tensor<string, []>("op_1471_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1472_cast_fp16 = mul(x = var_1468_cast_fp16, y = var_1471_cast_fp16)[name = tensor<string, []>("op_1472_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_49_cast_fp16 = add(x = var_1472_cast_fp16, y = var_1465_cast_fp16_1)[name = tensor<string, []>("xt_49_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1475_to_fp16 = const()[name = tensor<string, []>("op_1475_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44276416)))];
+            tensor<fp16, [1, 256, ?]> var_1476_cast_fp16 = mul(x = xt_49_cast_fp16, y = var_1475_to_fp16)[name = tensor<string, []>("op_1476_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_33_cast_fp16 = cos(x = var_1476_cast_fp16)[name = tensor<string, []>("cv_33_cast_fp16")];
+            tensor<fp16, []> var_1478_to_fp16 = const()[name = tensor<string, []>("op_1478_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1479_cast_fp16 = mul(x = cv_33_cast_fp16, y = var_1478_to_fp16)[name = tensor<string, []>("op_1479_cast_fp16")];
+            tensor<fp16, []> var_1480_to_fp16 = const()[name = tensor<string, []>("op_1480_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1481_cast_fp16 = add(x = var_1479_cast_fp16, y = var_1480_to_fp16)[name = tensor<string, []>("op_1481_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1482_to_fp16 = const()[name = tensor<string, []>("op_1482_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44276992)))];
+            tensor<fp16, [1, 256, ?]> var_1485_cast_fp16 = mul(x = var_1481_cast_fp16, y = var_1482_to_fp16)[name = tensor<string, []>("op_1485_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_155_cast_fp16 = add(x = xt_49_cast_fp16, y = var_1485_cast_fp16)[name = tensor<string, []>("input_155_cast_fp16")];
+            tensor<string, []> input_157_pad_type_0 = const()[name = tensor<string, []>("input_157_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_157_pad_0 = const()[name = tensor<string, []>("input_157_pad_0"), val = tensor<int32, [2]>([25, 25])];
+            tensor<int32, [1]> input_157_dilations_0 = const()[name = tensor<string, []>("input_157_dilations_0"), val = tensor<int32, [1]>([5])];
+            tensor<int32, [1]> input_157_strides_0 = const()[name = tensor<string, []>("input_157_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_157_groups_0 = const()[name = tensor<string, []>("input_157_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 11]> weight_123_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [720896]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44277568))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44998528))), name = tensor<string, []>("weight_123_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 11])];
+            tensor<fp16, [256]> resblocks_2_convs1_2_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_2_convs1_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44999104)))];
+            tensor<fp16, [1, 256, ?]> input_157_cast_fp16 = conv(bias = resblocks_2_convs1_2_bias_to_fp16, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = weight_123_to_fp16_palettized, x = input_155_cast_fp16)[name = tensor<string, []>("input_157_cast_fp16")];
+            tensor<fp16, [512, 128]> resblocks_2_adain2_2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [65536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44999680))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45065280))), name = tensor<string, []>("resblocks_2_adain2_2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([512, 128])];
+            tensor<fp16, [512]> resblocks_2_adain2_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_2_adain2_2_fc_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45065856)))];
+            tensor<fp16, [1, 512]> linear_27_cast_fp16 = linear(bias = resblocks_2_adain2_2_fc_bias_to_fp16, weight = resblocks_2_adain2_2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_27_cast_fp16")];
+            tensor<int32, [3]> var_1501 = const()[name = tensor<string, []>("op_1501"), val = tensor<int32, [3]>([1, 512, 1])];
+            tensor<fp16, [1, 512, 1]> h_111_cast_fp16 = reshape(shape = var_1501, x = linear_27_cast_fp16)[name = tensor<string, []>("h_111_cast_fp16")];
+            tensor<int32, [2]> var_1503_split_sizes_0 = const()[name = tensor<string, []>("op_1503_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
+            tensor<int32, []> var_1503_axis_0 = const()[name = tensor<string, []>("op_1503_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 256, 1]> var_1503_cast_fp16_0, tensor<fp16, [1, 256, 1]> var_1503_cast_fp16_1 = split(axis = var_1503_axis_0, split_sizes = var_1503_split_sizes_0, x = h_111_cast_fp16)[name = tensor<string, []>("op_1503_cast_fp16")];
+            tensor<fp16, []> var_1505_promoted_to_fp16 = const()[name = tensor<string, []>("op_1505_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 256, 1]> var_1506_cast_fp16 = add(x = var_1503_cast_fp16_0, y = var_1505_promoted_to_fp16)[name = tensor<string, []>("op_1506_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1509_cast_fp16 = instance_norm(beta = resblocks_0_adain1_0_norm_bias_to_fp16, epsilon = var_1226_to_fp16, gamma = resblocks_0_adain1_0_norm_weight_to_fp16, x = input_157_cast_fp16)[name = tensor<string, []>("op_1509_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1510_cast_fp16 = mul(x = var_1506_cast_fp16, y = var_1509_cast_fp16)[name = tensor<string, []>("op_1510_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xt_51_cast_fp16 = add(x = var_1510_cast_fp16, y = var_1503_cast_fp16_1)[name = tensor<string, []>("xt_51_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1513_to_fp16 = const()[name = tensor<string, []>("op_1513_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45066944)))];
+            tensor<fp16, [1, 256, ?]> var_1514_cast_fp16 = mul(x = xt_51_cast_fp16, y = var_1513_to_fp16)[name = tensor<string, []>("op_1514_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> cv_35_cast_fp16 = cos(x = var_1514_cast_fp16)[name = tensor<string, []>("cv_35_cast_fp16")];
+            tensor<fp16, []> var_1516_to_fp16 = const()[name = tensor<string, []>("op_1516_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1517_cast_fp16 = mul(x = cv_35_cast_fp16, y = var_1516_to_fp16)[name = tensor<string, []>("op_1517_cast_fp16")];
+            tensor<fp16, []> var_1518_to_fp16 = const()[name = tensor<string, []>("op_1518_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 256, ?]> var_1519_cast_fp16 = add(x = var_1517_cast_fp16, y = var_1518_to_fp16)[name = tensor<string, []>("op_1519_cast_fp16")];
+            tensor<fp16, [1, 256, 1]> var_1520_to_fp16 = const()[name = tensor<string, []>("op_1520_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45067520)))];
+            tensor<fp16, [1, 256, ?]> var_1523_cast_fp16 = mul(x = var_1519_cast_fp16, y = var_1520_to_fp16)[name = tensor<string, []>("op_1523_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> input_159_cast_fp16 = add(x = xt_51_cast_fp16, y = var_1523_cast_fp16)[name = tensor<string, []>("input_159_cast_fp16")];
+            tensor<string, []> xt_53_pad_type_0 = const()[name = tensor<string, []>("xt_53_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_53_pad_0 = const()[name = tensor<string, []>("xt_53_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> xt_53_strides_0 = const()[name = tensor<string, []>("xt_53_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_53_dilations_0 = const()[name = tensor<string, []>("xt_53_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_53_groups_0 = const()[name = tensor<string, []>("xt_53_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 256, 11]> weight_127_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [720896]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45068096))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45789056))), name = tensor<string, []>("weight_127_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 256, 11])];
+            tensor<fp16, [256]> resblocks_2_convs2_2_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_2_convs2_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45789632)))];
+            tensor<fp16, [1, 256, ?]> xt_53_cast_fp16 = conv(bias = resblocks_2_convs2_2_bias_to_fp16, dilations = xt_53_dilations_0, groups = xt_53_groups_0, pad = xt_53_pad_0, pad_type = xt_53_pad_type_0, strides = xt_53_strides_0, weight = weight_127_to_fp16_palettized, x = input_159_cast_fp16)[name = tensor<string, []>("xt_53_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> var_1532_cast_fp16 = add(x = xt_53_cast_fp16, y = input_153_cast_fp16)[name = tensor<string, []>("op_1532_cast_fp16")];
+            tensor<fp16, [1, 256, ?]> xs_5_cast_fp16 = add(x = xs_3_cast_fp16, y = var_1532_cast_fp16)[name = tensor<string, []>("xs_5_cast_fp16")];
+            tensor<fp16, []> _inversed_input_161_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_input_161_y_0_to_fp16"), val = tensor<fp16, []>(0x1.554p-2)];
+            tensor<fp16, [1, 256, ?]> _inversed_input_161_cast_fp16 = mul(x = xs_5_cast_fp16, y = _inversed_input_161_y_0_to_fp16)[name = tensor<string, []>("_inversed_input_161_cast_fp16")];
+            tensor<fp32, []> var_1537 = const()[name = tensor<string, []>("op_1537"), val = tensor<fp32, []>(0x1.99999ap-4)];
+            tensor<fp16, [1, 256, ?]> input_163_cast_fp16 = leaky_relu(alpha = var_1537, x = _inversed_input_161_cast_fp16)[name = tensor<string, []>("input_163_cast_fp16")];
+            tensor<string, []> x_11_pad_type_0 = const()[name = tensor<string, []>("x_11_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> x_11_pad_0 = const()[name = tensor<string, []>("x_11_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> x_11_strides_0 = const()[name = tensor<string, []>("x_11_strides_0"), val = tensor<int32, [1]>([6])];
+            tensor<int32, [1]> x_11_dilations_0 = const()[name = tensor<string, []>("x_11_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> x_11_groups_0 = const()[name = tensor<string, []>("x_11_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [256, 128, 12]> op_1540_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [393216]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45790208))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46183488))), name = tensor<string, []>("op_1540_to_fp16_palettized"), shape = tensor<uint32, [3]>([256, 128, 12])];
+            tensor<fp16, [128]> ups_1_bias_to_fp16 = const()[name = tensor<string, []>("ups_1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46184064)))];
+            tensor<fp16, [1, 128, ?]> x_11_cast_fp16 = conv_transpose(bias = ups_1_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 = op_1540_to_fp16_palettized, x = input_163_cast_fp16)[name = tensor<string, []>("x_11_cast_fp16")];
+            tensor<int32, [3]> var_1567_begin_0 = const()[name = tensor<string, []>("op_1567_begin_0"), val = tensor<int32, [3]>([0, 0, 1])];
+            tensor<int32, [3]> var_1567_end_0 = const()[name = tensor<string, []>("op_1567_end_0"), val = tensor<int32, [3]>([1, 128, 2])];
+            tensor<bool, [3]> var_1567_end_mask_0 = const()[name = tensor<string, []>("op_1567_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<fp16, [1, 128, 1]> var_1567_cast_fp16 = slice_by_index(begin = var_1567_begin_0, end = var_1567_end_0, end_mask = var_1567_end_mask_0, x = x_11_cast_fp16)[name = tensor<string, []>("op_1567_cast_fp16")];
+            tensor<int32, []> var_1569 = const()[name = tensor<string, []>("op_1569"), val = tensor<int32, []>(2)];
+            tensor<bool, []> x_interleave_0 = const()[name = tensor<string, []>("x_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 128, ?]> x_cast_fp16 = concat(axis = var_1569, interleave = x_interleave_0, values = (var_1567_cast_fp16, x_11_cast_fp16))[name = tensor<string, []>("x_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_165_cast_fp16 = add(x = x_cast_fp16, y = x_source_1)[name = tensor<string, []>("input_165_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_3_adain1_0_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46184384))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46217216))), name = tensor<string, []>("resblocks_3_adain1_0_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_3_adain1_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_3_adain1_0_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46217792)))];
+            tensor<fp16, [1, 256]> linear_28_cast_fp16 = linear(bias = resblocks_3_adain1_0_fc_bias_to_fp16, weight = resblocks_3_adain1_0_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_28_cast_fp16")];
+            tensor<int32, [3]> var_1661 = const()[name = tensor<string, []>("op_1661"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_115_cast_fp16 = reshape(shape = var_1661, x = linear_28_cast_fp16)[name = tensor<string, []>("h_115_cast_fp16")];
+            tensor<int32, [2]> var_1663_split_sizes_0 = const()[name = tensor<string, []>("op_1663_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_1663_axis_0 = const()[name = tensor<string, []>("op_1663_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_1663_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_1663_cast_fp16_1 = split(axis = var_1663_axis_0, split_sizes = var_1663_split_sizes_0, x = h_115_cast_fp16)[name = tensor<string, []>("op_1663_cast_fp16")];
+            tensor<fp16, []> var_1665_promoted_to_fp16 = const()[name = tensor<string, []>("op_1665_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_1666_cast_fp16 = add(x = var_1663_cast_fp16_0, y = var_1665_promoted_to_fp16)[name = tensor<string, []>("op_1666_cast_fp16")];
+            tensor<fp16, [128]> resblocks_3_adain1_0_norm_weight_to_fp16 = const()[name = tensor<string, []>("resblocks_3_adain1_0_norm_weight_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46218368)))];
+            tensor<fp16, [128]> resblocks_3_adain1_0_norm_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_3_adain1_0_norm_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46218688)))];
+            tensor<fp16, []> var_1578_to_fp16 = const()[name = tensor<string, []>("op_1578_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 128, ?]> var_1669_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_1578_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_165_cast_fp16)[name = tensor<string, []>("op_1669_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_1670_cast_fp16 = mul(x = var_1666_cast_fp16, y = var_1669_cast_fp16)[name = tensor<string, []>("op_1670_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_55_cast_fp16 = add(x = var_1670_cast_fp16, y = var_1663_cast_fp16_1)[name = tensor<string, []>("xt_55_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_1673_to_fp16 = const()[name = tensor<string, []>("op_1673_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46219008)))];
+            tensor<fp16, [1, 128, ?]> var_1674_cast_fp16 = mul(x = xt_55_cast_fp16, y = var_1673_to_fp16)[name = tensor<string, []>("op_1674_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_37_cast_fp16 = cos(x = var_1674_cast_fp16)[name = tensor<string, []>("cv_37_cast_fp16")];
+            tensor<fp16, []> var_1676_to_fp16 = const()[name = tensor<string, []>("op_1676_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_1677_cast_fp16 = mul(x = cv_37_cast_fp16, y = var_1676_to_fp16)[name = tensor<string, []>("op_1677_cast_fp16")];
+            tensor<fp16, []> var_1678_to_fp16 = const()[name = tensor<string, []>("op_1678_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_1679_cast_fp16 = add(x = var_1677_cast_fp16, y = var_1678_to_fp16)[name = tensor<string, []>("op_1679_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_1680_to_fp16 = const()[name = tensor<string, []>("op_1680_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46219328)))];
+            tensor<fp16, [1, 128, ?]> var_1683_cast_fp16 = mul(x = var_1679_cast_fp16, y = var_1680_to_fp16)[name = tensor<string, []>("op_1683_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_167_cast_fp16 = add(x = xt_55_cast_fp16, y = var_1683_cast_fp16)[name = tensor<string, []>("input_167_cast_fp16")];
+            tensor<string, []> input_169_pad_type_0 = const()[name = tensor<string, []>("input_169_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_169_pad_0 = const()[name = tensor<string, []>("input_169_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> input_169_strides_0 = const()[name = tensor<string, []>("input_169_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_169_dilations_0 = const()[name = tensor<string, []>("input_169_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_169_groups_0 = const()[name = tensor<string, []>("input_169_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3]> weight_131_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46219648))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46268864))), name = tensor<string, []>("weight_131_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 3])];
+            tensor<fp16, [128]> resblocks_3_convs1_0_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_3_convs1_0_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46269440)))];
+            tensor<fp16, [1, 128, ?]> input_169_cast_fp16 = conv(bias = resblocks_3_convs1_0_bias_to_fp16, dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = weight_131_to_fp16_palettized, x = input_167_cast_fp16)[name = tensor<string, []>("input_169_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_3_adain2_0_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46269760))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46302592))), name = tensor<string, []>("resblocks_3_adain2_0_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_3_adain2_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_3_adain2_0_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46303168)))];
+            tensor<fp16, [1, 256]> linear_29_cast_fp16 = linear(bias = resblocks_3_adain2_0_fc_bias_to_fp16, weight = resblocks_3_adain2_0_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_29_cast_fp16")];
+            tensor<int32, [3]> var_1699 = const()[name = tensor<string, []>("op_1699"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_119_cast_fp16 = reshape(shape = var_1699, x = linear_29_cast_fp16)[name = tensor<string, []>("h_119_cast_fp16")];
+            tensor<int32, [2]> var_1701_split_sizes_0 = const()[name = tensor<string, []>("op_1701_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_1701_axis_0 = const()[name = tensor<string, []>("op_1701_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_1701_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_1701_cast_fp16_1 = split(axis = var_1701_axis_0, split_sizes = var_1701_split_sizes_0, x = h_119_cast_fp16)[name = tensor<string, []>("op_1701_cast_fp16")];
+            tensor<fp16, []> var_1703_promoted_to_fp16 = const()[name = tensor<string, []>("op_1703_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_1704_cast_fp16 = add(x = var_1701_cast_fp16_0, y = var_1703_promoted_to_fp16)[name = tensor<string, []>("op_1704_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_1707_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_1578_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_169_cast_fp16)[name = tensor<string, []>("op_1707_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_1708_cast_fp16 = mul(x = var_1704_cast_fp16, y = var_1707_cast_fp16)[name = tensor<string, []>("op_1708_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_57_cast_fp16 = add(x = var_1708_cast_fp16, y = var_1701_cast_fp16_1)[name = tensor<string, []>("xt_57_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_1711_to_fp16 = const()[name = tensor<string, []>("op_1711_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46303744)))];
+            tensor<fp16, [1, 128, ?]> var_1712_cast_fp16 = mul(x = xt_57_cast_fp16, y = var_1711_to_fp16)[name = tensor<string, []>("op_1712_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_39_cast_fp16 = cos(x = var_1712_cast_fp16)[name = tensor<string, []>("cv_39_cast_fp16")];
+            tensor<fp16, []> var_1714_to_fp16 = const()[name = tensor<string, []>("op_1714_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_1715_cast_fp16 = mul(x = cv_39_cast_fp16, y = var_1714_to_fp16)[name = tensor<string, []>("op_1715_cast_fp16")];
+            tensor<fp16, []> var_1716_to_fp16 = const()[name = tensor<string, []>("op_1716_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_1717_cast_fp16 = add(x = var_1715_cast_fp16, y = var_1716_to_fp16)[name = tensor<string, []>("op_1717_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_1718_to_fp16 = const()[name = tensor<string, []>("op_1718_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46304064)))];
+            tensor<fp16, [1, 128, ?]> var_1721_cast_fp16 = mul(x = var_1717_cast_fp16, y = var_1718_to_fp16)[name = tensor<string, []>("op_1721_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_171_cast_fp16 = add(x = xt_57_cast_fp16, y = var_1721_cast_fp16)[name = tensor<string, []>("input_171_cast_fp16")];
+            tensor<string, []> xt_59_pad_type_0 = const()[name = tensor<string, []>("xt_59_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_59_pad_0 = const()[name = tensor<string, []>("xt_59_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> xt_59_strides_0 = const()[name = tensor<string, []>("xt_59_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_59_dilations_0 = const()[name = tensor<string, []>("xt_59_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_59_groups_0 = const()[name = tensor<string, []>("xt_59_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3]> weight_135_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46304384))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46353600))), name = tensor<string, []>("weight_135_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 3])];
+            tensor<fp16, [128]> resblocks_3_convs2_0_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_3_convs2_0_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46354176)))];
+            tensor<fp16, [1, 128, ?]> xt_59_cast_fp16 = conv(bias = resblocks_3_convs2_0_bias_to_fp16, dilations = xt_59_dilations_0, groups = xt_59_groups_0, pad = xt_59_pad_0, pad_type = xt_59_pad_type_0, strides = xt_59_strides_0, weight = weight_135_to_fp16_palettized, x = input_171_cast_fp16)[name = tensor<string, []>("xt_59_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_173_cast_fp16 = add(x = xt_59_cast_fp16, y = input_165_cast_fp16)[name = tensor<string, []>("input_173_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_3_adain1_1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46354496))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46387328))), name = tensor<string, []>("resblocks_3_adain1_1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_3_adain1_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_3_adain1_1_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46387904)))];
+            tensor<fp16, [1, 256]> linear_30_cast_fp16 = linear(bias = resblocks_3_adain1_1_fc_bias_to_fp16, weight = resblocks_3_adain1_1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_30_cast_fp16")];
+            tensor<int32, [3]> var_1738 = const()[name = tensor<string, []>("op_1738"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_123_cast_fp16 = reshape(shape = var_1738, x = linear_30_cast_fp16)[name = tensor<string, []>("h_123_cast_fp16")];
+            tensor<int32, [2]> var_1740_split_sizes_0 = const()[name = tensor<string, []>("op_1740_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_1740_axis_0 = const()[name = tensor<string, []>("op_1740_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_1740_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_1740_cast_fp16_1 = split(axis = var_1740_axis_0, split_sizes = var_1740_split_sizes_0, x = h_123_cast_fp16)[name = tensor<string, []>("op_1740_cast_fp16")];
+            tensor<fp16, []> var_1742_promoted_to_fp16 = const()[name = tensor<string, []>("op_1742_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_1743_cast_fp16 = add(x = var_1740_cast_fp16_0, y = var_1742_promoted_to_fp16)[name = tensor<string, []>("op_1743_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_1746_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_1578_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_173_cast_fp16)[name = tensor<string, []>("op_1746_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_1747_cast_fp16 = mul(x = var_1743_cast_fp16, y = var_1746_cast_fp16)[name = tensor<string, []>("op_1747_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_61_cast_fp16 = add(x = var_1747_cast_fp16, y = var_1740_cast_fp16_1)[name = tensor<string, []>("xt_61_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_1750_to_fp16 = const()[name = tensor<string, []>("op_1750_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46388480)))];
+            tensor<fp16, [1, 128, ?]> var_1751_cast_fp16 = mul(x = xt_61_cast_fp16, y = var_1750_to_fp16)[name = tensor<string, []>("op_1751_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_41_cast_fp16 = cos(x = var_1751_cast_fp16)[name = tensor<string, []>("cv_41_cast_fp16")];
+            tensor<fp16, []> var_1753_to_fp16 = const()[name = tensor<string, []>("op_1753_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_1754_cast_fp16 = mul(x = cv_41_cast_fp16, y = var_1753_to_fp16)[name = tensor<string, []>("op_1754_cast_fp16")];
+            tensor<fp16, []> var_1755_to_fp16 = const()[name = tensor<string, []>("op_1755_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_1756_cast_fp16 = add(x = var_1754_cast_fp16, y = var_1755_to_fp16)[name = tensor<string, []>("op_1756_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_1757_to_fp16 = const()[name = tensor<string, []>("op_1757_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46388800)))];
+            tensor<fp16, [1, 128, ?]> var_1760_cast_fp16 = mul(x = var_1756_cast_fp16, y = var_1757_to_fp16)[name = tensor<string, []>("op_1760_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_175_cast_fp16 = add(x = xt_61_cast_fp16, y = var_1760_cast_fp16)[name = tensor<string, []>("input_175_cast_fp16")];
+            tensor<string, []> input_177_pad_type_0 = const()[name = tensor<string, []>("input_177_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_177_pad_0 = const()[name = tensor<string, []>("input_177_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> input_177_dilations_0 = const()[name = tensor<string, []>("input_177_dilations_0"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> input_177_strides_0 = const()[name = tensor<string, []>("input_177_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_177_groups_0 = const()[name = tensor<string, []>("input_177_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3]> weight_139_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46389120))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46438336))), name = tensor<string, []>("weight_139_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 3])];
+            tensor<fp16, [128]> resblocks_3_convs1_1_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_3_convs1_1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46438912)))];
+            tensor<fp16, [1, 128, ?]> input_177_cast_fp16 = conv(bias = resblocks_3_convs1_1_bias_to_fp16, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = weight_139_to_fp16_palettized, x = input_175_cast_fp16)[name = tensor<string, []>("input_177_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_3_adain2_1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46439232))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46472064))), name = tensor<string, []>("resblocks_3_adain2_1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_3_adain2_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_3_adain2_1_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46472640)))];
+            tensor<fp16, [1, 256]> linear_31_cast_fp16 = linear(bias = resblocks_3_adain2_1_fc_bias_to_fp16, weight = resblocks_3_adain2_1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_31_cast_fp16")];
+            tensor<int32, [3]> var_1776 = const()[name = tensor<string, []>("op_1776"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_127_cast_fp16 = reshape(shape = var_1776, x = linear_31_cast_fp16)[name = tensor<string, []>("h_127_cast_fp16")];
+            tensor<int32, [2]> var_1778_split_sizes_0 = const()[name = tensor<string, []>("op_1778_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_1778_axis_0 = const()[name = tensor<string, []>("op_1778_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_1778_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_1778_cast_fp16_1 = split(axis = var_1778_axis_0, split_sizes = var_1778_split_sizes_0, x = h_127_cast_fp16)[name = tensor<string, []>("op_1778_cast_fp16")];
+            tensor<fp16, []> var_1780_promoted_to_fp16 = const()[name = tensor<string, []>("op_1780_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_1781_cast_fp16 = add(x = var_1778_cast_fp16_0, y = var_1780_promoted_to_fp16)[name = tensor<string, []>("op_1781_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_1784_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_1578_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_177_cast_fp16)[name = tensor<string, []>("op_1784_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_1785_cast_fp16 = mul(x = var_1781_cast_fp16, y = var_1784_cast_fp16)[name = tensor<string, []>("op_1785_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_63_cast_fp16 = add(x = var_1785_cast_fp16, y = var_1778_cast_fp16_1)[name = tensor<string, []>("xt_63_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_1788_to_fp16 = const()[name = tensor<string, []>("op_1788_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46473216)))];
+            tensor<fp16, [1, 128, ?]> var_1789_cast_fp16 = mul(x = xt_63_cast_fp16, y = var_1788_to_fp16)[name = tensor<string, []>("op_1789_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_43_cast_fp16 = cos(x = var_1789_cast_fp16)[name = tensor<string, []>("cv_43_cast_fp16")];
+            tensor<fp16, []> var_1791_to_fp16 = const()[name = tensor<string, []>("op_1791_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_1792_cast_fp16 = mul(x = cv_43_cast_fp16, y = var_1791_to_fp16)[name = tensor<string, []>("op_1792_cast_fp16")];
+            tensor<fp16, []> var_1793_to_fp16 = const()[name = tensor<string, []>("op_1793_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_1794_cast_fp16 = add(x = var_1792_cast_fp16, y = var_1793_to_fp16)[name = tensor<string, []>("op_1794_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_1795_to_fp16 = const()[name = tensor<string, []>("op_1795_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46473536)))];
+            tensor<fp16, [1, 128, ?]> var_1798_cast_fp16 = mul(x = var_1794_cast_fp16, y = var_1795_to_fp16)[name = tensor<string, []>("op_1798_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_179_cast_fp16 = add(x = xt_63_cast_fp16, y = var_1798_cast_fp16)[name = tensor<string, []>("input_179_cast_fp16")];
+            tensor<string, []> xt_65_pad_type_0 = const()[name = tensor<string, []>("xt_65_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_65_pad_0 = const()[name = tensor<string, []>("xt_65_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> xt_65_strides_0 = const()[name = tensor<string, []>("xt_65_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_65_dilations_0 = const()[name = tensor<string, []>("xt_65_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_65_groups_0 = const()[name = tensor<string, []>("xt_65_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3]> weight_143_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46473856))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46523072))), name = tensor<string, []>("weight_143_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 3])];
+            tensor<fp16, [128]> resblocks_3_convs2_1_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_3_convs2_1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46523648)))];
+            tensor<fp16, [1, 128, ?]> xt_65_cast_fp16 = conv(bias = resblocks_3_convs2_1_bias_to_fp16, dilations = xt_65_dilations_0, groups = xt_65_groups_0, pad = xt_65_pad_0, pad_type = xt_65_pad_type_0, strides = xt_65_strides_0, weight = weight_143_to_fp16_palettized, x = input_179_cast_fp16)[name = tensor<string, []>("xt_65_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_181_cast_fp16 = add(x = xt_65_cast_fp16, y = input_173_cast_fp16)[name = tensor<string, []>("input_181_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_3_adain1_2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46523968))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46556800))), name = tensor<string, []>("resblocks_3_adain1_2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_3_adain1_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_3_adain1_2_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46557376)))];
+            tensor<fp16, [1, 256]> linear_32_cast_fp16 = linear(bias = resblocks_3_adain1_2_fc_bias_to_fp16, weight = resblocks_3_adain1_2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_32_cast_fp16")];
+            tensor<int32, [3]> var_1815 = const()[name = tensor<string, []>("op_1815"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_131_cast_fp16 = reshape(shape = var_1815, x = linear_32_cast_fp16)[name = tensor<string, []>("h_131_cast_fp16")];
+            tensor<int32, [2]> var_1817_split_sizes_0 = const()[name = tensor<string, []>("op_1817_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_1817_axis_0 = const()[name = tensor<string, []>("op_1817_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_1817_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_1817_cast_fp16_1 = split(axis = var_1817_axis_0, split_sizes = var_1817_split_sizes_0, x = h_131_cast_fp16)[name = tensor<string, []>("op_1817_cast_fp16")];
+            tensor<fp16, []> var_1819_promoted_to_fp16 = const()[name = tensor<string, []>("op_1819_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_1820_cast_fp16 = add(x = var_1817_cast_fp16_0, y = var_1819_promoted_to_fp16)[name = tensor<string, []>("op_1820_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_1823_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_1578_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_181_cast_fp16)[name = tensor<string, []>("op_1823_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_1824_cast_fp16 = mul(x = var_1820_cast_fp16, y = var_1823_cast_fp16)[name = tensor<string, []>("op_1824_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_67_cast_fp16 = add(x = var_1824_cast_fp16, y = var_1817_cast_fp16_1)[name = tensor<string, []>("xt_67_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_1827_to_fp16 = const()[name = tensor<string, []>("op_1827_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46557952)))];
+            tensor<fp16, [1, 128, ?]> var_1828_cast_fp16 = mul(x = xt_67_cast_fp16, y = var_1827_to_fp16)[name = tensor<string, []>("op_1828_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_45_cast_fp16 = cos(x = var_1828_cast_fp16)[name = tensor<string, []>("cv_45_cast_fp16")];
+            tensor<fp16, []> var_1830_to_fp16 = const()[name = tensor<string, []>("op_1830_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_1831_cast_fp16 = mul(x = cv_45_cast_fp16, y = var_1830_to_fp16)[name = tensor<string, []>("op_1831_cast_fp16")];
+            tensor<fp16, []> var_1832_to_fp16 = const()[name = tensor<string, []>("op_1832_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_1833_cast_fp16 = add(x = var_1831_cast_fp16, y = var_1832_to_fp16)[name = tensor<string, []>("op_1833_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_1834_to_fp16 = const()[name = tensor<string, []>("op_1834_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46558272)))];
+            tensor<fp16, [1, 128, ?]> var_1837_cast_fp16 = mul(x = var_1833_cast_fp16, y = var_1834_to_fp16)[name = tensor<string, []>("op_1837_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_183_cast_fp16 = add(x = xt_67_cast_fp16, y = var_1837_cast_fp16)[name = tensor<string, []>("input_183_cast_fp16")];
+            tensor<string, []> input_185_pad_type_0 = const()[name = tensor<string, []>("input_185_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_185_pad_0 = const()[name = tensor<string, []>("input_185_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> input_185_dilations_0 = const()[name = tensor<string, []>("input_185_dilations_0"), val = tensor<int32, [1]>([5])];
+            tensor<int32, [1]> input_185_strides_0 = const()[name = tensor<string, []>("input_185_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_185_groups_0 = const()[name = tensor<string, []>("input_185_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3]> weight_147_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46558592))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46607808))), name = tensor<string, []>("weight_147_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 3])];
+            tensor<fp16, [128]> resblocks_3_convs1_2_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_3_convs1_2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46608384)))];
+            tensor<fp16, [1, 128, ?]> input_185_cast_fp16 = conv(bias = resblocks_3_convs1_2_bias_to_fp16, dilations = input_185_dilations_0, groups = input_185_groups_0, pad = input_185_pad_0, pad_type = input_185_pad_type_0, strides = input_185_strides_0, weight = weight_147_to_fp16_palettized, x = input_183_cast_fp16)[name = tensor<string, []>("input_185_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_3_adain2_2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46608704))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46641536))), name = tensor<string, []>("resblocks_3_adain2_2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_3_adain2_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_3_adain2_2_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46642112)))];
+            tensor<fp16, [1, 256]> linear_33_cast_fp16 = linear(bias = resblocks_3_adain2_2_fc_bias_to_fp16, weight = resblocks_3_adain2_2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_33_cast_fp16")];
+            tensor<int32, [3]> var_1853 = const()[name = tensor<string, []>("op_1853"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_135_cast_fp16 = reshape(shape = var_1853, x = linear_33_cast_fp16)[name = tensor<string, []>("h_135_cast_fp16")];
+            tensor<int32, [2]> var_1855_split_sizes_0 = const()[name = tensor<string, []>("op_1855_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_1855_axis_0 = const()[name = tensor<string, []>("op_1855_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_1855_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_1855_cast_fp16_1 = split(axis = var_1855_axis_0, split_sizes = var_1855_split_sizes_0, x = h_135_cast_fp16)[name = tensor<string, []>("op_1855_cast_fp16")];
+            tensor<fp16, []> var_1857_promoted_to_fp16 = const()[name = tensor<string, []>("op_1857_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_1858_cast_fp16 = add(x = var_1855_cast_fp16_0, y = var_1857_promoted_to_fp16)[name = tensor<string, []>("op_1858_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_1861_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_1578_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_185_cast_fp16)[name = tensor<string, []>("op_1861_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_1862_cast_fp16 = mul(x = var_1858_cast_fp16, y = var_1861_cast_fp16)[name = tensor<string, []>("op_1862_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_69_cast_fp16 = add(x = var_1862_cast_fp16, y = var_1855_cast_fp16_1)[name = tensor<string, []>("xt_69_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_1865_to_fp16 = const()[name = tensor<string, []>("op_1865_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46642688)))];
+            tensor<fp16, [1, 128, ?]> var_1866_cast_fp16 = mul(x = xt_69_cast_fp16, y = var_1865_to_fp16)[name = tensor<string, []>("op_1866_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_47_cast_fp16 = cos(x = var_1866_cast_fp16)[name = tensor<string, []>("cv_47_cast_fp16")];
+            tensor<fp16, []> var_1868_to_fp16 = const()[name = tensor<string, []>("op_1868_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_1869_cast_fp16 = mul(x = cv_47_cast_fp16, y = var_1868_to_fp16)[name = tensor<string, []>("op_1869_cast_fp16")];
+            tensor<fp16, []> var_1870_to_fp16 = const()[name = tensor<string, []>("op_1870_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_1871_cast_fp16 = add(x = var_1869_cast_fp16, y = var_1870_to_fp16)[name = tensor<string, []>("op_1871_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_1872_to_fp16 = const()[name = tensor<string, []>("op_1872_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46643008)))];
+            tensor<fp16, [1, 128, ?]> var_1875_cast_fp16 = mul(x = var_1871_cast_fp16, y = var_1872_to_fp16)[name = tensor<string, []>("op_1875_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_187_cast_fp16 = add(x = xt_69_cast_fp16, y = var_1875_cast_fp16)[name = tensor<string, []>("input_187_cast_fp16")];
+            tensor<string, []> xt_71_pad_type_0 = const()[name = tensor<string, []>("xt_71_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_71_pad_0 = const()[name = tensor<string, []>("xt_71_pad_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [1]> xt_71_strides_0 = const()[name = tensor<string, []>("xt_71_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_71_dilations_0 = const()[name = tensor<string, []>("xt_71_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_71_groups_0 = const()[name = tensor<string, []>("xt_71_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 3]> weight_151_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [49152]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46643328))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46692544))), name = tensor<string, []>("weight_151_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 3])];
+            tensor<fp16, [128]> resblocks_3_convs2_2_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_3_convs2_2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46693120)))];
+            tensor<fp16, [1, 128, ?]> xt_71_cast_fp16 = conv(bias = resblocks_3_convs2_2_bias_to_fp16, dilations = xt_71_dilations_0, groups = xt_71_groups_0, pad = xt_71_pad_0, pad_type = xt_71_pad_type_0, strides = xt_71_strides_0, weight = weight_151_to_fp16_palettized, x = input_187_cast_fp16)[name = tensor<string, []>("xt_71_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xs_7_cast_fp16 = add(x = xt_71_cast_fp16, y = input_181_cast_fp16)[name = tensor<string, []>("xs_7_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_4_adain1_0_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46693440))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46726272))), name = tensor<string, []>("resblocks_4_adain1_0_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_4_adain1_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_4_adain1_0_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46726848)))];
+            tensor<fp16, [1, 256]> linear_34_cast_fp16 = linear(bias = resblocks_4_adain1_0_fc_bias_to_fp16, weight = resblocks_4_adain1_0_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_34_cast_fp16")];
+            tensor<int32, [3]> var_1975 = const()[name = tensor<string, []>("op_1975"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_139_cast_fp16 = reshape(shape = var_1975, x = linear_34_cast_fp16)[name = tensor<string, []>("h_139_cast_fp16")];
+            tensor<int32, [2]> var_1977_split_sizes_0 = const()[name = tensor<string, []>("op_1977_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_1977_axis_0 = const()[name = tensor<string, []>("op_1977_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_1977_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_1977_cast_fp16_1 = split(axis = var_1977_axis_0, split_sizes = var_1977_split_sizes_0, x = h_139_cast_fp16)[name = tensor<string, []>("op_1977_cast_fp16")];
+            tensor<fp16, []> var_1979_promoted_to_fp16 = const()[name = tensor<string, []>("op_1979_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_1980_cast_fp16 = add(x = var_1977_cast_fp16_0, y = var_1979_promoted_to_fp16)[name = tensor<string, []>("op_1980_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_1984_cast_fp16 = mul(x = var_1980_cast_fp16, y = var_1669_cast_fp16)[name = tensor<string, []>("op_1984_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_73_cast_fp16 = add(x = var_1984_cast_fp16, y = var_1977_cast_fp16_1)[name = tensor<string, []>("xt_73_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_1987_to_fp16 = const()[name = tensor<string, []>("op_1987_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46727424)))];
+            tensor<fp16, [1, 128, ?]> var_1988_cast_fp16 = mul(x = xt_73_cast_fp16, y = var_1987_to_fp16)[name = tensor<string, []>("op_1988_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_49_cast_fp16 = cos(x = var_1988_cast_fp16)[name = tensor<string, []>("cv_49_cast_fp16")];
+            tensor<fp16, []> var_1990_to_fp16 = const()[name = tensor<string, []>("op_1990_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_1991_cast_fp16 = mul(x = cv_49_cast_fp16, y = var_1990_to_fp16)[name = tensor<string, []>("op_1991_cast_fp16")];
+            tensor<fp16, []> var_1992_to_fp16 = const()[name = tensor<string, []>("op_1992_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_1993_cast_fp16 = add(x = var_1991_cast_fp16, y = var_1992_to_fp16)[name = tensor<string, []>("op_1993_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_1994_to_fp16 = const()[name = tensor<string, []>("op_1994_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46727744)))];
+            tensor<fp16, [1, 128, ?]> var_1997_cast_fp16 = mul(x = var_1993_cast_fp16, y = var_1994_to_fp16)[name = tensor<string, []>("op_1997_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_189_cast_fp16 = add(x = xt_73_cast_fp16, y = var_1997_cast_fp16)[name = tensor<string, []>("input_189_cast_fp16")];
+            tensor<string, []> input_191_pad_type_0 = const()[name = tensor<string, []>("input_191_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_191_pad_0 = const()[name = tensor<string, []>("input_191_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> input_191_strides_0 = const()[name = tensor<string, []>("input_191_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_191_dilations_0 = const()[name = tensor<string, []>("input_191_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_191_groups_0 = const()[name = tensor<string, []>("input_191_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 7]> weight_155_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [114688]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46728064))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46842816))), name = tensor<string, []>("weight_155_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 7])];
+            tensor<fp16, [128]> resblocks_4_convs1_0_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_4_convs1_0_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46843392)))];
+            tensor<fp16, [1, 128, ?]> input_191_cast_fp16 = conv(bias = resblocks_4_convs1_0_bias_to_fp16, dilations = input_191_dilations_0, groups = input_191_groups_0, pad = input_191_pad_0, pad_type = input_191_pad_type_0, strides = input_191_strides_0, weight = weight_155_to_fp16_palettized, x = input_189_cast_fp16)[name = tensor<string, []>("input_191_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_4_adain2_0_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46843712))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46876544))), name = tensor<string, []>("resblocks_4_adain2_0_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_4_adain2_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_4_adain2_0_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46877120)))];
+            tensor<fp16, [1, 256]> linear_35_cast_fp16 = linear(bias = resblocks_4_adain2_0_fc_bias_to_fp16, weight = resblocks_4_adain2_0_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_35_cast_fp16")];
+            tensor<int32, [3]> var_2013 = const()[name = tensor<string, []>("op_2013"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_143_cast_fp16 = reshape(shape = var_2013, x = linear_35_cast_fp16)[name = tensor<string, []>("h_143_cast_fp16")];
+            tensor<int32, [2]> var_2015_split_sizes_0 = const()[name = tensor<string, []>("op_2015_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_2015_axis_0 = const()[name = tensor<string, []>("op_2015_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_2015_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_2015_cast_fp16_1 = split(axis = var_2015_axis_0, split_sizes = var_2015_split_sizes_0, x = h_143_cast_fp16)[name = tensor<string, []>("op_2015_cast_fp16")];
+            tensor<fp16, []> var_2017_promoted_to_fp16 = const()[name = tensor<string, []>("op_2017_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_2018_cast_fp16 = add(x = var_2015_cast_fp16_0, y = var_2017_promoted_to_fp16)[name = tensor<string, []>("op_2018_cast_fp16")];
+            tensor<fp16, []> var_1892_to_fp16 = const()[name = tensor<string, []>("op_1892_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 128, ?]> var_2021_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_1892_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_191_cast_fp16)[name = tensor<string, []>("op_2021_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2022_cast_fp16 = mul(x = var_2018_cast_fp16, y = var_2021_cast_fp16)[name = tensor<string, []>("op_2022_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_75_cast_fp16 = add(x = var_2022_cast_fp16, y = var_2015_cast_fp16_1)[name = tensor<string, []>("xt_75_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2025_to_fp16 = const()[name = tensor<string, []>("op_2025_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46877696)))];
+            tensor<fp16, [1, 128, ?]> var_2026_cast_fp16 = mul(x = xt_75_cast_fp16, y = var_2025_to_fp16)[name = tensor<string, []>("op_2026_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_51_cast_fp16 = cos(x = var_2026_cast_fp16)[name = tensor<string, []>("cv_51_cast_fp16")];
+            tensor<fp16, []> var_2028_to_fp16 = const()[name = tensor<string, []>("op_2028_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2029_cast_fp16 = mul(x = cv_51_cast_fp16, y = var_2028_to_fp16)[name = tensor<string, []>("op_2029_cast_fp16")];
+            tensor<fp16, []> var_2030_to_fp16 = const()[name = tensor<string, []>("op_2030_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2031_cast_fp16 = add(x = var_2029_cast_fp16, y = var_2030_to_fp16)[name = tensor<string, []>("op_2031_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2032_to_fp16 = const()[name = tensor<string, []>("op_2032_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46878016)))];
+            tensor<fp16, [1, 128, ?]> var_2035_cast_fp16 = mul(x = var_2031_cast_fp16, y = var_2032_to_fp16)[name = tensor<string, []>("op_2035_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_193_cast_fp16 = add(x = xt_75_cast_fp16, y = var_2035_cast_fp16)[name = tensor<string, []>("input_193_cast_fp16")];
+            tensor<string, []> xt_77_pad_type_0 = const()[name = tensor<string, []>("xt_77_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_77_pad_0 = const()[name = tensor<string, []>("xt_77_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> xt_77_strides_0 = const()[name = tensor<string, []>("xt_77_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_77_dilations_0 = const()[name = tensor<string, []>("xt_77_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_77_groups_0 = const()[name = tensor<string, []>("xt_77_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 7]> weight_159_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [114688]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46878336))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46993088))), name = tensor<string, []>("weight_159_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 7])];
+            tensor<fp16, [128]> resblocks_4_convs2_0_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_4_convs2_0_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46993664)))];
+            tensor<fp16, [1, 128, ?]> xt_77_cast_fp16 = conv(bias = resblocks_4_convs2_0_bias_to_fp16, dilations = xt_77_dilations_0, groups = xt_77_groups_0, pad = xt_77_pad_0, pad_type = xt_77_pad_type_0, strides = xt_77_strides_0, weight = weight_159_to_fp16_palettized, x = input_193_cast_fp16)[name = tensor<string, []>("xt_77_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_195_cast_fp16 = add(x = xt_77_cast_fp16, y = input_165_cast_fp16)[name = tensor<string, []>("input_195_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_4_adain1_1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46993984))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47026816))), name = tensor<string, []>("resblocks_4_adain1_1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_4_adain1_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_4_adain1_1_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47027392)))];
+            tensor<fp16, [1, 256]> linear_36_cast_fp16 = linear(bias = resblocks_4_adain1_1_fc_bias_to_fp16, weight = resblocks_4_adain1_1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_36_cast_fp16")];
+            tensor<int32, [3]> var_2052 = const()[name = tensor<string, []>("op_2052"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_147_cast_fp16 = reshape(shape = var_2052, x = linear_36_cast_fp16)[name = tensor<string, []>("h_147_cast_fp16")];
+            tensor<int32, [2]> var_2054_split_sizes_0 = const()[name = tensor<string, []>("op_2054_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_2054_axis_0 = const()[name = tensor<string, []>("op_2054_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_2054_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_2054_cast_fp16_1 = split(axis = var_2054_axis_0, split_sizes = var_2054_split_sizes_0, x = h_147_cast_fp16)[name = tensor<string, []>("op_2054_cast_fp16")];
+            tensor<fp16, []> var_2056_promoted_to_fp16 = const()[name = tensor<string, []>("op_2056_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_2057_cast_fp16 = add(x = var_2054_cast_fp16_0, y = var_2056_promoted_to_fp16)[name = tensor<string, []>("op_2057_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2060_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_1892_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_195_cast_fp16)[name = tensor<string, []>("op_2060_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2061_cast_fp16 = mul(x = var_2057_cast_fp16, y = var_2060_cast_fp16)[name = tensor<string, []>("op_2061_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_79_cast_fp16 = add(x = var_2061_cast_fp16, y = var_2054_cast_fp16_1)[name = tensor<string, []>("xt_79_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2064_to_fp16 = const()[name = tensor<string, []>("op_2064_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47027968)))];
+            tensor<fp16, [1, 128, ?]> var_2065_cast_fp16 = mul(x = xt_79_cast_fp16, y = var_2064_to_fp16)[name = tensor<string, []>("op_2065_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_53_cast_fp16 = cos(x = var_2065_cast_fp16)[name = tensor<string, []>("cv_53_cast_fp16")];
+            tensor<fp16, []> var_2067_to_fp16 = const()[name = tensor<string, []>("op_2067_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2068_cast_fp16 = mul(x = cv_53_cast_fp16, y = var_2067_to_fp16)[name = tensor<string, []>("op_2068_cast_fp16")];
+            tensor<fp16, []> var_2069_to_fp16 = const()[name = tensor<string, []>("op_2069_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2070_cast_fp16 = add(x = var_2068_cast_fp16, y = var_2069_to_fp16)[name = tensor<string, []>("op_2070_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2071_to_fp16 = const()[name = tensor<string, []>("op_2071_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47028288)))];
+            tensor<fp16, [1, 128, ?]> var_2074_cast_fp16 = mul(x = var_2070_cast_fp16, y = var_2071_to_fp16)[name = tensor<string, []>("op_2074_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_197_cast_fp16 = add(x = xt_79_cast_fp16, y = var_2074_cast_fp16)[name = tensor<string, []>("input_197_cast_fp16")];
+            tensor<string, []> input_199_pad_type_0 = const()[name = tensor<string, []>("input_199_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_199_pad_0 = const()[name = tensor<string, []>("input_199_pad_0"), val = tensor<int32, [2]>([9, 9])];
+            tensor<int32, [1]> input_199_dilations_0 = const()[name = tensor<string, []>("input_199_dilations_0"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> input_199_strides_0 = const()[name = tensor<string, []>("input_199_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_199_groups_0 = const()[name = tensor<string, []>("input_199_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 7]> weight_163_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [114688]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47028608))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47143360))), name = tensor<string, []>("weight_163_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 7])];
+            tensor<fp16, [128]> resblocks_4_convs1_1_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_4_convs1_1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47143936)))];
+            tensor<fp16, [1, 128, ?]> input_199_cast_fp16 = conv(bias = resblocks_4_convs1_1_bias_to_fp16, dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = weight_163_to_fp16_palettized, x = input_197_cast_fp16)[name = tensor<string, []>("input_199_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_4_adain2_1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47144256))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47177088))), name = tensor<string, []>("resblocks_4_adain2_1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_4_adain2_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_4_adain2_1_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47177664)))];
+            tensor<fp16, [1, 256]> linear_37_cast_fp16 = linear(bias = resblocks_4_adain2_1_fc_bias_to_fp16, weight = resblocks_4_adain2_1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_37_cast_fp16")];
+            tensor<int32, [3]> var_2090 = const()[name = tensor<string, []>("op_2090"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_151_cast_fp16 = reshape(shape = var_2090, x = linear_37_cast_fp16)[name = tensor<string, []>("h_151_cast_fp16")];
+            tensor<int32, [2]> var_2092_split_sizes_0 = const()[name = tensor<string, []>("op_2092_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_2092_axis_0 = const()[name = tensor<string, []>("op_2092_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_2092_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_2092_cast_fp16_1 = split(axis = var_2092_axis_0, split_sizes = var_2092_split_sizes_0, x = h_151_cast_fp16)[name = tensor<string, []>("op_2092_cast_fp16")];
+            tensor<fp16, []> var_2094_promoted_to_fp16 = const()[name = tensor<string, []>("op_2094_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_2095_cast_fp16 = add(x = var_2092_cast_fp16_0, y = var_2094_promoted_to_fp16)[name = tensor<string, []>("op_2095_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2098_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_1892_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_199_cast_fp16)[name = tensor<string, []>("op_2098_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2099_cast_fp16 = mul(x = var_2095_cast_fp16, y = var_2098_cast_fp16)[name = tensor<string, []>("op_2099_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_81_cast_fp16 = add(x = var_2099_cast_fp16, y = var_2092_cast_fp16_1)[name = tensor<string, []>("xt_81_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2102_to_fp16 = const()[name = tensor<string, []>("op_2102_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47178240)))];
+            tensor<fp16, [1, 128, ?]> var_2103_cast_fp16 = mul(x = xt_81_cast_fp16, y = var_2102_to_fp16)[name = tensor<string, []>("op_2103_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_55_cast_fp16 = cos(x = var_2103_cast_fp16)[name = tensor<string, []>("cv_55_cast_fp16")];
+            tensor<fp16, []> var_2105_to_fp16 = const()[name = tensor<string, []>("op_2105_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2106_cast_fp16 = mul(x = cv_55_cast_fp16, y = var_2105_to_fp16)[name = tensor<string, []>("op_2106_cast_fp16")];
+            tensor<fp16, []> var_2107_to_fp16 = const()[name = tensor<string, []>("op_2107_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2108_cast_fp16 = add(x = var_2106_cast_fp16, y = var_2107_to_fp16)[name = tensor<string, []>("op_2108_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2109_to_fp16 = const()[name = tensor<string, []>("op_2109_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47178560)))];
+            tensor<fp16, [1, 128, ?]> var_2112_cast_fp16 = mul(x = var_2108_cast_fp16, y = var_2109_to_fp16)[name = tensor<string, []>("op_2112_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_201_cast_fp16 = add(x = xt_81_cast_fp16, y = var_2112_cast_fp16)[name = tensor<string, []>("input_201_cast_fp16")];
+            tensor<string, []> xt_83_pad_type_0 = const()[name = tensor<string, []>("xt_83_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_83_pad_0 = const()[name = tensor<string, []>("xt_83_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> xt_83_strides_0 = const()[name = tensor<string, []>("xt_83_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_83_dilations_0 = const()[name = tensor<string, []>("xt_83_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_83_groups_0 = const()[name = tensor<string, []>("xt_83_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 7]> weight_167_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [114688]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47178880))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47293632))), name = tensor<string, []>("weight_167_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 7])];
+            tensor<fp16, [128]> resblocks_4_convs2_1_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_4_convs2_1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47294208)))];
+            tensor<fp16, [1, 128, ?]> xt_83_cast_fp16 = conv(bias = resblocks_4_convs2_1_bias_to_fp16, dilations = xt_83_dilations_0, groups = xt_83_groups_0, pad = xt_83_pad_0, pad_type = xt_83_pad_type_0, strides = xt_83_strides_0, weight = weight_167_to_fp16_palettized, x = input_201_cast_fp16)[name = tensor<string, []>("xt_83_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_203_cast_fp16 = add(x = xt_83_cast_fp16, y = input_195_cast_fp16)[name = tensor<string, []>("input_203_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_4_adain1_2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47294528))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47327360))), name = tensor<string, []>("resblocks_4_adain1_2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_4_adain1_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_4_adain1_2_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47327936)))];
+            tensor<fp16, [1, 256]> linear_38_cast_fp16 = linear(bias = resblocks_4_adain1_2_fc_bias_to_fp16, weight = resblocks_4_adain1_2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_38_cast_fp16")];
+            tensor<int32, [3]> var_2129 = const()[name = tensor<string, []>("op_2129"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_155_cast_fp16 = reshape(shape = var_2129, x = linear_38_cast_fp16)[name = tensor<string, []>("h_155_cast_fp16")];
+            tensor<int32, [2]> var_2131_split_sizes_0 = const()[name = tensor<string, []>("op_2131_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_2131_axis_0 = const()[name = tensor<string, []>("op_2131_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_2131_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_2131_cast_fp16_1 = split(axis = var_2131_axis_0, split_sizes = var_2131_split_sizes_0, x = h_155_cast_fp16)[name = tensor<string, []>("op_2131_cast_fp16")];
+            tensor<fp16, []> var_2133_promoted_to_fp16 = const()[name = tensor<string, []>("op_2133_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_2134_cast_fp16 = add(x = var_2131_cast_fp16_0, y = var_2133_promoted_to_fp16)[name = tensor<string, []>("op_2134_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2137_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_1892_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_203_cast_fp16)[name = tensor<string, []>("op_2137_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2138_cast_fp16 = mul(x = var_2134_cast_fp16, y = var_2137_cast_fp16)[name = tensor<string, []>("op_2138_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_85_cast_fp16 = add(x = var_2138_cast_fp16, y = var_2131_cast_fp16_1)[name = tensor<string, []>("xt_85_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2141_to_fp16 = const()[name = tensor<string, []>("op_2141_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47328512)))];
+            tensor<fp16, [1, 128, ?]> var_2142_cast_fp16 = mul(x = xt_85_cast_fp16, y = var_2141_to_fp16)[name = tensor<string, []>("op_2142_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_57_cast_fp16 = cos(x = var_2142_cast_fp16)[name = tensor<string, []>("cv_57_cast_fp16")];
+            tensor<fp16, []> var_2144_to_fp16 = const()[name = tensor<string, []>("op_2144_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2145_cast_fp16 = mul(x = cv_57_cast_fp16, y = var_2144_to_fp16)[name = tensor<string, []>("op_2145_cast_fp16")];
+            tensor<fp16, []> var_2146_to_fp16 = const()[name = tensor<string, []>("op_2146_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2147_cast_fp16 = add(x = var_2145_cast_fp16, y = var_2146_to_fp16)[name = tensor<string, []>("op_2147_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2148_to_fp16 = const()[name = tensor<string, []>("op_2148_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47328832)))];
+            tensor<fp16, [1, 128, ?]> var_2151_cast_fp16 = mul(x = var_2147_cast_fp16, y = var_2148_to_fp16)[name = tensor<string, []>("op_2151_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_205_cast_fp16 = add(x = xt_85_cast_fp16, y = var_2151_cast_fp16)[name = tensor<string, []>("input_205_cast_fp16")];
+            tensor<string, []> input_207_pad_type_0 = const()[name = tensor<string, []>("input_207_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_207_pad_0 = const()[name = tensor<string, []>("input_207_pad_0"), val = tensor<int32, [2]>([15, 15])];
+            tensor<int32, [1]> input_207_dilations_0 = const()[name = tensor<string, []>("input_207_dilations_0"), val = tensor<int32, [1]>([5])];
+            tensor<int32, [1]> input_207_strides_0 = const()[name = tensor<string, []>("input_207_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_207_groups_0 = const()[name = tensor<string, []>("input_207_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 7]> weight_171_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [114688]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47329152))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47443904))), name = tensor<string, []>("weight_171_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 7])];
+            tensor<fp16, [128]> resblocks_4_convs1_2_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_4_convs1_2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47444480)))];
+            tensor<fp16, [1, 128, ?]> input_207_cast_fp16 = conv(bias = resblocks_4_convs1_2_bias_to_fp16, dilations = input_207_dilations_0, groups = input_207_groups_0, pad = input_207_pad_0, pad_type = input_207_pad_type_0, strides = input_207_strides_0, weight = weight_171_to_fp16_palettized, x = input_205_cast_fp16)[name = tensor<string, []>("input_207_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_4_adain2_2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47444800))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47477632))), name = tensor<string, []>("resblocks_4_adain2_2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_4_adain2_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_4_adain2_2_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47478208)))];
+            tensor<fp16, [1, 256]> linear_39_cast_fp16 = linear(bias = resblocks_4_adain2_2_fc_bias_to_fp16, weight = resblocks_4_adain2_2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_39_cast_fp16")];
+            tensor<int32, [3]> var_2167 = const()[name = tensor<string, []>("op_2167"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_159_cast_fp16 = reshape(shape = var_2167, x = linear_39_cast_fp16)[name = tensor<string, []>("h_159_cast_fp16")];
+            tensor<int32, [2]> var_2169_split_sizes_0 = const()[name = tensor<string, []>("op_2169_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_2169_axis_0 = const()[name = tensor<string, []>("op_2169_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_2169_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_2169_cast_fp16_1 = split(axis = var_2169_axis_0, split_sizes = var_2169_split_sizes_0, x = h_159_cast_fp16)[name = tensor<string, []>("op_2169_cast_fp16")];
+            tensor<fp16, []> var_2171_promoted_to_fp16 = const()[name = tensor<string, []>("op_2171_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_2172_cast_fp16 = add(x = var_2169_cast_fp16_0, y = var_2171_promoted_to_fp16)[name = tensor<string, []>("op_2172_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2175_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_1892_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_207_cast_fp16)[name = tensor<string, []>("op_2175_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2176_cast_fp16 = mul(x = var_2172_cast_fp16, y = var_2175_cast_fp16)[name = tensor<string, []>("op_2176_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_87_cast_fp16 = add(x = var_2176_cast_fp16, y = var_2169_cast_fp16_1)[name = tensor<string, []>("xt_87_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2179_to_fp16 = const()[name = tensor<string, []>("op_2179_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47478784)))];
+            tensor<fp16, [1, 128, ?]> var_2180_cast_fp16 = mul(x = xt_87_cast_fp16, y = var_2179_to_fp16)[name = tensor<string, []>("op_2180_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_59_cast_fp16 = cos(x = var_2180_cast_fp16)[name = tensor<string, []>("cv_59_cast_fp16")];
+            tensor<fp16, []> var_2182_to_fp16 = const()[name = tensor<string, []>("op_2182_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2183_cast_fp16 = mul(x = cv_59_cast_fp16, y = var_2182_to_fp16)[name = tensor<string, []>("op_2183_cast_fp16")];
+            tensor<fp16, []> var_2184_to_fp16 = const()[name = tensor<string, []>("op_2184_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2185_cast_fp16 = add(x = var_2183_cast_fp16, y = var_2184_to_fp16)[name = tensor<string, []>("op_2185_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2186_to_fp16 = const()[name = tensor<string, []>("op_2186_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47479104)))];
+            tensor<fp16, [1, 128, ?]> var_2189_cast_fp16 = mul(x = var_2185_cast_fp16, y = var_2186_to_fp16)[name = tensor<string, []>("op_2189_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_209_cast_fp16 = add(x = xt_87_cast_fp16, y = var_2189_cast_fp16)[name = tensor<string, []>("input_209_cast_fp16")];
+            tensor<string, []> xt_89_pad_type_0 = const()[name = tensor<string, []>("xt_89_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_89_pad_0 = const()[name = tensor<string, []>("xt_89_pad_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [1]> xt_89_strides_0 = const()[name = tensor<string, []>("xt_89_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_89_dilations_0 = const()[name = tensor<string, []>("xt_89_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_89_groups_0 = const()[name = tensor<string, []>("xt_89_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 7]> weight_175_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [114688]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47479424))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47594176))), name = tensor<string, []>("weight_175_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 7])];
+            tensor<fp16, [128]> resblocks_4_convs2_2_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_4_convs2_2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47594752)))];
+            tensor<fp16, [1, 128, ?]> xt_89_cast_fp16 = conv(bias = resblocks_4_convs2_2_bias_to_fp16, dilations = xt_89_dilations_0, groups = xt_89_groups_0, pad = xt_89_pad_0, pad_type = xt_89_pad_type_0, strides = xt_89_strides_0, weight = weight_175_to_fp16_palettized, x = input_209_cast_fp16)[name = tensor<string, []>("xt_89_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2198_cast_fp16 = add(x = xt_89_cast_fp16, y = input_203_cast_fp16)[name = tensor<string, []>("op_2198_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xs_9_cast_fp16 = add(x = xs_7_cast_fp16, y = var_2198_cast_fp16)[name = tensor<string, []>("xs_9_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_5_adain1_0_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47595072))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47627904))), name = tensor<string, []>("resblocks_5_adain1_0_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_5_adain1_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_5_adain1_0_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47628480)))];
+            tensor<fp16, [1, 256]> linear_40_cast_fp16 = linear(bias = resblocks_5_adain1_0_fc_bias_to_fp16, weight = resblocks_5_adain1_0_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_40_cast_fp16")];
+            tensor<int32, [3]> var_2291 = const()[name = tensor<string, []>("op_2291"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_163_cast_fp16 = reshape(shape = var_2291, x = linear_40_cast_fp16)[name = tensor<string, []>("h_163_cast_fp16")];
+            tensor<int32, [2]> var_2293_split_sizes_0 = const()[name = tensor<string, []>("op_2293_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_2293_axis_0 = const()[name = tensor<string, []>("op_2293_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_2293_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_2293_cast_fp16_1 = split(axis = var_2293_axis_0, split_sizes = var_2293_split_sizes_0, x = h_163_cast_fp16)[name = tensor<string, []>("op_2293_cast_fp16")];
+            tensor<fp16, []> var_2295_promoted_to_fp16 = const()[name = tensor<string, []>("op_2295_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_2296_cast_fp16 = add(x = var_2293_cast_fp16_0, y = var_2295_promoted_to_fp16)[name = tensor<string, []>("op_2296_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2300_cast_fp16 = mul(x = var_2296_cast_fp16, y = var_1669_cast_fp16)[name = tensor<string, []>("op_2300_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_91_cast_fp16 = add(x = var_2300_cast_fp16, y = var_2293_cast_fp16_1)[name = tensor<string, []>("xt_91_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2303_to_fp16 = const()[name = tensor<string, []>("op_2303_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47629056)))];
+            tensor<fp16, [1, 128, ?]> var_2304_cast_fp16 = mul(x = xt_91_cast_fp16, y = var_2303_to_fp16)[name = tensor<string, []>("op_2304_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_61_cast_fp16 = cos(x = var_2304_cast_fp16)[name = tensor<string, []>("cv_61_cast_fp16")];
+            tensor<fp16, []> var_2306_to_fp16 = const()[name = tensor<string, []>("op_2306_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2307_cast_fp16 = mul(x = cv_61_cast_fp16, y = var_2306_to_fp16)[name = tensor<string, []>("op_2307_cast_fp16")];
+            tensor<fp16, []> var_2308_to_fp16 = const()[name = tensor<string, []>("op_2308_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2309_cast_fp16 = add(x = var_2307_cast_fp16, y = var_2308_to_fp16)[name = tensor<string, []>("op_2309_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2310_to_fp16 = const()[name = tensor<string, []>("op_2310_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47629376)))];
+            tensor<fp16, [1, 128, ?]> var_2313_cast_fp16 = mul(x = var_2309_cast_fp16, y = var_2310_to_fp16)[name = tensor<string, []>("op_2313_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_211_cast_fp16 = add(x = xt_91_cast_fp16, y = var_2313_cast_fp16)[name = tensor<string, []>("input_211_cast_fp16")];
+            tensor<string, []> input_213_pad_type_0 = const()[name = tensor<string, []>("input_213_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_213_pad_0 = const()[name = tensor<string, []>("input_213_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> input_213_strides_0 = const()[name = tensor<string, []>("input_213_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> input_213_dilations_0 = const()[name = tensor<string, []>("input_213_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_213_groups_0 = const()[name = tensor<string, []>("input_213_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 11]> weight_179_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [180224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47629696))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47809984))), name = tensor<string, []>("weight_179_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 11])];
+            tensor<fp16, [128]> resblocks_5_convs1_0_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_5_convs1_0_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47810560)))];
+            tensor<fp16, [1, 128, ?]> input_213_cast_fp16 = conv(bias = resblocks_5_convs1_0_bias_to_fp16, dilations = input_213_dilations_0, groups = input_213_groups_0, pad = input_213_pad_0, pad_type = input_213_pad_type_0, strides = input_213_strides_0, weight = weight_179_to_fp16_palettized, x = input_211_cast_fp16)[name = tensor<string, []>("input_213_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_5_adain2_0_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47810880))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47843712))), name = tensor<string, []>("resblocks_5_adain2_0_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_5_adain2_0_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_5_adain2_0_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47844288)))];
+            tensor<fp16, [1, 256]> linear_41_cast_fp16 = linear(bias = resblocks_5_adain2_0_fc_bias_to_fp16, weight = resblocks_5_adain2_0_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_41_cast_fp16")];
+            tensor<int32, [3]> var_2329 = const()[name = tensor<string, []>("op_2329"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_167_cast_fp16 = reshape(shape = var_2329, x = linear_41_cast_fp16)[name = tensor<string, []>("h_167_cast_fp16")];
+            tensor<int32, [2]> var_2331_split_sizes_0 = const()[name = tensor<string, []>("op_2331_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_2331_axis_0 = const()[name = tensor<string, []>("op_2331_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_2331_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_2331_cast_fp16_1 = split(axis = var_2331_axis_0, split_sizes = var_2331_split_sizes_0, x = h_167_cast_fp16)[name = tensor<string, []>("op_2331_cast_fp16")];
+            tensor<fp16, []> var_2333_promoted_to_fp16 = const()[name = tensor<string, []>("op_2333_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_2334_cast_fp16 = add(x = var_2331_cast_fp16_0, y = var_2333_promoted_to_fp16)[name = tensor<string, []>("op_2334_cast_fp16")];
+            tensor<fp16, []> var_2208_to_fp16 = const()[name = tensor<string, []>("op_2208_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 128, ?]> var_2337_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_2208_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_213_cast_fp16)[name = tensor<string, []>("op_2337_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2338_cast_fp16 = mul(x = var_2334_cast_fp16, y = var_2337_cast_fp16)[name = tensor<string, []>("op_2338_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_93_cast_fp16 = add(x = var_2338_cast_fp16, y = var_2331_cast_fp16_1)[name = tensor<string, []>("xt_93_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2341_to_fp16 = const()[name = tensor<string, []>("op_2341_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47844864)))];
+            tensor<fp16, [1, 128, ?]> var_2342_cast_fp16 = mul(x = xt_93_cast_fp16, y = var_2341_to_fp16)[name = tensor<string, []>("op_2342_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_63_cast_fp16 = cos(x = var_2342_cast_fp16)[name = tensor<string, []>("cv_63_cast_fp16")];
+            tensor<fp16, []> var_2344_to_fp16 = const()[name = tensor<string, []>("op_2344_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2345_cast_fp16 = mul(x = cv_63_cast_fp16, y = var_2344_to_fp16)[name = tensor<string, []>("op_2345_cast_fp16")];
+            tensor<fp16, []> var_2346_to_fp16 = const()[name = tensor<string, []>("op_2346_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2347_cast_fp16 = add(x = var_2345_cast_fp16, y = var_2346_to_fp16)[name = tensor<string, []>("op_2347_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2348_to_fp16 = const()[name = tensor<string, []>("op_2348_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47845184)))];
+            tensor<fp16, [1, 128, ?]> var_2351_cast_fp16 = mul(x = var_2347_cast_fp16, y = var_2348_to_fp16)[name = tensor<string, []>("op_2351_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_215_cast_fp16 = add(x = xt_93_cast_fp16, y = var_2351_cast_fp16)[name = tensor<string, []>("input_215_cast_fp16")];
+            tensor<string, []> xt_95_pad_type_0 = const()[name = tensor<string, []>("xt_95_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_95_pad_0 = const()[name = tensor<string, []>("xt_95_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> xt_95_strides_0 = const()[name = tensor<string, []>("xt_95_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_95_dilations_0 = const()[name = tensor<string, []>("xt_95_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_95_groups_0 = const()[name = tensor<string, []>("xt_95_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 11]> weight_183_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [180224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47845504))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48025792))), name = tensor<string, []>("weight_183_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 11])];
+            tensor<fp16, [128]> resblocks_5_convs2_0_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_5_convs2_0_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48026368)))];
+            tensor<fp16, [1, 128, ?]> xt_95_cast_fp16 = conv(bias = resblocks_5_convs2_0_bias_to_fp16, dilations = xt_95_dilations_0, groups = xt_95_groups_0, pad = xt_95_pad_0, pad_type = xt_95_pad_type_0, strides = xt_95_strides_0, weight = weight_183_to_fp16_palettized, x = input_215_cast_fp16)[name = tensor<string, []>("xt_95_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_217_cast_fp16 = add(x = xt_95_cast_fp16, y = input_165_cast_fp16)[name = tensor<string, []>("input_217_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_5_adain1_1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48026688))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48059520))), name = tensor<string, []>("resblocks_5_adain1_1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_5_adain1_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_5_adain1_1_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48060096)))];
+            tensor<fp16, [1, 256]> linear_42_cast_fp16 = linear(bias = resblocks_5_adain1_1_fc_bias_to_fp16, weight = resblocks_5_adain1_1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_42_cast_fp16")];
+            tensor<int32, [3]> var_2368 = const()[name = tensor<string, []>("op_2368"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_171_cast_fp16 = reshape(shape = var_2368, x = linear_42_cast_fp16)[name = tensor<string, []>("h_171_cast_fp16")];
+            tensor<int32, [2]> var_2370_split_sizes_0 = const()[name = tensor<string, []>("op_2370_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_2370_axis_0 = const()[name = tensor<string, []>("op_2370_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_2370_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_2370_cast_fp16_1 = split(axis = var_2370_axis_0, split_sizes = var_2370_split_sizes_0, x = h_171_cast_fp16)[name = tensor<string, []>("op_2370_cast_fp16")];
+            tensor<fp16, []> var_2372_promoted_to_fp16 = const()[name = tensor<string, []>("op_2372_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_2373_cast_fp16 = add(x = var_2370_cast_fp16_0, y = var_2372_promoted_to_fp16)[name = tensor<string, []>("op_2373_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2376_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_2208_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_217_cast_fp16)[name = tensor<string, []>("op_2376_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2377_cast_fp16 = mul(x = var_2373_cast_fp16, y = var_2376_cast_fp16)[name = tensor<string, []>("op_2377_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_97_cast_fp16 = add(x = var_2377_cast_fp16, y = var_2370_cast_fp16_1)[name = tensor<string, []>("xt_97_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2380_to_fp16 = const()[name = tensor<string, []>("op_2380_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48060672)))];
+            tensor<fp16, [1, 128, ?]> var_2381_cast_fp16 = mul(x = xt_97_cast_fp16, y = var_2380_to_fp16)[name = tensor<string, []>("op_2381_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_65_cast_fp16 = cos(x = var_2381_cast_fp16)[name = tensor<string, []>("cv_65_cast_fp16")];
+            tensor<fp16, []> var_2383_to_fp16 = const()[name = tensor<string, []>("op_2383_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2384_cast_fp16 = mul(x = cv_65_cast_fp16, y = var_2383_to_fp16)[name = tensor<string, []>("op_2384_cast_fp16")];
+            tensor<fp16, []> var_2385_to_fp16 = const()[name = tensor<string, []>("op_2385_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2386_cast_fp16 = add(x = var_2384_cast_fp16, y = var_2385_to_fp16)[name = tensor<string, []>("op_2386_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2387_to_fp16 = const()[name = tensor<string, []>("op_2387_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48060992)))];
+            tensor<fp16, [1, 128, ?]> var_2390_cast_fp16 = mul(x = var_2386_cast_fp16, y = var_2387_to_fp16)[name = tensor<string, []>("op_2390_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_219_cast_fp16 = add(x = xt_97_cast_fp16, y = var_2390_cast_fp16)[name = tensor<string, []>("input_219_cast_fp16")];
+            tensor<string, []> input_221_pad_type_0 = const()[name = tensor<string, []>("input_221_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_221_pad_0 = const()[name = tensor<string, []>("input_221_pad_0"), val = tensor<int32, [2]>([15, 15])];
+            tensor<int32, [1]> input_221_dilations_0 = const()[name = tensor<string, []>("input_221_dilations_0"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> input_221_strides_0 = const()[name = tensor<string, []>("input_221_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_221_groups_0 = const()[name = tensor<string, []>("input_221_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 11]> weight_187_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [180224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48061312))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48241600))), name = tensor<string, []>("weight_187_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 11])];
+            tensor<fp16, [128]> resblocks_5_convs1_1_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_5_convs1_1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48242176)))];
+            tensor<fp16, [1, 128, ?]> input_221_cast_fp16 = conv(bias = resblocks_5_convs1_1_bias_to_fp16, dilations = input_221_dilations_0, groups = input_221_groups_0, pad = input_221_pad_0, pad_type = input_221_pad_type_0, strides = input_221_strides_0, weight = weight_187_to_fp16_palettized, x = input_219_cast_fp16)[name = tensor<string, []>("input_221_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_5_adain2_1_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48242496))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48275328))), name = tensor<string, []>("resblocks_5_adain2_1_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_5_adain2_1_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_5_adain2_1_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48275904)))];
+            tensor<fp16, [1, 256]> linear_43_cast_fp16 = linear(bias = resblocks_5_adain2_1_fc_bias_to_fp16, weight = resblocks_5_adain2_1_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_43_cast_fp16")];
+            tensor<int32, [3]> var_2406 = const()[name = tensor<string, []>("op_2406"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_175_cast_fp16 = reshape(shape = var_2406, x = linear_43_cast_fp16)[name = tensor<string, []>("h_175_cast_fp16")];
+            tensor<int32, [2]> var_2408_split_sizes_0 = const()[name = tensor<string, []>("op_2408_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_2408_axis_0 = const()[name = tensor<string, []>("op_2408_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_2408_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_2408_cast_fp16_1 = split(axis = var_2408_axis_0, split_sizes = var_2408_split_sizes_0, x = h_175_cast_fp16)[name = tensor<string, []>("op_2408_cast_fp16")];
+            tensor<fp16, []> var_2410_promoted_to_fp16 = const()[name = tensor<string, []>("op_2410_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_2411_cast_fp16 = add(x = var_2408_cast_fp16_0, y = var_2410_promoted_to_fp16)[name = tensor<string, []>("op_2411_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2414_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_2208_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_221_cast_fp16)[name = tensor<string, []>("op_2414_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2415_cast_fp16 = mul(x = var_2411_cast_fp16, y = var_2414_cast_fp16)[name = tensor<string, []>("op_2415_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_99_cast_fp16 = add(x = var_2415_cast_fp16, y = var_2408_cast_fp16_1)[name = tensor<string, []>("xt_99_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2418_to_fp16 = const()[name = tensor<string, []>("op_2418_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48276480)))];
+            tensor<fp16, [1, 128, ?]> var_2419_cast_fp16 = mul(x = xt_99_cast_fp16, y = var_2418_to_fp16)[name = tensor<string, []>("op_2419_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_67_cast_fp16 = cos(x = var_2419_cast_fp16)[name = tensor<string, []>("cv_67_cast_fp16")];
+            tensor<fp16, []> var_2421_to_fp16 = const()[name = tensor<string, []>("op_2421_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2422_cast_fp16 = mul(x = cv_67_cast_fp16, y = var_2421_to_fp16)[name = tensor<string, []>("op_2422_cast_fp16")];
+            tensor<fp16, []> var_2423_to_fp16 = const()[name = tensor<string, []>("op_2423_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2424_cast_fp16 = add(x = var_2422_cast_fp16, y = var_2423_to_fp16)[name = tensor<string, []>("op_2424_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2425_to_fp16 = const()[name = tensor<string, []>("op_2425_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48276800)))];
+            tensor<fp16, [1, 128, ?]> var_2428_cast_fp16 = mul(x = var_2424_cast_fp16, y = var_2425_to_fp16)[name = tensor<string, []>("op_2428_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_223_cast_fp16 = add(x = xt_99_cast_fp16, y = var_2428_cast_fp16)[name = tensor<string, []>("input_223_cast_fp16")];
+            tensor<string, []> xt_101_pad_type_0 = const()[name = tensor<string, []>("xt_101_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_101_pad_0 = const()[name = tensor<string, []>("xt_101_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> xt_101_strides_0 = const()[name = tensor<string, []>("xt_101_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_101_dilations_0 = const()[name = tensor<string, []>("xt_101_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_101_groups_0 = const()[name = tensor<string, []>("xt_101_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 11]> weight_191_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [180224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48277120))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48457408))), name = tensor<string, []>("weight_191_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 11])];
+            tensor<fp16, [128]> resblocks_5_convs2_1_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_5_convs2_1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48457984)))];
+            tensor<fp16, [1, 128, ?]> xt_101_cast_fp16 = conv(bias = resblocks_5_convs2_1_bias_to_fp16, dilations = xt_101_dilations_0, groups = xt_101_groups_0, pad = xt_101_pad_0, pad_type = xt_101_pad_type_0, strides = xt_101_strides_0, weight = weight_191_to_fp16_palettized, x = input_223_cast_fp16)[name = tensor<string, []>("xt_101_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_225_cast_fp16 = add(x = xt_101_cast_fp16, y = input_217_cast_fp16)[name = tensor<string, []>("input_225_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_5_adain1_2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48458304))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48491136))), name = tensor<string, []>("resblocks_5_adain1_2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_5_adain1_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_5_adain1_2_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48491712)))];
+            tensor<fp16, [1, 256]> linear_44_cast_fp16 = linear(bias = resblocks_5_adain1_2_fc_bias_to_fp16, weight = resblocks_5_adain1_2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_44_cast_fp16")];
+            tensor<int32, [3]> var_2445 = const()[name = tensor<string, []>("op_2445"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_179_cast_fp16 = reshape(shape = var_2445, x = linear_44_cast_fp16)[name = tensor<string, []>("h_179_cast_fp16")];
+            tensor<int32, [2]> var_2447_split_sizes_0 = const()[name = tensor<string, []>("op_2447_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_2447_axis_0 = const()[name = tensor<string, []>("op_2447_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_2447_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_2447_cast_fp16_1 = split(axis = var_2447_axis_0, split_sizes = var_2447_split_sizes_0, x = h_179_cast_fp16)[name = tensor<string, []>("op_2447_cast_fp16")];
+            tensor<fp16, []> var_2449_promoted_to_fp16 = const()[name = tensor<string, []>("op_2449_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_2450_cast_fp16 = add(x = var_2447_cast_fp16_0, y = var_2449_promoted_to_fp16)[name = tensor<string, []>("op_2450_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2453_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_2208_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_225_cast_fp16)[name = tensor<string, []>("op_2453_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2454_cast_fp16 = mul(x = var_2450_cast_fp16, y = var_2453_cast_fp16)[name = tensor<string, []>("op_2454_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_103_cast_fp16 = add(x = var_2454_cast_fp16, y = var_2447_cast_fp16_1)[name = tensor<string, []>("xt_103_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2457_to_fp16 = const()[name = tensor<string, []>("op_2457_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48492288)))];
+            tensor<fp16, [1, 128, ?]> var_2458_cast_fp16 = mul(x = xt_103_cast_fp16, y = var_2457_to_fp16)[name = tensor<string, []>("op_2458_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_69_cast_fp16 = cos(x = var_2458_cast_fp16)[name = tensor<string, []>("cv_69_cast_fp16")];
+            tensor<fp16, []> var_2460_to_fp16 = const()[name = tensor<string, []>("op_2460_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2461_cast_fp16 = mul(x = cv_69_cast_fp16, y = var_2460_to_fp16)[name = tensor<string, []>("op_2461_cast_fp16")];
+            tensor<fp16, []> var_2462_to_fp16 = const()[name = tensor<string, []>("op_2462_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2463_cast_fp16 = add(x = var_2461_cast_fp16, y = var_2462_to_fp16)[name = tensor<string, []>("op_2463_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2464_to_fp16 = const()[name = tensor<string, []>("op_2464_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48492608)))];
+            tensor<fp16, [1, 128, ?]> var_2467_cast_fp16 = mul(x = var_2463_cast_fp16, y = var_2464_to_fp16)[name = tensor<string, []>("op_2467_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_227_cast_fp16 = add(x = xt_103_cast_fp16, y = var_2467_cast_fp16)[name = tensor<string, []>("input_227_cast_fp16")];
+            tensor<string, []> input_229_pad_type_0 = const()[name = tensor<string, []>("input_229_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_229_pad_0 = const()[name = tensor<string, []>("input_229_pad_0"), val = tensor<int32, [2]>([25, 25])];
+            tensor<int32, [1]> input_229_dilations_0 = const()[name = tensor<string, []>("input_229_dilations_0"), val = tensor<int32, [1]>([5])];
+            tensor<int32, [1]> input_229_strides_0 = const()[name = tensor<string, []>("input_229_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_229_groups_0 = const()[name = tensor<string, []>("input_229_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 11]> weight_195_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [180224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48492928))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48673216))), name = tensor<string, []>("weight_195_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 11])];
+            tensor<fp16, [128]> resblocks_5_convs1_2_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_5_convs1_2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48673792)))];
+            tensor<fp16, [1, 128, ?]> input_229_cast_fp16 = conv(bias = resblocks_5_convs1_2_bias_to_fp16, dilations = input_229_dilations_0, groups = input_229_groups_0, pad = input_229_pad_0, pad_type = input_229_pad_type_0, strides = input_229_strides_0, weight = weight_195_to_fp16_palettized, x = input_227_cast_fp16)[name = tensor<string, []>("input_229_cast_fp16")];
+            tensor<fp16, [256, 128]> resblocks_5_adain2_2_fc_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [32768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48674112))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48706944))), name = tensor<string, []>("resblocks_5_adain2_2_fc_weight_to_fp16_palettized"), shape = tensor<uint32, [2]>([256, 128])];
+            tensor<fp16, [256]> resblocks_5_adain2_2_fc_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_5_adain2_2_fc_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48707520)))];
+            tensor<fp16, [1, 256]> linear_45_cast_fp16 = linear(bias = resblocks_5_adain2_2_fc_bias_to_fp16, weight = resblocks_5_adain2_2_fc_weight_to_fp16_palettized, x = style_timbre)[name = tensor<string, []>("linear_45_cast_fp16")];
+            tensor<int32, [3]> var_2483 = const()[name = tensor<string, []>("op_2483"), val = tensor<int32, [3]>([1, 256, 1])];
+            tensor<fp16, [1, 256, 1]> h_cast_fp16 = reshape(shape = var_2483, x = linear_45_cast_fp16)[name = tensor<string, []>("h_cast_fp16")];
+            tensor<int32, [2]> var_2485_split_sizes_0 = const()[name = tensor<string, []>("op_2485_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
+            tensor<int32, []> var_2485_axis_0 = const()[name = tensor<string, []>("op_2485_axis_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [1, 128, 1]> var_2485_cast_fp16_0, tensor<fp16, [1, 128, 1]> var_2485_cast_fp16_1 = split(axis = var_2485_axis_0, split_sizes = var_2485_split_sizes_0, x = h_cast_fp16)[name = tensor<string, []>("op_2485_cast_fp16")];
+            tensor<fp16, []> var_2487_promoted_to_fp16 = const()[name = tensor<string, []>("op_2487_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 128, 1]> var_2488_cast_fp16 = add(x = var_2485_cast_fp16_0, y = var_2487_promoted_to_fp16)[name = tensor<string, []>("op_2488_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2491_cast_fp16 = instance_norm(beta = resblocks_3_adain1_0_norm_bias_to_fp16, epsilon = var_2208_to_fp16, gamma = resblocks_3_adain1_0_norm_weight_to_fp16, x = input_229_cast_fp16)[name = tensor<string, []>("op_2491_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2492_cast_fp16 = mul(x = var_2488_cast_fp16, y = var_2491_cast_fp16)[name = tensor<string, []>("op_2492_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xt_105_cast_fp16 = add(x = var_2492_cast_fp16, y = var_2485_cast_fp16_1)[name = tensor<string, []>("xt_105_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2495_to_fp16 = const()[name = tensor<string, []>("op_2495_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48708096)))];
+            tensor<fp16, [1, 128, ?]> var_2496_cast_fp16 = mul(x = xt_105_cast_fp16, y = var_2495_to_fp16)[name = tensor<string, []>("op_2496_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> cv_cast_fp16 = cos(x = var_2496_cast_fp16)[name = tensor<string, []>("cv_cast_fp16")];
+            tensor<fp16, []> var_2498_to_fp16 = const()[name = tensor<string, []>("op_2498_to_fp16"), val = tensor<fp16, []>(-0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2499_cast_fp16 = mul(x = cv_cast_fp16, y = var_2498_to_fp16)[name = tensor<string, []>("op_2499_cast_fp16")];
+            tensor<fp16, []> var_2500_to_fp16 = const()[name = tensor<string, []>("op_2500_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
+            tensor<fp16, [1, 128, ?]> var_2501_cast_fp16 = add(x = var_2499_cast_fp16, y = var_2500_to_fp16)[name = tensor<string, []>("op_2501_cast_fp16")];
+            tensor<fp16, [1, 128, 1]> var_2502_to_fp16 = const()[name = tensor<string, []>("op_2502_to_fp16"), val = tensor<fp16, [1, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48708416)))];
+            tensor<fp16, [1, 128, ?]> var_2505_cast_fp16 = mul(x = var_2501_cast_fp16, y = var_2502_to_fp16)[name = tensor<string, []>("op_2505_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> input_231_cast_fp16 = add(x = xt_105_cast_fp16, y = var_2505_cast_fp16)[name = tensor<string, []>("input_231_cast_fp16")];
+            tensor<string, []> xt_pad_type_0 = const()[name = tensor<string, []>("xt_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> xt_pad_0 = const()[name = tensor<string, []>("xt_pad_0"), val = tensor<int32, [2]>([5, 5])];
+            tensor<int32, [1]> xt_strides_0 = const()[name = tensor<string, []>("xt_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> xt_dilations_0 = const()[name = tensor<string, []>("xt_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> xt_groups_0 = const()[name = tensor<string, []>("xt_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [128, 128, 11]> weight_199_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor<uint8, [180224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48708736))), lut = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48889024))), name = tensor<string, []>("weight_199_to_fp16_palettized"), shape = tensor<uint32, [3]>([128, 128, 11])];
+            tensor<fp16, [128]> resblocks_5_convs2_2_bias_to_fp16 = const()[name = tensor<string, []>("resblocks_5_convs2_2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48889600)))];
+            tensor<fp16, [1, 128, ?]> xt_cast_fp16 = conv(bias = resblocks_5_convs2_2_bias_to_fp16, dilations = xt_dilations_0, groups = xt_groups_0, pad = xt_pad_0, pad_type = xt_pad_type_0, strides = xt_strides_0, weight = weight_199_to_fp16_palettized, x = input_231_cast_fp16)[name = tensor<string, []>("xt_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> var_2514_cast_fp16 = add(x = xt_cast_fp16, y = input_225_cast_fp16)[name = tensor<string, []>("op_2514_cast_fp16")];
+            tensor<fp16, [1, 128, ?]> xs_cast_fp16 = add(x = xs_9_cast_fp16, y = var_2514_cast_fp16)[name = tensor<string, []>("xs_cast_fp16")];
+            tensor<fp16, []> _inversed_input_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_input_y_0_to_fp16"), val = tensor<fp16, []>(0x1.554p-2)];
+            tensor<fp16, [1, 128, ?]> _inversed_input_cast_fp16 = mul(x = xs_cast_fp16, y = _inversed_input_y_0_to_fp16)[name = tensor<string, []>("_inversed_input_cast_fp16")];
+            tensor<fp32, []> var_2519 = const()[name = tensor<string, []>("op_2519"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp16, [1, 128, ?]> x_pre = leaky_relu(alpha = var_2519, x = _inversed_input_cast_fp16)[name = tensor<string, []>("x_pre_cast_fp16")];
+            tensor<bool, []> var_2522_keep_dims_0 = const()[name = tensor<string, []>("op_2522_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, []> var_2522_cast_fp16 = reduce_mean(keep_dims = var_2522_keep_dims_0, x = x_pre)[name = tensor<string, []>("op_2522_cast_fp16")];
+            tensor<int32, [1]> var_2524_axes_0 = const()[name = tensor<string, []>("op_2524_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1]> anchor = expand_dims(axes = var_2524_axes_0, x = var_2522_cast_fp16)[name = tensor<string, []>("op_2524_cast_fp16")];
+        } -> (anchor, x_pre);
+}
\ No newline at end of file
diff --git a/ANE/ANE-zh/KokoroVocoder.mlmodelc/weights/weight.bin b/ANE/ANE-zh/KokoroVocoder.mlmodelc/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..d5f40c8db5c10f403e377fdbf87cba44ca401901
--- /dev/null
+++ b/ANE/ANE-zh/KokoroVocoder.mlmodelc/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:daa560673b32e3efce3ca99299d083c42c5844dd8022a8a847c23f2d00b20c6b
+size 48889920
diff --git a/ANE/ANE-zh/KokoroVocoder.mlpackage/Data/com.apple.CoreML/model.mlmodel b/ANE/ANE-zh/KokoroVocoder.mlpackage/Data/com.apple.CoreML/model.mlmodel
new file mode 100644
index 0000000000000000000000000000000000000000..14a42ef45baf1969d5f17f805ddb59cc00325c93
--- /dev/null
+++ b/ANE/ANE-zh/KokoroVocoder.mlpackage/Data/com.apple.CoreML/model.mlmodel
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb36344f47c7c0da6e39b928edd8e2d0393246d84cf807e5d74855eef37dfebd
+size 255855
diff --git a/ANE/ANE-zh/KokoroVocoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/ANE/ANE-zh/KokoroVocoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..d5f40c8db5c10f403e377fdbf87cba44ca401901
--- /dev/null
+++ b/ANE/ANE-zh/KokoroVocoder.mlpackage/Data/com.apple.CoreML/weights/weight.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:daa560673b32e3efce3ca99299d083c42c5844dd8022a8a847c23f2d00b20c6b
+size 48889920
diff --git a/ANE/ANE-zh/KokoroVocoder.mlpackage/Manifest.json b/ANE/ANE-zh/KokoroVocoder.mlpackage/Manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..e2cbdf47b8ad7606e19c70e7b06ecd053b8a38df
--- /dev/null
+++ b/ANE/ANE-zh/KokoroVocoder.mlpackage/Manifest.json
@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "43E7FECC-0F8F-4FFD-8BEA-805B89C6839F": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "D8E47693-058B-43E2-BD65-9D1DDE555FD0": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "D8E47693-058B-43E2-BD65-9D1DDE555FD0"
+}
diff --git a/ANE/ANE-zh/LICENSE b/ANE/ANE-zh/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..f795c299c648d3ce5a6cfa4ab5c5f1f903d16dfa
--- /dev/null
+++ b/ANE/ANE-zh/LICENSE
@@ -0,0 +1,202 @@
+
+                                 Apache License
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+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+   5. Submission of Contributions. Unless You explicitly state otherwise,
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+   6. Trademarks. This License does not grant permission to use the trade
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diff --git a/ANE/ANE-zh/README.md b/ANE/ANE-zh/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b9d38c9f927af74fe6ec0697a6165ff4faf070be
--- /dev/null
+++ b/ANE/ANE-zh/README.md
@@ -0,0 +1,75 @@
+# Kokoro-82M-v1.1-zh — 7-stage CoreML chain (Mandarin)
+
+Sibling of [`ANE/`](../ANE/) but for the Mandarin checkpoint
+[`hexgrad/Kokoro-82M-v1.1-zh`](https://huggingface.co/hexgrad/Kokoro-82M-v1.1-zh).
+Same architecture (StyleTTS2 + ALBERT, 82M params), same 7-stage split,
+same RangeDim shape bounds, same fp16+int8 palettization preset. Differs
+only in the embedding vocabulary (171 vs 177 entries) and the trained
+weights.
+
+## Contents
+
+| File                              | Size    | Notes                                         |
+|-----------------------------------|---------|-----------------------------------------------|
+| `KokoroAlbert.mlmodelc/.mlpackage`     | 5.6 MB  | fp16 + int8 palettize, `CPU_AND_NE`           |
+| `KokoroPostAlbert.mlmodelc/.mlpackage` | 13 MB   | fp16 + int8 palettize, `CPU_AND_NE`           |
+| `KokoroAlignment.mlmodelc/.mlpackage`  | 32 KB   | fp16 + int8 palettize, `CPU_AND_NE`           |
+| `KokoroProsody.mlmodelc/.mlpackage`    | 8.2 MB  | fp16 + int8 palettize, `ALL`                  |
+| `KokoroNoise.mlmodelc/.mlpackage`      | 4.5 MB  | fp32 + int8 palettize, `ALL`                  |
+| `KokoroVocoder.mlmodelc/.mlpackage`    | 47 MB   | fp16 + int8 palettize, `CPU_AND_NE`           |
+| `KokoroTail.mlmodelc/.mlpackage`       | 100 KB  | fp32, `ALL`                                   |
+| `vocab.json`                      | 1.8 KB  | 171 entries (38 Bopomofo + IPA + tone digits + punctuation + Hanzi) |
+| `voices/zf_001.bin`               | 510 KB  | Female voice pack `[510, 256]` flat fp32      |
+| `voices/zm_009.bin`               | 510 KB  | Male voice pack `[510, 256]` flat fp32        |
+| `benchmark_data.json`             | 4.7 KB  | 6 precomputed Mandarin phoneme cases for benchmarking |
+
+Total compiled chain: ~78 MB mlmodelc.
+
+## Differences vs `ANE/` (v1.0 English)
+
+| Item                | `ANE/` (v1.0)            | `ANE-zh/` (v1.1-zh)          |
+|---------------------|--------------------------|------------------------------|
+| Upstream checkpoint | `hexgrad/Kokoro-82M`     | `hexgrad/Kokoro-82M-v1.1-zh` |
+| Vocab size          | 177 (IPA + arrow tones)  | 171 (IPA + Bopomofo `ㄅㄆㄇ` + tone digits `1-5`) |
+| `lang_code`         | `'a'` (English)          | `'z'` (Mandarin)             |
+| G2P backend         | `misaki.en` (espeak fallback) | `misaki.zh` (jieba + pypinyin → Bopomofo+digit) |
+| Voice pack          | `af_heart.bin`           | `voices/zf_001.bin` (female) + `voices/zm_009.bin` (male) |
+
+Architecture, layer counts, hidden sizes, RangeDim bounds, op-translation
+patches, and per-stage compute-unit choices are unchanged from the v1.0
+chain.
+
+## Conversion provenance
+
+Converted from the upstream PyTorch checkpoint via the script in
+[mobius/models/tts/kokoro-v1.1-zh/coreml/](https://github.com/FluidInference/FluidAudio/tree/main/mobius/models/tts/kokoro-v1.1-zh/coreml)
+(adapted from [`models/tts/kokoro/laishere-coreml/`](https://github.com/FluidInference/FluidAudio/tree/main/mobius/models/tts/kokoro/laishere-coreml),
+MIT licensed by laishere).
+
+Conversion run on Apple Silicon (Darwin 25.5.0). Per-stage timings on the
+test sentence `你好世界，今天天气真好。` (T_enc=37):
+
+| Stage          | CPU_AND_NE |
+|----------------|------------|
+| Albert         | 2 ms       |
+| PostAlbert     | 4 ms       |
+| Alignment      | 1 ms       |
+| Prosody        | 4 ms       |
+| Noise          | 50 ms      |
+| Vocoder        | 130–266 ms |
+| Tail           | 6–8 ms     |
+| **Chain (E2E)**| **200–333 ms** for 3.3–3.5 s of audio (10–17× real-time) |
+
+E2E mel-spectrogram correlation vs PyTorch teacher: 0.97–0.998.
+
+## Voices
+
+The 2 packaged voices keep the bundle small. Upstream provides the full
+96-voice set (49 `zf_*` + 47 `zm_*` + 3 EN). To produce additional voice
+`.bin` files locally, see
+[`dump-benchmark-data.py --voices …`](https://github.com/FluidInference/FluidAudio/tree/main/mobius/models/tts/kokoro-v1.1-zh/coreml/dump-benchmark-data.py).
+
+## License
+
+MIT (conversion). Underlying Kokoro-82M-v1.1-zh model © hexgrad,
+Apache-2.0.
diff --git a/ANE/ANE-zh/benchmark_data.json b/ANE/ANE-zh/benchmark_data.json
new file mode 100644
index 0000000000000000000000000000000000000000..9728dc8ab68014bb090add8dd7d58b385c3379f1
--- /dev/null
+++ b/ANE/ANE-zh/benchmark_data.json
@@ -0,0 +1,47 @@
+{
+  "voices": [
+    "zf_001",
+    "zm_009"
+  ],
+  "lang": "z",
+  "repo_id": "hexgrad/Kokoro-82M-v1.1-zh",
+  "sample_rate": 24000,
+  "cases": [
+    {
+      "id": 0,
+      "text": "你好。",
+      "phonemes": "ㄋㄧ2ㄏㄠ3.",
+      "n_phonemes": 7
+    },
+    {
+      "id": 1,
+      "text": "今天的天气真不错，阳光明媚，微风拂面。",
+      "phonemes": "ㄐ阴1ㄊ言1/ㄉㄜ5/ㄊ言1ㄑㄧ4/ㄓㄣ1ㄅㄨ5ㄘ我4, 阳2ㄍ王1ㄇ应2ㄇㄟ4, 为1ㄈㄥ1/ㄈㄨ2ㄇ言4.",
+      "n_phonemes": 55
+    },
+    {
+      "id": 2,
+      "text": "她已经等了将近一个小时。公交车一如既往地迟到了。一阵冷风吹来，吹动了她脚边的落叶。",
+      "phonemes": "ㄊㄚ1/ㄧ3ㄐ应1/ㄉㄥ3/ㄌㄜ5/ㄐ阳1ㄐ阴4/ㄧ2ㄍㄜ5/ㄒ要3ㄕ十2. ㄍ中1ㄐ要1ㄔㄜ1/ㄧ4ㄖㄨ2ㄐㄧ4王3/ㄉㄜ5/ㄔ十2ㄉㄠ4/ㄌㄜ5. ㄧ2ㄓㄣ4/ㄌㄥ3ㄈㄥ1ㄔ为1/ㄌㄞ2, ㄔ为1ㄉ中4/ㄌㄜ5/ㄊㄚ1/ㄐ要3ㄅ言1/ㄉㄜ5/ㄌ我4ㄝ4.",
+      "n_phonemes": 129
+    },
+    {
+      "id": 3,
+      "text": "在安静的小镇米尔布鲁克，消息传播得比风还快。等警长到达现场时，半数居民已经聚集在那里，低声议论着各种猜测，交换着模糊的事实。没人真正知道发生了什么，但每个人都有自己的看法。",
+      "phonemes": "ㄗㄞ4/ㄢ1ㄐ应4/ㄉㄜ5/ㄒ要3ㄓㄣ4/ㄇㄧ2ㄦ3/ㄅㄨ4ㄌㄨ3ㄎㄜ4, ㄒ要1ㄒㄧ5/ㄔ万2ㄅㄛ1/ㄉㄜ2ㄅㄧ3/ㄈㄥ1/ㄏㄞ2/ㄎ外4. ㄉㄥ3ㄐ应2ㄓㄤ3/ㄉㄠ4ㄉㄚ2/ㄒ言4ㄔㄤ3/ㄕ十2, ㄅㄢ4ㄕㄨ4/ㄐㄩ1ㄇ阴2/ㄧ3ㄐ应1/ㄐㄩ4ㄐㄧ2/ㄗㄞ4/ㄋㄚ4ㄌㄧ3, ㄉㄧ1ㄕㄥ1/ㄧ4ㄌ文4/ㄓㄜ5/ㄍㄜ4ㄓ中3/ㄘㄞ1ㄘㄜ4, ㄐ要1ㄏ万4/ㄓㄜ5/ㄇㄛ2ㄏㄨ5/ㄉㄜ5/ㄕ十4ㄕ十2. ㄇㄟ2/ㄖㄣ2/ㄓㄣ1ㄓㄥ4/ㄓ十1ㄉㄠ4/ㄈㄚ1ㄕㄥ1/ㄌㄜ5/ㄕㄣ2ㄇㄜ5, ㄉㄢ4/ㄇㄟ3ㄍㄜ5/ㄖㄣ2/ㄉㄡ1/又3/ㄗㄭ4ㄐㄧ3/ㄉㄜ5/ㄎㄢ4ㄈㄚ3.",
+      "n_phonemes": 283
+    },
+    {
+      "id": 4,
+      "text": "探险队于一九二三年三月十四日出发，共有十二名队员，三辆雪橇，以及足够六十天的补给。两周内，一场突如其来的暴风雪使先头部队与补给队失去联系；本应是常规的穿越，变成了求生的殊死搏斗。当救援到达时，原队中只剩下四人，他们的日记后来被誉为极地探险的非凡记录。",
+      "phonemes": "ㄊㄢ4ㄒ言3ㄉ为4/ㄩ2/ㄧ1ㄐ又3ㄦ4/ㄙㄢ1ㄋ言2/ㄙㄢ1月4/ㄕ十2ㄙㄭ4ㄖ十4/ㄔㄨ1ㄈㄚ1, ㄍ中4又3/ㄕ十2ㄦ4ㄇ应2/ㄉ为4元2, ㄙㄢ1ㄌ阳4/ㄒ月3ㄑ要1, ㄧ3ㄐㄧ2/ㄗㄨ2ㄍㄡ4/ㄌ又4ㄕ十2ㄊ言1/ㄉㄜ5/ㄅㄨ2ㄐㄧ3. ㄌ阳3ㄓㄡ1/ㄋㄟ4, ㄧ4ㄔㄤ3/ㄊㄨ1ㄖㄨ2ㄑㄧ2ㄌㄞ2/ㄉㄜ5/ㄅㄠ4ㄈㄥ1ㄒ月3/ㄕ十3/ㄒ言1ㄊㄡ2ㄅㄨ4ㄉ为4/ㄩ3ㄅㄨ2ㄐㄧ3/ㄉ为4/ㄕ十1ㄑㄩ4/ㄌ言2ㄒㄧ4; ㄅㄣ3/应1/ㄕ十4/ㄔㄤ2ㄍ为1/ㄉㄜ5/ㄔ万1月4, ㄅ言4ㄔㄥ2/ㄌㄜ5/ㄑ又2ㄕㄥ1/ㄉㄜ5/ㄕㄨ1ㄙㄭ3ㄅㄛ2ㄉㄡ4. ㄉㄤ1/ㄐ又4元2/ㄉㄠ4ㄉㄚ2/ㄕ十2, 元2ㄉ为4/ㄓ中1/ㄓ十3/ㄕㄥ4ㄒ压4/ㄙㄭ4/ㄖㄣ2, ㄊㄚ1ㄇㄣ5/ㄉㄜ5/ㄖ十4ㄐㄧ4/ㄏㄡ4ㄌㄞ2/ㄅㄟ4ㄩ4为2/ㄐㄧ2ㄉㄧ5/ㄊㄢ4ㄒ言3/ㄉㄜ5/ㄈㄟ1ㄈㄢ2/ㄐㄧ4ㄌㄨ4.",
+      "n_phonemes": 396
+    },
+    {
+      "id": 5,
+      "text": "人类飞行的历史，在许多方面，是一部固执拒绝的历史。几千年来，观察者们看着鸟儿轻盈地翱翔在头顶，断定人类生来并不是为了与它们一同飞翔。然而，发明家们坚持不懈。他们用羽毛和蜡制作翅膀，用丝绸和竹子制作滑翔机，用氢气、氦气，甚至烟火加热的热空气填充气球。每一次失败都让他们对升力、阻力，以及流体在这些弯曲表面上的奇特行为有了新的认识。",
+      "phonemes": "ㄖㄣ2ㄌㄟ4/ㄈㄟ1ㄒ应2/ㄉㄜ5/ㄌㄧ4ㄕ十3, ㄗㄞ4/ㄒㄩ3ㄉ我1/ㄈㄤ1ㄇ言4, ㄕ十4/ㄧ2ㄅㄨ4/ㄍㄨ4ㄓ十2/ㄐㄩ4ㄐ月2/ㄉㄜ5/ㄌㄧ4ㄕ十3. ㄐㄧ3ㄑ言1ㄋ言2/ㄌㄞ2, ㄍ万1ㄔㄚ2ㄓㄜ3/ㄇㄣ5/ㄎㄢ4ㄓㄜ5/ㄋ要3ㄦ5/ㄑ应1应2/ㄉㄜ5/ㄠ2ㄒ阳2/ㄗㄞ4/ㄊㄡ2ㄉ应3, ㄉ万4ㄉ应4/ㄖㄣ2ㄌㄟ4/ㄕㄥ1ㄌㄞ2/ㄅ应4/ㄅㄨ2ㄕ十4/为4ㄌㄜ5/ㄩ3/ㄊㄚ1ㄇㄣ5/ㄧ4ㄊ中2/ㄈㄟ1ㄒ阳2. ㄖㄢ2ㄦ2, ㄈㄚ1ㄇ应2ㄐ压1/ㄇㄣ5/ㄐ言1ㄔ十2ㄅㄨ2ㄒㄝ4. ㄊㄚ1ㄇㄣ5/用4/ㄩ3ㄇㄠ2/ㄏㄜ2/ㄌㄚ4/ㄓ十4ㄗ我4/ㄔ十4ㄅㄤ3, 用4/ㄙㄭ1ㄔㄡ2/ㄏㄜ2/ㄓㄨ2ㄗㄭ5/ㄓ十4ㄗ我4/ㄏ穵2ㄒ阳2ㄐㄧ1, 用4/ㄑ应1ㄑㄧ4, ㄏㄞ4ㄑㄧ4, ㄕㄣ4ㄓ十4/言1ㄏ我3/ㄐ压1ㄖㄜ4/ㄉㄜ5/ㄖㄜ4ㄎ中1ㄑㄧ4/ㄊ言2ㄔ中1/ㄑㄧ4ㄑ又2. ㄇㄟ3/ㄧ2ㄘㄭ4/ㄕ十1ㄅㄞ4/ㄉㄡ1/ㄖㄤ4/ㄊㄚ1ㄇㄣ5/ㄉ为4/ㄕㄥ1ㄌㄧ4, ㄗㄨ3ㄌㄧ4, ㄧ3ㄐㄧ2/ㄌ又2ㄊㄧ3/ㄗㄞ4/ㄓㄜ4ㄒㄝ1/万1ㄑㄩ1ㄅ要3ㄇ言4/ㄕㄤ4/ㄉㄜ5/ㄑㄧ2ㄊㄜ4/ㄒ应2为2/又3/ㄌㄜ5/",
+      "n_phonemes": 510
+    }
+  ]
+}
\ No newline at end of file
diff --git a/ANE/ANE-zh/vocab.json b/ANE/ANE-zh/vocab.json
new file mode 100644
index 0000000000000000000000000000000000000000..3fbce5d9b08db4b61ab829c9abfb8bd7487ae48a
--- /dev/null
+++ b/ANE/ANE-zh/vocab.json
@@ -0,0 +1 @@
+{";": 1, ":": 2, ",": 3, ".": 4, "!": 5, "?": 6, "/": 7, "—": 9, "…": 10, "\"": 11, "(": 12, ")": 13, "“": 14, "”": 15, " ": 16, "̃": 17, "ʣ": 18, "ʥ": 19, "ʦ": 20, "ʨ": 21, "ᵝ": 22, "ㄓ": 23, "A": 24, "I": 25, "ㄅ": 30, "O": 31, "ㄆ": 32, "Q": 33, "R": 34, "S": 35, "T": 36, "ㄇ": 37, "ㄈ": 38, "W": 39, "ㄉ": 40, "Y": 41, "ᵊ": 42, "a": 43, "b": 44, "c": 45, "d": 46, "e": 47, "f": 48, "ㄊ": 49, "h": 50, "i": 51, "j": 52, "k": 53, "l": 54, "m": 55, "n": 56, "o": 57, "p": 58, "q": 59, "r": 60, "s": 61, "t": 62, "u": 63, "v": 64, "w": 65, "x": 66, "y": 67, "z": 68, "ɑ": 69, "ɐ": 70, "ɒ": 71, "æ": 72, "ㄋ": 73, "ㄌ": 74, "β": 75, "ɔ": 76, "ɕ": 77, "ç": 78, "ㄍ": 79, "ɖ": 80, "ð": 81, "ʤ": 82, "ə": 83, "ㄎ": 84, "ㄦ": 85, "ɛ": 86, "ɜ": 87, "ㄏ": 88, "ㄐ": 89, "ɟ": 90, "ㄑ": 91, "ɡ": 92, "ㄒ": 93, "ㄔ": 94, "ㄕ": 95, "ㄗ": 96, "ㄘ": 97, "ㄙ": 98, "月": 99, "ㄚ": 100, "ɨ": 101, "ɪ": 102, "ʝ": 103, "ㄛ": 104, "ㄝ": 105, "ㄞ": 106, "ㄟ": 107, "ㄠ": 108, "ㄡ": 109, "ɯ": 110, "ɰ": 111, "ŋ": 112, "ɳ": 113, "ɲ": 114, "ɴ": 115, "ø": 116, "ㄢ": 117, "ɸ": 118, "θ": 119, "œ": 120, "ㄣ": 121, "ㄤ": 122, "ɹ": 123, "ㄥ": 124, "ɾ": 125, "ㄖ": 126, "ㄧ": 127, "ʁ": 128, "ɽ": 129, "ʂ": 130, "ʃ": 131, "ʈ": 132, "ʧ": 133, "ㄨ": 134, "ʊ": 135, "ʋ": 136, "ㄩ": 137, "ʌ": 138, "ɣ": 139, "ㄜ": 140, "ㄭ": 141, "χ": 142, "ʎ": 143, "十": 144, "压": 145, "言": 146, "ʒ": 147, "ʔ": 148, "阳": 149, "要": 150, "阴": 151, "应": 152, "用": 153, "又": 154, "中": 155, "ˈ": 156, "ˌ": 157, "ː": 158, "穵": 159, "外": 160, "万": 161, "ʰ": 162, "王": 163, "ʲ": 164, "为": 165, "文": 166, "瓮": 167, "我": 168, "3": 169, "5": 170, "1": 171, "2": 172, "4": 173, "元": 175, "云": 176, "ᵻ": 177}
\ No newline at end of file
diff --git a/ANE/ANE-zh/voices/zf_001.bin b/ANE/ANE-zh/voices/zf_001.bin
new file mode 100644
index 0000000000000000000000000000000000000000..55d4e87a6c5c7c2b89ed69f5c4fcfd709b29dbbc
--- /dev/null
+++ b/ANE/ANE-zh/voices/zf_001.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0a89ec12bb93fb9c74077924daf02568baad64e1f869389f5aaee01a386035f8
+size 522240
diff --git a/ANE/ANE-zh/voices/zm_009.bin b/ANE/ANE-zh/voices/zm_009.bin
new file mode 100644
index 0000000000000000000000000000000000000000..66dc29289039f2e7f46cef7c89068afec9d638c1
--- /dev/null
+++ b/ANE/ANE-zh/voices/zm_009.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b74d6ed22f201e2fa28758e78ce6197082779f2b80e69ea1bf877908609514a
+size 522240