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DurationPredictor.mlmodelc/analytics/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b836b72b388c2bc009a13bcc495cd08278a1742b1a866cdaf7faab7d80786bcb
+size 243

DurationPredictor.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3ee71b864377bd54479f6b07d141ed37c9fc49718aba6dba21c56cc5cb975be0
+size 425

DurationPredictor.mlmodelc/model.mil

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+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 8, 16]> style_dp, tensor<int32, [1, 128]> text_ids, tensor<fp32, [1, 1, 128]> text_mask) {
+            int32 var_25 = const()[name = string("op_25"), val = int32(2)];
+            int32 var_26 = const()[name = string("op_26"), val = int32(1)];
+            int32 var_27 = const()[name = string("op_27"), val = int32(-1)];
+            int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)];
+            bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)];
+            tensor<fp16, [8322, 64]> m_char_embedder_weight_to_fp16 = const()[name = string("m_char_embedder_weight_to_fp16"), val = tensor<fp16, [8322, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            string text_ids_to_int16_dtype_0 = const()[name = string("text_ids_to_int16_dtype_0"), val = string("int16")];
+            string cast_19_dtype_0 = const()[name = string("cast_19_dtype_0"), val = string("int32")];
+            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
+            tensor<int16, [1, 128]> text_ids_to_int16 = cast(dtype = text_ids_to_int16_dtype_0, x = text_ids)[name = string("cast_25")];
+            tensor<int32, [1, 128]> cast_19 = cast(dtype = cast_19_dtype_0, x = text_ids_to_int16)[name = string("cast_24")];
+            tensor<bool, [1, 128]> greater_equal_0 = greater_equal(x = cast_19, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
+            int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(8322)];
+            tensor<int32, [1, 128]> add_0 = add(x = cast_19, y = slice_by_index_0)[name = string("add_0")];
+            tensor<int32, [1, 128]> select_0 = select(a = cast_19, b = add_0, cond = greater_equal_0)[name = string("select_0")];
+            int32 x_1_cast_fp16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_axis_0"), val = int32(0)];
+            string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")];
+            tensor<int16, [1, 128]> select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_23")];
+            tensor<fp16, [1, 128, 64]> x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_to_int16, validate_indices = x_1_validate_indices_0, x = m_char_embedder_weight_to_fp16)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")];
+            tensor<int32, [3]> var_53_perm_0 = const()[name = string("op_53_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 1, 128]> text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_22")];
+            tensor<fp16, [1, 64, 128]> var_53_cast_fp16 = transpose(perm = var_53_perm_0, x = x_1_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_24")];
+            tensor<fp16, [1, 64, 128]> x_3_cast_fp16 = mul(x = var_53_cast_fp16, y = text_mask_to_fp16)[name = string("x_3_cast_fp16")];
+            bool x_5_interleave_0 = const()[name = string("x_5_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 64, 1]> m_sentence_token_to_fp16 = const()[name = string("m_sentence_token_to_fp16"), val = tensor<fp16, [1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1065344)))];
+            tensor<fp16, [1, 64, 129]> x_5_cast_fp16 = concat(axis = var_25, interleave = x_5_interleave_0, values = (m_sentence_token_to_fp16, x_3_cast_fp16))[name = string("x_5_cast_fp16")];
+            bool mask_interleave_0 = const()[name = string("mask_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 1, 1]> fill_0_to_fp16 = const()[name = string("fill_0_to_fp16"), val = tensor<fp16, [1, 1, 1]>([[[0x1p+0]]])];
+            tensor<fp16, [1, 1, 129]> mask_cast_fp16 = concat(axis = var_25, interleave = mask_interleave_0, values = (fill_0_to_fp16, text_mask_to_fp16))[name = string("mask_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> input_1_cast_fp16 = mul(x = x_5_cast_fp16, y = mask_cast_fp16)[name = string("input_1_cast_fp16")];
+            tensor<int32, [6]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
+            string input_3_mode_0 = const()[name = string("input_3_mode_0"), val = string("replicate")];
+            fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 64, 133]> input_3_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_3_mode_0, pad = input_3_pad_0, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")];
+            string h_1_pad_type_0 = const()[name = string("h_1_pad_type_0"), val = string("valid")];
+            int32 h_1_groups_0 = const()[name = string("h_1_groups_0"), val = int32(64)];
+            tensor<int32, [1]> h_1_strides_0 = const()[name = string("h_1_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_1_pad_0 = const()[name = string("h_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_1_dilations_0 = const()[name = string("h_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [64, 1, 5]> m_convnext_0_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_0_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1065536)))];
+            tensor<fp16, [64]> m_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066240)))];
+            tensor<fp16, [1, 64, 129]> h_1_cast_fp16 = conv(bias = m_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_1_dilations_0, groups = h_1_groups_0, pad = h_1_pad_0, pad_type = h_1_pad_type_0, strides = h_1_strides_0, weight = m_convnext_0_dwconv__conv_weight_to_fp16, x = input_3_cast_fp16)[name = string("h_1_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_7_cast_fp16 = mul(x = h_1_cast_fp16, y = mask_cast_fp16)[name = string("x_7_cast_fp16")];
+            tensor<int32, [3]> input_5_perm_0 = const()[name = string("input_5_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_85_axes_0 = const()[name = string("op_85_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [64]> m_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_0_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066432)))];
+            tensor<fp16, [64]> m_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_0_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066624)))];
+            fp16 var_20_to_fp16 = const()[name = string("op_20_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 129, 64]> input_5_cast_fp16 = transpose(perm = input_5_perm_0, x = x_7_cast_fp16)[name = string("transpose_23")];
+            tensor<fp16, [1, 129, 64]> var_85_cast_fp16 = layer_norm(axes = var_85_axes_0, beta = m_convnext_0_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_0_norm_norm_weight_to_fp16, x = input_5_cast_fp16)[name = string("op_85_cast_fp16")];
+            tensor<int32, [3]> input_7_perm_0 = const()[name = string("input_7_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 64, 1]> m_convnext_0_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_0_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066816)))];
+            tensor<fp16, [256]> m_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_0_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1099648)))];
+            tensor<fp16, [1, 64, 129]> input_7_cast_fp16 = transpose(perm = input_7_perm_0, x = var_85_cast_fp16)[name = string("transpose_22")];
+            tensor<fp16, [1, 256, 129]> h_3_cast_fp16 = conv(bias = m_convnext_0_pwconv1_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = m_convnext_0_pwconv1_weight_to_fp16, x = input_7_cast_fp16)[name = string("h_3_cast_fp16")];
+            string input_9_mode_0 = const()[name = string("input_9_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 256, 129]> input_9_cast_fp16 = gelu(mode = input_9_mode_0, x = h_3_cast_fp16)[name = string("input_9_cast_fp16")];
+            string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 256, 1]> var_102_weight_0_to_fp16 = const()[name = string("op_102_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1100224)))];
+            tensor<fp16, [64]> var_102_bias_0_to_fp16 = const()[name = string("op_102_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1133056)))];
+            tensor<fp16, [1, 64, 129]> var_102_cast_fp16 = conv(bias = var_102_bias_0_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = var_102_weight_0_to_fp16, x = input_9_cast_fp16)[name = string("op_102_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> out_1_cast_fp16 = add(x = input_1_cast_fp16, y = var_102_cast_fp16)[name = string("out_1_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = mask_cast_fp16)[name = string("x_9_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> input_11_cast_fp16 = mul(x = x_9_cast_fp16, y = mask_cast_fp16)[name = string("input_11_cast_fp16")];
+            tensor<int32, [6]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
+            string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("replicate")];
+            fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 64, 133]> input_13_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = input_13_mode_0, pad = input_13_pad_0, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")];
+            string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")];
+            int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(64)];
+            tensor<int32, [1]> h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [64, 1, 5]> m_convnext_1_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_1_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1133248)))];
+            tensor<fp16, [64]> m_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1133952)))];
+            tensor<fp16, [1, 64, 129]> h_7_cast_fp16 = conv(bias = m_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = m_convnext_1_dwconv__conv_weight_to_fp16, x = input_13_cast_fp16)[name = string("h_7_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_11_cast_fp16 = mul(x = h_7_cast_fp16, y = mask_cast_fp16)[name = string("x_11_cast_fp16")];
+            tensor<int32, [3]> input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_127_axes_0 = const()[name = string("op_127_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [64]> m_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_1_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1134144)))];
+            tensor<fp16, [64]> m_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_1_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1134336)))];
+            tensor<fp16, [1, 129, 64]> input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = x_11_cast_fp16)[name = string("transpose_21")];
+            tensor<fp16, [1, 129, 64]> var_127_cast_fp16 = layer_norm(axes = var_127_axes_0, beta = m_convnext_1_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_1_norm_norm_weight_to_fp16, x = input_15_cast_fp16)[name = string("op_127_cast_fp16")];
+            tensor<int32, [3]> input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 64, 1]> m_convnext_1_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_1_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1134528)))];
+            tensor<fp16, [256]> m_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_1_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1167360)))];
+            tensor<fp16, [1, 64, 129]> input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = var_127_cast_fp16)[name = string("transpose_20")];
+            tensor<fp16, [1, 256, 129]> h_9_cast_fp16 = conv(bias = m_convnext_1_pwconv1_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = m_convnext_1_pwconv1_weight_to_fp16, x = input_17_cast_fp16)[name = string("h_9_cast_fp16")];
+            string input_19_mode_0 = const()[name = string("input_19_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 256, 129]> input_19_cast_fp16 = gelu(mode = input_19_mode_0, x = h_9_cast_fp16)[name = string("input_19_cast_fp16")];
+            string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 256, 1]> var_144_weight_0_to_fp16 = const()[name = string("op_144_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1167936)))];
+            tensor<fp16, [64]> var_144_bias_0_to_fp16 = const()[name = string("op_144_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1200768)))];
+            tensor<fp16, [1, 64, 129]> var_144_cast_fp16 = conv(bias = var_144_bias_0_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = var_144_weight_0_to_fp16, x = input_19_cast_fp16)[name = string("op_144_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> out_3_cast_fp16 = add(x = input_11_cast_fp16, y = var_144_cast_fp16)[name = string("out_3_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_13_cast_fp16 = mul(x = out_3_cast_fp16, y = mask_cast_fp16)[name = string("x_13_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> input_21_cast_fp16 = mul(x = x_13_cast_fp16, y = mask_cast_fp16)[name = string("input_21_cast_fp16")];
+            tensor<int32, [6]> input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
+            string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")];
+            fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 64, 133]> input_23_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")];
+            string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")];
+            int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(64)];
+            tensor<int32, [1]> h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [64, 1, 5]> m_convnext_2_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_2_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1200960)))];
+            tensor<fp16, [64]> m_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1201664)))];
+            tensor<fp16, [1, 64, 129]> h_13_cast_fp16 = conv(bias = m_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = m_convnext_2_dwconv__conv_weight_to_fp16, x = input_23_cast_fp16)[name = string("h_13_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_15_cast_fp16 = mul(x = h_13_cast_fp16, y = mask_cast_fp16)[name = string("x_15_cast_fp16")];
+            tensor<int32, [3]> input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_169_axes_0 = const()[name = string("op_169_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [64]> m_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_2_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1201856)))];
+            tensor<fp16, [64]> m_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_2_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1202048)))];
+            tensor<fp16, [1, 129, 64]> input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_15_cast_fp16)[name = string("transpose_19")];
+            tensor<fp16, [1, 129, 64]> var_169_cast_fp16 = layer_norm(axes = var_169_axes_0, beta = m_convnext_2_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_2_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_169_cast_fp16")];
+            tensor<int32, [3]> input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 64, 1]> m_convnext_2_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_2_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1202240)))];
+            tensor<fp16, [256]> m_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_2_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1235072)))];
+            tensor<fp16, [1, 64, 129]> input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_169_cast_fp16)[name = string("transpose_18")];
+            tensor<fp16, [1, 256, 129]> h_15_cast_fp16 = conv(bias = m_convnext_2_pwconv1_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = m_convnext_2_pwconv1_weight_to_fp16, x = input_27_cast_fp16)[name = string("h_15_cast_fp16")];
+            string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 256, 129]> input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_15_cast_fp16)[name = string("input_29_cast_fp16")];
+            string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 256, 1]> var_186_weight_0_to_fp16 = const()[name = string("op_186_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1235648)))];
+            tensor<fp16, [64]> var_186_bias_0_to_fp16 = const()[name = string("op_186_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1268480)))];
+            tensor<fp16, [1, 64, 129]> var_186_cast_fp16 = conv(bias = var_186_bias_0_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = var_186_weight_0_to_fp16, x = input_29_cast_fp16)[name = string("op_186_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> out_5_cast_fp16 = add(x = input_21_cast_fp16, y = var_186_cast_fp16)[name = string("out_5_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_17_cast_fp16 = mul(x = out_5_cast_fp16, y = mask_cast_fp16)[name = string("x_17_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> input_31_cast_fp16 = mul(x = x_17_cast_fp16, y = mask_cast_fp16)[name = string("input_31_cast_fp16")];
+            tensor<int32, [6]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
+            string input_33_mode_0 = const()[name = string("input_33_mode_0"), val = string("replicate")];
+            fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 64, 133]> input_33_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_33_mode_0, pad = input_33_pad_0, x = input_31_cast_fp16)[name = string("input_33_cast_fp16")];
+            string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")];
+            int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(64)];
+            tensor<int32, [1]> h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [64, 1, 5]> m_convnext_3_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_3_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1268672)))];
+            tensor<fp16, [64]> m_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269376)))];
+            tensor<fp16, [1, 64, 129]> h_19_cast_fp16 = conv(bias = m_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = m_convnext_3_dwconv__conv_weight_to_fp16, x = input_33_cast_fp16)[name = string("h_19_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_19_cast_fp16 = mul(x = h_19_cast_fp16, y = mask_cast_fp16)[name = string("x_19_cast_fp16")];
+            tensor<int32, [3]> input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_211_axes_0 = const()[name = string("op_211_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [64]> m_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_3_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269568)))];
+            tensor<fp16, [64]> m_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_3_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269760)))];
+            tensor<fp16, [1, 129, 64]> input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = x_19_cast_fp16)[name = string("transpose_17")];
+            tensor<fp16, [1, 129, 64]> var_211_cast_fp16 = layer_norm(axes = var_211_axes_0, beta = m_convnext_3_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_3_norm_norm_weight_to_fp16, x = input_35_cast_fp16)[name = string("op_211_cast_fp16")];
+            tensor<int32, [3]> input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 64, 1]> m_convnext_3_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_3_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269952)))];
+            tensor<fp16, [256]> m_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_3_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1302784)))];
+            tensor<fp16, [1, 64, 129]> input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = var_211_cast_fp16)[name = string("transpose_16")];
+            tensor<fp16, [1, 256, 129]> h_21_cast_fp16 = conv(bias = m_convnext_3_pwconv1_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = m_convnext_3_pwconv1_weight_to_fp16, x = input_37_cast_fp16)[name = string("h_21_cast_fp16")];
+            string input_39_mode_0 = const()[name = string("input_39_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 256, 129]> input_39_cast_fp16 = gelu(mode = input_39_mode_0, x = h_21_cast_fp16)[name = string("input_39_cast_fp16")];
+            string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 256, 1]> var_228_weight_0_to_fp16 = const()[name = string("op_228_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1303360)))];
+            tensor<fp16, [64]> var_228_bias_0_to_fp16 = const()[name = string("op_228_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1336192)))];
+            tensor<fp16, [1, 64, 129]> var_228_cast_fp16 = conv(bias = var_228_bias_0_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = var_228_weight_0_to_fp16, x = input_39_cast_fp16)[name = string("op_228_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> out_7_cast_fp16 = add(x = input_31_cast_fp16, y = var_228_cast_fp16)[name = string("out_7_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_21_cast_fp16 = mul(x = out_7_cast_fp16, y = mask_cast_fp16)[name = string("x_21_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> input_41_cast_fp16 = mul(x = x_21_cast_fp16, y = mask_cast_fp16)[name = string("input_41_cast_fp16")];
+            tensor<int32, [6]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
+            string input_43_mode_0 = const()[name = string("input_43_mode_0"), val = string("replicate")];
+            fp16 const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 64, 133]> input_43_cast_fp16 = pad(constant_val = const_5_to_fp16, mode = input_43_mode_0, pad = input_43_pad_0, x = input_41_cast_fp16)[name = string("input_43_cast_fp16")];
+            string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")];
+            int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(64)];
+            tensor<int32, [1]> h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [64, 1, 5]> m_convnext_4_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_4_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1336384)))];
+            tensor<fp16, [64]> m_convnext_4_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_4_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337088)))];
+            tensor<fp16, [1, 64, 129]> h_25_cast_fp16 = conv(bias = m_convnext_4_dwconv__conv_bias_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = m_convnext_4_dwconv__conv_weight_to_fp16, x = input_43_cast_fp16)[name = string("h_25_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_23_cast_fp16 = mul(x = h_25_cast_fp16, y = mask_cast_fp16)[name = string("x_23_cast_fp16")];
+            tensor<int32, [3]> input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_253_axes_0 = const()[name = string("op_253_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [64]> m_convnext_4_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_4_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337280)))];
+            tensor<fp16, [64]> m_convnext_4_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_4_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337472)))];
+            tensor<fp16, [1, 129, 64]> input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_23_cast_fp16)[name = string("transpose_15")];
+            tensor<fp16, [1, 129, 64]> var_253_cast_fp16 = layer_norm(axes = var_253_axes_0, beta = m_convnext_4_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_4_norm_norm_weight_to_fp16, x = input_45_cast_fp16)[name = string("op_253_cast_fp16")];
+            tensor<int32, [3]> input_47_perm_0 = const()[name = string("input_47_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 64, 1]> m_convnext_4_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_4_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337664)))];
+            tensor<fp16, [256]> m_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_4_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1370496)))];
+            tensor<fp16, [1, 64, 129]> input_47_cast_fp16 = transpose(perm = input_47_perm_0, x = var_253_cast_fp16)[name = string("transpose_14")];
+            tensor<fp16, [1, 256, 129]> h_27_cast_fp16 = conv(bias = m_convnext_4_pwconv1_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = m_convnext_4_pwconv1_weight_to_fp16, x = input_47_cast_fp16)[name = string("h_27_cast_fp16")];
+            string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 256, 129]> input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = h_27_cast_fp16)[name = string("input_49_cast_fp16")];
+            string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 256, 1]> var_270_weight_0_to_fp16 = const()[name = string("op_270_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1371072)))];
+            tensor<fp16, [64]> var_270_bias_0_to_fp16 = const()[name = string("op_270_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1403904)))];
+            tensor<fp16, [1, 64, 129]> var_270_cast_fp16 = conv(bias = var_270_bias_0_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = var_270_weight_0_to_fp16, x = input_49_cast_fp16)[name = string("op_270_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> out_9_cast_fp16 = add(x = input_41_cast_fp16, y = var_270_cast_fp16)[name = string("out_9_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_25_cast_fp16 = mul(x = out_9_cast_fp16, y = mask_cast_fp16)[name = string("x_25_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> input_51_cast_fp16 = mul(x = x_25_cast_fp16, y = mask_cast_fp16)[name = string("input_51_cast_fp16")];
+            tensor<int32, [6]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
+            string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("replicate")];
+            fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 64, 133]> input_53_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_53_mode_0, pad = input_53_pad_0, x = input_51_cast_fp16)[name = string("input_53_cast_fp16")];
+            string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")];
+            int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(64)];
+            tensor<int32, [1]> h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [64, 1, 5]> m_convnext_5_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_5_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1404096)))];
+            tensor<fp16, [64]> m_convnext_5_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_5_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1404800)))];
+            tensor<fp16, [1, 64, 129]> h_31_cast_fp16 = conv(bias = m_convnext_5_dwconv__conv_bias_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = m_convnext_5_dwconv__conv_weight_to_fp16, x = input_53_cast_fp16)[name = string("h_31_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_27_cast_fp16 = mul(x = h_31_cast_fp16, y = mask_cast_fp16)[name = string("x_27_cast_fp16")];
+            tensor<int32, [3]> input_55_perm_0 = const()[name = string("input_55_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_295_axes_0 = const()[name = string("op_295_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [64]> m_convnext_5_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_5_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1404992)))];
+            tensor<fp16, [64]> m_convnext_5_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_5_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405184)))];
+            tensor<fp16, [1, 129, 64]> input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_27_cast_fp16)[name = string("transpose_13")];
+            tensor<fp16, [1, 129, 64]> var_295_cast_fp16 = layer_norm(axes = var_295_axes_0, beta = m_convnext_5_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_5_norm_norm_weight_to_fp16, x = input_55_cast_fp16)[name = string("op_295_cast_fp16")];
+            tensor<int32, [3]> input_57_perm_0 = const()[name = string("input_57_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 64, 1]> m_convnext_5_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_5_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405376)))];
+            tensor<fp16, [256]> m_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_5_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1438208)))];
+            tensor<fp16, [1, 64, 129]> input_57_cast_fp16 = transpose(perm = input_57_perm_0, x = var_295_cast_fp16)[name = string("transpose_12")];
+            tensor<fp16, [1, 256, 129]> h_33_cast_fp16 = conv(bias = m_convnext_5_pwconv1_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = m_convnext_5_pwconv1_weight_to_fp16, x = input_57_cast_fp16)[name = string("h_33_cast_fp16")];
+            string input_59_mode_0 = const()[name = string("input_59_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 256, 129]> input_59_cast_fp16 = gelu(mode = input_59_mode_0, x = h_33_cast_fp16)[name = string("input_59_cast_fp16")];
+            string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 256, 1]> var_312_weight_0_to_fp16 = const()[name = string("op_312_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1438784)))];
+            tensor<fp16, [64]> var_312_bias_0_to_fp16 = const()[name = string("op_312_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1471616)))];
+            tensor<fp16, [1, 64, 129]> var_312_cast_fp16 = conv(bias = var_312_bias_0_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = var_312_weight_0_to_fp16, x = input_59_cast_fp16)[name = string("op_312_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> out_11_cast_fp16 = add(x = input_51_cast_fp16, y = var_312_cast_fp16)[name = string("out_11_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_29_cast_fp16 = mul(x = out_11_cast_fp16, y = mask_cast_fp16)[name = string("x_29_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = mask_cast_fp16)[name = string("x_31_cast_fp16")];
+            string var_335_pad_type_0 = const()[name = string("op_335_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_335_strides_0 = const()[name = string("op_335_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_335_pad_0 = const()[name = string("op_335_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_335_dilations_0 = const()[name = string("op_335_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_335_groups_0 = const()[name = string("op_335_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 1]> m_attn_layers_0_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_q_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1471808)))];
+            tensor<fp16, [64]> m_attn_layers_0_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_q_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1480064)))];
+            tensor<fp16, [1, 64, 129]> var_335_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_q_bias_to_fp16, dilations = var_335_dilations_0, groups = var_335_groups_0, pad = var_335_pad_0, pad_type = var_335_pad_type_0, strides = var_335_strides_0, weight = m_attn_layers_0_attn_conv_q_weight_to_fp16, x = x_31_cast_fp16)[name = string("op_335_cast_fp16")];
+            tensor<int32, [4]> var_336 = const()[name = string("op_336"), val = tensor<int32, [4]>([1, 2, 32, 129])];
+            tensor<fp16, [1, 2, 32, 129]> var_337_cast_fp16 = reshape(shape = var_336, x = var_335_cast_fp16)[name = string("op_337_cast_fp16")];
+            tensor<int32, [4]> q_1_perm_0 = const()[name = string("q_1_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
+            string var_345_pad_type_0 = const()[name = string("op_345_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_345_strides_0 = const()[name = string("op_345_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_345_pad_0 = const()[name = string("op_345_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_345_dilations_0 = const()[name = string("op_345_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_345_groups_0 = const()[name = string("op_345_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 1]> m_attn_layers_0_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_k_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1480256)))];
+            tensor<fp16, [64]> m_attn_layers_0_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_k_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1488512)))];
+            tensor<fp16, [1, 64, 129]> var_345_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_k_bias_to_fp16, dilations = var_345_dilations_0, groups = var_345_groups_0, pad = var_345_pad_0, pad_type = var_345_pad_type_0, strides = var_345_strides_0, weight = m_attn_layers_0_attn_conv_k_weight_to_fp16, x = x_31_cast_fp16)[name = string("op_345_cast_fp16")];
+            tensor<int32, [4]> var_346 = const()[name = string("op_346"), val = tensor<int32, [4]>([1, 2, 32, 129])];
+            tensor<fp16, [1, 2, 32, 129]> var_347_cast_fp16 = reshape(shape = var_346, x = var_345_cast_fp16)[name = string("op_347_cast_fp16")];
+            string var_355_pad_type_0 = const()[name = string("op_355_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_355_strides_0 = const()[name = string("op_355_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_355_pad_0 = const()[name = string("op_355_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_355_dilations_0 = const()[name = string("op_355_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_355_groups_0 = const()[name = string("op_355_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 1]> m_attn_layers_0_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_v_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1488704)))];
+            tensor<fp16, [64]> m_attn_layers_0_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_v_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1496960)))];
+            tensor<fp16, [1, 64, 129]> var_355_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_v_bias_to_fp16, dilations = var_355_dilations_0, groups = var_355_groups_0, pad = var_355_pad_0, pad_type = var_355_pad_type_0, strides = var_355_strides_0, weight = m_attn_layers_0_attn_conv_v_weight_to_fp16, x = x_31_cast_fp16)[name = string("op_355_cast_fp16")];
+            tensor<int32, [4]> var_356 = const()[name = string("op_356"), val = tensor<int32, [4]>([1, 2, 32, 129])];
+            tensor<fp16, [1, 2, 32, 129]> var_357_cast_fp16 = reshape(shape = var_356, x = var_355_cast_fp16)[name = string("op_357_cast_fp16")];
+            fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1.6ap-3)];
+            tensor<fp16, [1, 2, 129, 32]> q_1_cast_fp16 = transpose(perm = q_1_perm_0, x = var_337_cast_fp16)[name = string("transpose_11")];
+            tensor<fp16, [1, 2, 129, 32]> var_360_cast_fp16 = mul(x = q_1_cast_fp16, y = var_359_to_fp16)[name = string("op_360_cast_fp16")];
+            bool scores_1_transpose_x_0 = const()[name = string("scores_1_transpose_x_0"), val = bool(false)];
+            bool scores_1_transpose_y_0 = const()[name = string("scores_1_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 2, 129, 129]> scores_1_cast_fp16 = matmul(transpose_x = scores_1_transpose_x_0, transpose_y = scores_1_transpose_y_0, x = var_360_cast_fp16, y = var_347_cast_fp16)[name = string("scores_1_cast_fp16")];
+            bool x_33_transpose_x_0 = const()[name = string("x_33_transpose_x_0"), val = bool(false)];
+            bool x_33_transpose_y_0 = const()[name = string("x_33_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 1, 32, 257]> var_384_to_fp16 = const()[name = string("op_384_to_fp16"), val = tensor<fp16, [1, 1, 32, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1497152)))];
+            tensor<fp16, [1, 2, 129, 257]> x_33_cast_fp16 = matmul(transpose_x = x_33_transpose_x_0, transpose_y = x_33_transpose_y_0, x = var_360_cast_fp16, y = var_384_to_fp16)[name = string("x_33_cast_fp16")];
+            tensor<int32, [8]> x_35_pad_0 = const()[name = string("x_35_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 1])];
+            string x_35_mode_0 = const()[name = string("x_35_mode_0"), val = string("constant")];
+            fp16 const_13_to_fp16 = const()[name = string("const_13_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 2, 129, 258]> x_35_cast_fp16 = pad(constant_val = const_13_to_fp16, mode = x_35_mode_0, pad = x_35_pad_0, x = x_33_cast_fp16)[name = string("x_35_cast_fp16")];
+            tensor<int32, [3]> var_396 = const()[name = string("op_396"), val = tensor<int32, [3]>([1, 2, 33282])];
+            tensor<fp16, [1, 2, 33282]> input_63_cast_fp16 = reshape(shape = var_396, x = x_35_cast_fp16)[name = string("input_63_cast_fp16")];
+            tensor<int32, [6]> x_37_pad_0 = const()[name = string("x_37_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 128])];
+            string x_37_mode_0 = const()[name = string("x_37_mode_0"), val = string("constant")];
+            fp16 const_14_to_fp16 = const()[name = string("const_14_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 2, 33410]> x_37_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = x_37_mode_0, pad = x_37_pad_0, x = input_63_cast_fp16)[name = string("x_37_cast_fp16")];
+            tensor<int32, [4]> var_411 = const()[name = string("op_411"), val = tensor<int32, [4]>([1, 2, 130, 257])];
+            tensor<fp16, [1, 2, 130, 257]> x_39_cast_fp16 = reshape(shape = var_411, x = x_37_cast_fp16)[name = string("x_39_cast_fp16")];
+            tensor<int32, [4]> var_418_begin_0 = const()[name = string("op_418_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> var_418_end_0 = const()[name = string("op_418_end_0"), val = tensor<int32, [4]>([1, 2, 129, 257])];
+            tensor<bool, [4]> var_418_end_mask_0 = const()[name = string("op_418_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
+            tensor<fp16, [1, 2, 129, 257]> var_418_cast_fp16 = slice_by_index(begin = var_418_begin_0, end = var_418_end_0, end_mask = var_418_end_mask_0, x = x_39_cast_fp16)[name = string("op_418_cast_fp16")];
+            tensor<int32, [4]> var_419_begin_0 = const()[name = string("op_419_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 128])];
+            tensor<int32, [4]> var_419_end_0 = const()[name = string("op_419_end_0"), val = tensor<int32, [4]>([1, 2, 129, 257])];
+            tensor<bool, [4]> var_419_end_mask_0 = const()[name = string("op_419_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 2, 129, 129]> var_419_cast_fp16 = slice_by_index(begin = var_419_begin_0, end = var_419_end_0, end_mask = var_419_end_mask_0, x = var_418_cast_fp16)[name = string("op_419_cast_fp16")];
+            tensor<fp16, [1, 2, 129, 129]> scores_3_cast_fp16 = add(x = scores_1_cast_fp16, y = var_419_cast_fp16)[name = string("scores_3_cast_fp16")];
+            tensor<int32, [1]> var_421_axes_0 = const()[name = string("op_421_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 1, 1, 129]> var_421_cast_fp16 = expand_dims(axes = var_421_axes_0, x = mask_cast_fp16)[name = string("op_421_cast_fp16")];
+            fp16 var_10_to_fp16 = const()[name = string("op_10_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [1, 1, 1, 129]> var_422_cast_fp16 = sub(x = var_10_to_fp16, y = var_421_cast_fp16)[name = string("op_422_cast_fp16")];
+            fp16 var_423_to_fp16 = const()[name = string("op_423_to_fp16"), val = fp16(0x1.388p+13)];
+            tensor<fp16, [1, 1, 1, 129]> var_424_cast_fp16 = mul(x = var_422_cast_fp16, y = var_423_to_fp16)[name = string("op_424_cast_fp16")];
+            tensor<fp16, [1, 2, 129, 129]> input_65_cast_fp16 = sub(x = scores_3_cast_fp16, y = var_424_cast_fp16)[name = string("input_65_cast_fp16")];
+            tensor<fp16, [1, 2, 129, 129]> p_attn_1_cast_fp16 = softmax(axis = var_27, x = input_65_cast_fp16)[name = string("p_attn_1_cast_fp16")];
+            bool out_13_transpose_x_1 = const()[name = string("out_13_transpose_x_1"), val = bool(false)];
+            bool out_13_transpose_y_1 = const()[name = string("out_13_transpose_y_1"), val = bool(true)];
+            tensor<fp16, [1, 2, 129, 32]> out_13_cast_fp16 = matmul(transpose_x = out_13_transpose_x_1, transpose_y = out_13_transpose_y_1, x = p_attn_1_cast_fp16, y = var_357_cast_fp16)[name = string("out_13_cast_fp16")];
+            tensor<int32, [8]> x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 128])];
+            string x_41_mode_0 = const()[name = string("x_41_mode_0"), val = string("constant")];
+            fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 2, 129, 257]> x_41_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = x_41_mode_0, pad = x_41_pad_0, x = p_attn_1_cast_fp16)[name = string("x_41_cast_fp16")];
+            tensor<int32, [3]> var_461 = const()[name = string("op_461"), val = tensor<int32, [3]>([1, 2, 33153])];
+            tensor<fp16, [1, 2, 33153]> input_69_cast_fp16 = reshape(shape = var_461, x = x_41_cast_fp16)[name = string("input_69_cast_fp16")];
+            tensor<int32, [6]> x_43_pad_0 = const()[name = string("x_43_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 129, 0])];
+            string x_43_mode_0 = const()[name = string("x_43_mode_0"), val = string("constant")];
+            fp16 const_20_to_fp16 = const()[name = string("const_20_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 2, 33282]> x_43_cast_fp16 = pad(constant_val = const_20_to_fp16, mode = x_43_mode_0, pad = x_43_pad_0, x = input_69_cast_fp16)[name = string("x_43_cast_fp16")];
+            tensor<int32, [4]> var_468 = const()[name = string("op_468"), val = tensor<int32, [4]>([1, 2, 129, 258])];
+            tensor<fp16, [1, 2, 129, 258]> x_45_cast_fp16 = reshape(shape = var_468, x = x_43_cast_fp16)[name = string("x_45_cast_fp16")];
+            tensor<int32, [4]> rel_weights_1_begin_0 = const()[name = string("rel_weights_1_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
+            tensor<int32, [4]> rel_weights_1_end_0 = const()[name = string("rel_weights_1_end_0"), val = tensor<int32, [4]>([1, 2, 129, 258])];
+            tensor<bool, [4]> rel_weights_1_end_mask_0 = const()[name = string("rel_weights_1_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 2, 129, 257]> rel_weights_1_cast_fp16 = slice_by_index(begin = rel_weights_1_begin_0, end = rel_weights_1_end_0, end_mask = rel_weights_1_end_mask_0, x = x_45_cast_fp16)[name = string("rel_weights_1_cast_fp16")];
+            bool var_475_transpose_x_0 = const()[name = string("op_475_transpose_x_0"), val = bool(false)];
+            bool var_475_transpose_y_0 = const()[name = string("op_475_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 1, 257, 32]> var_474_to_fp16 = const()[name = string("op_474_to_fp16"), val = tensor<fp16, [1, 1, 257, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1513664)))];
+            tensor<fp16, [1, 2, 129, 32]> var_475_cast_fp16 = matmul(transpose_x = var_475_transpose_x_0, transpose_y = var_475_transpose_y_0, x = rel_weights_1_cast_fp16, y = var_474_to_fp16)[name = string("op_475_cast_fp16")];
+            tensor<fp16, [1, 2, 129, 32]> out_15_cast_fp16 = add(x = out_13_cast_fp16, y = var_475_cast_fp16)[name = string("out_15_cast_fp16")];
+            tensor<int32, [4]> var_477_perm_0 = const()[name = string("op_477_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
+            tensor<int32, [3]> var_478 = const()[name = string("op_478"), val = tensor<int32, [3]>([1, 64, 129])];
+            tensor<fp16, [1, 2, 32, 129]> var_477_cast_fp16 = transpose(perm = var_477_perm_0, x = out_15_cast_fp16)[name = string("transpose_10")];
+            tensor<fp16, [1, 64, 129]> input_71_cast_fp16 = reshape(shape = var_478, x = var_477_cast_fp16)[name = string("input_71_cast_fp16")];
+            string y_1_pad_type_0 = const()[name = string("y_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> y_1_strides_0 = const()[name = string("y_1_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> y_1_pad_0 = const()[name = string("y_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> y_1_dilations_0 = const()[name = string("y_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 y_1_groups_0 = const()[name = string("y_1_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 1]> m_attn_layers_0_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_o_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530176)))];
+            tensor<fp16, [64]> m_attn_layers_0_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_o_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1538432)))];
+            tensor<fp16, [1, 64, 129]> y_1_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_o_bias_to_fp16, dilations = y_1_dilations_0, groups = y_1_groups_0, pad = y_1_pad_0, pad_type = y_1_pad_type_0, strides = y_1_strides_0, weight = m_attn_layers_0_attn_conv_o_weight_to_fp16, x = input_71_cast_fp16)[name = string("y_1_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_47_cast_fp16 = add(x = x_31_cast_fp16, y = y_1_cast_fp16)[name = string("x_47_cast_fp16")];
+            tensor<int32, [3]> input_73_perm_0 = const()[name = string("input_73_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_493_axes_0 = const()[name = string("op_493_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [64]> m_attn_layers_0_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_0_norm_1_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1538624)))];
+            tensor<fp16, [64]> m_attn_layers_0_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_0_norm_1_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1538816)))];
+            tensor<fp16, [1, 129, 64]> input_73_cast_fp16 = transpose(perm = input_73_perm_0, x = x_47_cast_fp16)[name = string("transpose_9")];
+            tensor<fp16, [1, 129, 64]> var_493_cast_fp16 = layer_norm(axes = var_493_axes_0, beta = m_attn_layers_0_norm_1_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_0_norm_1_norm_weight_to_fp16, x = input_73_cast_fp16)[name = string("op_493_cast_fp16")];
+            tensor<int32, [3]> x_49_perm_0 = const()[name = string("x_49_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 64, 129]> x_49_cast_fp16 = transpose(perm = x_49_perm_0, x = var_493_cast_fp16)[name = string("transpose_8")];
+            tensor<fp16, [1, 64, 129]> input_75_cast_fp16 = mul(x = x_49_cast_fp16, y = mask_cast_fp16)[name = string("input_75_cast_fp16")];
+            string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 64, 1]> m_attn_layers_0_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1539008)))];
+            tensor<fp16, [256]> m_attn_layers_0_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1571840)))];
+            tensor<fp16, [1, 256, 129]> input_77_cast_fp16 = conv(bias = m_attn_layers_0_ffn_conv_1_bias_to_fp16, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = m_attn_layers_0_ffn_conv_1_weight_to_fp16, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")];
+            tensor<fp16, [1, 256, 129]> h_37_cast_fp16 = relu(x = input_77_cast_fp16)[name = string("h_37_cast_fp16")];
+            tensor<fp16, [1, 256, 129]> input_79_cast_fp16 = mul(x = h_37_cast_fp16, y = mask_cast_fp16)[name = string("input_79_cast_fp16")];
+            string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 256, 1]> m_attn_layers_0_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_2_weight_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1572416)))];
+            tensor<fp16, [64]> m_attn_layers_0_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_2_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605248)))];
+            tensor<fp16, [1, 64, 129]> h_39_cast_fp16 = conv(bias = m_attn_layers_0_ffn_conv_2_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = m_attn_layers_0_ffn_conv_2_weight_to_fp16, x = input_79_cast_fp16)[name = string("h_39_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> y_3_cast_fp16 = mul(x = h_39_cast_fp16, y = mask_cast_fp16)[name = string("y_3_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_51_cast_fp16 = add(x = x_49_cast_fp16, y = y_3_cast_fp16)[name = string("x_51_cast_fp16")];
+            tensor<int32, [3]> input_81_perm_0 = const()[name = string("input_81_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_521_axes_0 = const()[name = string("op_521_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [64]> m_attn_layers_0_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_0_norm_2_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605440)))];
+            tensor<fp16, [64]> m_attn_layers_0_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_0_norm_2_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605632)))];
+            tensor<fp16, [1, 129, 64]> input_81_cast_fp16 = transpose(perm = input_81_perm_0, x = x_51_cast_fp16)[name = string("transpose_7")];
+            tensor<fp16, [1, 129, 64]> var_521_cast_fp16 = layer_norm(axes = var_521_axes_0, beta = m_attn_layers_0_norm_2_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_0_norm_2_norm_weight_to_fp16, x = input_81_cast_fp16)[name = string("op_521_cast_fp16")];
+            tensor<int32, [3]> x_53_perm_0 = const()[name = string("x_53_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 64, 129]> x_53_cast_fp16 = transpose(perm = x_53_perm_0, x = var_521_cast_fp16)[name = string("transpose_6")];
+            tensor<fp16, [1, 64, 129]> x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = mask_cast_fp16)[name = string("x_55_cast_fp16")];
+            string var_543_pad_type_0 = const()[name = string("op_543_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_543_strides_0 = const()[name = string("op_543_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_543_pad_0 = const()[name = string("op_543_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_543_dilations_0 = const()[name = string("op_543_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_543_groups_0 = const()[name = string("op_543_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 1]> m_attn_layers_1_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_q_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605824)))];
+            tensor<fp16, [64]> m_attn_layers_1_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_q_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1614080)))];
+            tensor<fp16, [1, 64, 129]> var_543_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_q_bias_to_fp16, dilations = var_543_dilations_0, groups = var_543_groups_0, pad = var_543_pad_0, pad_type = var_543_pad_type_0, strides = var_543_strides_0, weight = m_attn_layers_1_attn_conv_q_weight_to_fp16, x = x_55_cast_fp16)[name = string("op_543_cast_fp16")];
+            tensor<int32, [4]> var_544 = const()[name = string("op_544"), val = tensor<int32, [4]>([1, 2, 32, 129])];
+            tensor<fp16, [1, 2, 32, 129]> var_545_cast_fp16 = reshape(shape = var_544, x = var_543_cast_fp16)[name = string("op_545_cast_fp16")];
+            tensor<int32, [4]> q_perm_0 = const()[name = string("q_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
+            string var_553_pad_type_0 = const()[name = string("op_553_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_553_strides_0 = const()[name = string("op_553_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_553_pad_0 = const()[name = string("op_553_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_553_dilations_0 = const()[name = string("op_553_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_553_groups_0 = const()[name = string("op_553_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 1]> m_attn_layers_1_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_k_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1614272)))];
+            tensor<fp16, [64]> m_attn_layers_1_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_k_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1622528)))];
+            tensor<fp16, [1, 64, 129]> var_553_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_k_bias_to_fp16, dilations = var_553_dilations_0, groups = var_553_groups_0, pad = var_553_pad_0, pad_type = var_553_pad_type_0, strides = var_553_strides_0, weight = m_attn_layers_1_attn_conv_k_weight_to_fp16, x = x_55_cast_fp16)[name = string("op_553_cast_fp16")];
+            tensor<int32, [4]> var_554 = const()[name = string("op_554"), val = tensor<int32, [4]>([1, 2, 32, 129])];
+            tensor<fp16, [1, 2, 32, 129]> var_555_cast_fp16 = reshape(shape = var_554, x = var_553_cast_fp16)[name = string("op_555_cast_fp16")];
+            string var_563_pad_type_0 = const()[name = string("op_563_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_563_strides_0 = const()[name = string("op_563_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_563_pad_0 = const()[name = string("op_563_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_563_dilations_0 = const()[name = string("op_563_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_563_groups_0 = const()[name = string("op_563_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 1]> m_attn_layers_1_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_v_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1622720)))];
+            tensor<fp16, [64]> m_attn_layers_1_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_v_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1630976)))];
+            tensor<fp16, [1, 64, 129]> var_563_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_v_bias_to_fp16, dilations = var_563_dilations_0, groups = var_563_groups_0, pad = var_563_pad_0, pad_type = var_563_pad_type_0, strides = var_563_strides_0, weight = m_attn_layers_1_attn_conv_v_weight_to_fp16, x = x_55_cast_fp16)[name = string("op_563_cast_fp16")];
+            tensor<int32, [4]> var_564 = const()[name = string("op_564"), val = tensor<int32, [4]>([1, 2, 32, 129])];
+            tensor<fp16, [1, 2, 32, 129]> var_565_cast_fp16 = reshape(shape = var_564, x = var_563_cast_fp16)[name = string("op_565_cast_fp16")];
+            fp16 var_567_to_fp16 = const()[name = string("op_567_to_fp16"), val = fp16(0x1.6ap-3)];
+            tensor<fp16, [1, 2, 129, 32]> q_cast_fp16 = transpose(perm = q_perm_0, x = var_545_cast_fp16)[name = string("transpose_5")];
+            tensor<fp16, [1, 2, 129, 32]> var_568_cast_fp16 = mul(x = q_cast_fp16, y = var_567_to_fp16)[name = string("op_568_cast_fp16")];
+            bool scores_5_transpose_x_0 = const()[name = string("scores_5_transpose_x_0"), val = bool(false)];
+            bool scores_5_transpose_y_0 = const()[name = string("scores_5_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 2, 129, 129]> scores_5_cast_fp16 = matmul(transpose_x = scores_5_transpose_x_0, transpose_y = scores_5_transpose_y_0, x = var_568_cast_fp16, y = var_555_cast_fp16)[name = string("scores_5_cast_fp16")];
+            bool x_57_transpose_x_0 = const()[name = string("x_57_transpose_x_0"), val = bool(false)];
+            bool x_57_transpose_y_0 = const()[name = string("x_57_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 1, 32, 257]> var_592_to_fp16 = const()[name = string("op_592_to_fp16"), val = tensor<fp16, [1, 1, 32, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1631168)))];
+            tensor<fp16, [1, 2, 129, 257]> x_57_cast_fp16 = matmul(transpose_x = x_57_transpose_x_0, transpose_y = x_57_transpose_y_0, x = var_568_cast_fp16, y = var_592_to_fp16)[name = string("x_57_cast_fp16")];
+            tensor<int32, [8]> x_59_pad_0 = const()[name = string("x_59_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 1])];
+            string x_59_mode_0 = const()[name = string("x_59_mode_0"), val = string("constant")];
+            fp16 const_27_to_fp16 = const()[name = string("const_27_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 2, 129, 258]> x_59_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = x_59_mode_0, pad = x_59_pad_0, x = x_57_cast_fp16)[name = string("x_59_cast_fp16")];
+            tensor<int32, [3]> var_604 = const()[name = string("op_604"), val = tensor<int32, [3]>([1, 2, 33282])];
+            tensor<fp16, [1, 2, 33282]> input_85_cast_fp16 = reshape(shape = var_604, x = x_59_cast_fp16)[name = string("input_85_cast_fp16")];
+            tensor<int32, [6]> x_61_pad_0 = const()[name = string("x_61_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 128])];
+            string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("constant")];
+            fp16 const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 2, 33410]> x_61_cast_fp16 = pad(constant_val = const_28_to_fp16, mode = x_61_mode_0, pad = x_61_pad_0, x = input_85_cast_fp16)[name = string("x_61_cast_fp16")];
+            tensor<int32, [4]> var_619 = const()[name = string("op_619"), val = tensor<int32, [4]>([1, 2, 130, 257])];
+            tensor<fp16, [1, 2, 130, 257]> x_63_cast_fp16 = reshape(shape = var_619, x = x_61_cast_fp16)[name = string("x_63_cast_fp16")];
+            tensor<int32, [4]> var_626_begin_0 = const()[name = string("op_626_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> var_626_end_0 = const()[name = string("op_626_end_0"), val = tensor<int32, [4]>([1, 2, 129, 257])];
+            tensor<bool, [4]> var_626_end_mask_0 = const()[name = string("op_626_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
+            tensor<fp16, [1, 2, 129, 257]> var_626_cast_fp16 = slice_by_index(begin = var_626_begin_0, end = var_626_end_0, end_mask = var_626_end_mask_0, x = x_63_cast_fp16)[name = string("op_626_cast_fp16")];
+            tensor<int32, [4]> var_627_begin_0 = const()[name = string("op_627_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 128])];
+            tensor<int32, [4]> var_627_end_0 = const()[name = string("op_627_end_0"), val = tensor<int32, [4]>([1, 2, 129, 257])];
+            tensor<bool, [4]> var_627_end_mask_0 = const()[name = string("op_627_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 2, 129, 129]> var_627_cast_fp16 = slice_by_index(begin = var_627_begin_0, end = var_627_end_0, end_mask = var_627_end_mask_0, x = var_626_cast_fp16)[name = string("op_627_cast_fp16")];
+            tensor<fp16, [1, 2, 129, 129]> scores_cast_fp16 = add(x = scores_5_cast_fp16, y = var_627_cast_fp16)[name = string("scores_cast_fp16")];
+            tensor<fp16, [1, 2, 129, 129]> input_87_cast_fp16 = sub(x = scores_cast_fp16, y = var_424_cast_fp16)[name = string("input_87_cast_fp16")];
+            tensor<fp16, [1, 2, 129, 129]> p_attn_cast_fp16 = softmax(axis = var_27, x = input_87_cast_fp16)[name = string("p_attn_cast_fp16")];
+            bool out_17_transpose_x_1 = const()[name = string("out_17_transpose_x_1"), val = bool(false)];
+            bool out_17_transpose_y_1 = const()[name = string("out_17_transpose_y_1"), val = bool(true)];
+            tensor<fp16, [1, 2, 129, 32]> out_17_cast_fp16 = matmul(transpose_x = out_17_transpose_x_1, transpose_y = out_17_transpose_y_1, x = p_attn_cast_fp16, y = var_565_cast_fp16)[name = string("out_17_cast_fp16")];
+            tensor<int32, [8]> x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 128])];
+            string x_65_mode_0 = const()[name = string("x_65_mode_0"), val = string("constant")];
+            fp16 const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 2, 129, 257]> x_65_cast_fp16 = pad(constant_val = const_33_to_fp16, mode = x_65_mode_0, pad = x_65_pad_0, x = p_attn_cast_fp16)[name = string("x_65_cast_fp16")];
+            tensor<int32, [3]> var_669 = const()[name = string("op_669"), val = tensor<int32, [3]>([1, 2, 33153])];
+            tensor<fp16, [1, 2, 33153]> input_91_cast_fp16 = reshape(shape = var_669, x = x_65_cast_fp16)[name = string("input_91_cast_fp16")];
+            tensor<int32, [6]> x_67_pad_0 = const()[name = string("x_67_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 129, 0])];
+            string x_67_mode_0 = const()[name = string("x_67_mode_0"), val = string("constant")];
+            fp16 const_34_to_fp16 = const()[name = string("const_34_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 2, 33282]> x_67_cast_fp16 = pad(constant_val = const_34_to_fp16, mode = x_67_mode_0, pad = x_67_pad_0, x = input_91_cast_fp16)[name = string("x_67_cast_fp16")];
+            tensor<int32, [4]> var_676 = const()[name = string("op_676"), val = tensor<int32, [4]>([1, 2, 129, 258])];
+            tensor<fp16, [1, 2, 129, 258]> x_69_cast_fp16 = reshape(shape = var_676, x = x_67_cast_fp16)[name = string("x_69_cast_fp16")];
+            tensor<int32, [4]> rel_weights_begin_0 = const()[name = string("rel_weights_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
+            tensor<int32, [4]> rel_weights_end_0 = const()[name = string("rel_weights_end_0"), val = tensor<int32, [4]>([1, 2, 129, 258])];
+            tensor<bool, [4]> rel_weights_end_mask_0 = const()[name = string("rel_weights_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 2, 129, 257]> rel_weights_cast_fp16 = slice_by_index(begin = rel_weights_begin_0, end = rel_weights_end_0, end_mask = rel_weights_end_mask_0, x = x_69_cast_fp16)[name = string("rel_weights_cast_fp16")];
+            bool var_683_transpose_x_0 = const()[name = string("op_683_transpose_x_0"), val = bool(false)];
+            bool var_683_transpose_y_0 = const()[name = string("op_683_transpose_y_0"), val = bool(false)];
+            tensor<fp16, [1, 1, 257, 32]> var_682_to_fp16 = const()[name = string("op_682_to_fp16"), val = tensor<fp16, [1, 1, 257, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1647680)))];
+            tensor<fp16, [1, 2, 129, 32]> var_683_cast_fp16 = matmul(transpose_x = var_683_transpose_x_0, transpose_y = var_683_transpose_y_0, x = rel_weights_cast_fp16, y = var_682_to_fp16)[name = string("op_683_cast_fp16")];
+            tensor<fp16, [1, 2, 129, 32]> out_cast_fp16 = add(x = out_17_cast_fp16, y = var_683_cast_fp16)[name = string("out_cast_fp16")];
+            tensor<int32, [4]> var_685_perm_0 = const()[name = string("op_685_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
+            tensor<int32, [3]> var_686 = const()[name = string("op_686"), val = tensor<int32, [3]>([1, 64, 129])];
+            tensor<fp16, [1, 2, 32, 129]> var_685_cast_fp16 = transpose(perm = var_685_perm_0, x = out_cast_fp16)[name = string("transpose_4")];
+            tensor<fp16, [1, 64, 129]> input_93_cast_fp16 = reshape(shape = var_686, x = var_685_cast_fp16)[name = string("input_93_cast_fp16")];
+            string y_5_pad_type_0 = const()[name = string("y_5_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> y_5_strides_0 = const()[name = string("y_5_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> y_5_pad_0 = const()[name = string("y_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> y_5_dilations_0 = const()[name = string("y_5_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 y_5_groups_0 = const()[name = string("y_5_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 1]> m_attn_layers_1_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_o_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1664192)))];
+            tensor<fp16, [64]> m_attn_layers_1_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_o_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672448)))];
+            tensor<fp16, [1, 64, 129]> y_5_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_o_bias_to_fp16, dilations = y_5_dilations_0, groups = y_5_groups_0, pad = y_5_pad_0, pad_type = y_5_pad_type_0, strides = y_5_strides_0, weight = m_attn_layers_1_attn_conv_o_weight_to_fp16, x = input_93_cast_fp16)[name = string("y_5_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_71_cast_fp16 = add(x = x_55_cast_fp16, y = y_5_cast_fp16)[name = string("x_71_cast_fp16")];
+            tensor<int32, [3]> input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_701_axes_0 = const()[name = string("op_701_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [64]> m_attn_layers_1_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_1_norm_1_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672640)))];
+            tensor<fp16, [64]> m_attn_layers_1_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_1_norm_1_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832)))];
+            tensor<fp16, [1, 129, 64]> input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_71_cast_fp16)[name = string("transpose_3")];
+            tensor<fp16, [1, 129, 64]> var_701_cast_fp16 = layer_norm(axes = var_701_axes_0, beta = m_attn_layers_1_norm_1_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_1_norm_1_norm_weight_to_fp16, x = input_95_cast_fp16)[name = string("op_701_cast_fp16")];
+            tensor<int32, [3]> x_73_perm_0 = const()[name = string("x_73_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 64, 129]> x_73_cast_fp16 = transpose(perm = x_73_perm_0, x = var_701_cast_fp16)[name = string("transpose_2")];
+            tensor<fp16, [1, 64, 129]> input_97_cast_fp16 = mul(x = x_73_cast_fp16, y = mask_cast_fp16)[name = string("input_97_cast_fp16")];
+            string input_99_pad_type_0 = const()[name = string("input_99_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_99_strides_0 = const()[name = string("input_99_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_99_pad_0 = const()[name = string("input_99_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_99_dilations_0 = const()[name = string("input_99_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_99_groups_0 = const()[name = string("input_99_groups_0"), val = int32(1)];
+            tensor<fp16, [256, 64, 1]> m_attn_layers_1_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673024)))];
+            tensor<fp16, [256]> m_attn_layers_1_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1705856)))];
+            tensor<fp16, [1, 256, 129]> input_99_cast_fp16 = conv(bias = m_attn_layers_1_ffn_conv_1_bias_to_fp16, dilations = input_99_dilations_0, groups = input_99_groups_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = m_attn_layers_1_ffn_conv_1_weight_to_fp16, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")];
+            tensor<fp16, [1, 256, 129]> h_41_cast_fp16 = relu(x = input_99_cast_fp16)[name = string("h_41_cast_fp16")];
+            tensor<fp16, [1, 256, 129]> input_101_cast_fp16 = mul(x = h_41_cast_fp16, y = mask_cast_fp16)[name = string("input_101_cast_fp16")];
+            string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_strides_0 = const()[name = string("h_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_pad_0 = const()[name = string("h_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 256, 1]> m_attn_layers_1_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_2_weight_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1706432)))];
+            tensor<fp16, [64]> m_attn_layers_1_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_2_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739264)))];
+            tensor<fp16, [1, 64, 129]> h_cast_fp16 = conv(bias = m_attn_layers_1_ffn_conv_2_bias_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = m_attn_layers_1_ffn_conv_2_weight_to_fp16, x = input_101_cast_fp16)[name = string("h_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> y_cast_fp16 = mul(x = h_cast_fp16, y = mask_cast_fp16)[name = string("y_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_75_cast_fp16 = add(x = x_73_cast_fp16, y = y_cast_fp16)[name = string("x_75_cast_fp16")];
+            tensor<int32, [3]> input_103_perm_0 = const()[name = string("input_103_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_729_axes_0 = const()[name = string("op_729_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [64]> m_attn_layers_1_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_1_norm_2_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739456)))];
+            tensor<fp16, [64]> m_attn_layers_1_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_1_norm_2_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739648)))];
+            tensor<fp16, [1, 129, 64]> input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_75_cast_fp16)[name = string("transpose_1")];
+            tensor<fp16, [1, 129, 64]> var_729_cast_fp16 = layer_norm(axes = var_729_axes_0, beta = m_attn_layers_1_norm_2_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_1_norm_2_norm_weight_to_fp16, x = input_103_cast_fp16)[name = string("op_729_cast_fp16")];
+            tensor<int32, [3]> x_77_perm_0 = const()[name = string("x_77_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<fp16, [1, 64, 129]> x_77_cast_fp16 = transpose(perm = x_77_perm_0, x = var_729_cast_fp16)[name = string("transpose_0")];
+            tensor<fp16, [1, 64, 129]> x_79_cast_fp16 = mul(x = x_77_cast_fp16, y = mask_cast_fp16)[name = string("x_79_cast_fp16")];
+            tensor<fp16, [1, 64, 129]> x_81_cast_fp16 = add(x = x_79_cast_fp16, y = x_29_cast_fp16)[name = string("x_81_cast_fp16")];
+            tensor<int32, [3]> input_105_begin_0 = const()[name = string("input_105_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> input_105_end_0 = const()[name = string("input_105_end_0"), val = tensor<int32, [3]>([1, 64, 1])];
+            tensor<bool, [3]> input_105_end_mask_0 = const()[name = string("input_105_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<fp16, [1, 64, 1]> input_105_cast_fp16 = slice_by_index(begin = input_105_begin_0, end = input_105_end_0, end_mask = input_105_end_mask_0, x = x_81_cast_fp16)[name = string("input_105_cast_fp16")];
+            tensor<int32, [3]> sentence_mask_begin_0 = const()[name = string("sentence_mask_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
+            tensor<int32, [3]> sentence_mask_end_0 = const()[name = string("sentence_mask_end_0"), val = tensor<int32, [3]>([1, 1, 1])];
+            tensor<bool, [3]> sentence_mask_end_mask_0 = const()[name = string("sentence_mask_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
+            tensor<fp16, [1, 1, 1]> sentence_mask_cast_fp16 = slice_by_index(begin = sentence_mask_begin_0, end = sentence_mask_end_0, end_mask = sentence_mask_end_mask_0, x = mask_cast_fp16)[name = string("sentence_mask_cast_fp16")];
+            string var_744_pad_type_0 = const()[name = string("op_744_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_744_strides_0 = const()[name = string("op_744_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_744_pad_0 = const()[name = string("op_744_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_744_dilations_0 = const()[name = string("op_744_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_744_groups_0 = const()[name = string("op_744_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 64, 1]> m_proj_out_conv_weight_to_fp16 = const()[name = string("m_proj_out_conv_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739840)))];
+            tensor<fp16, [1, 64, 1]> var_744_cast_fp16 = conv(dilations = var_744_dilations_0, groups = var_744_groups_0, pad = var_744_pad_0, pad_type = var_744_pad_type_0, strides = var_744_strides_0, weight = m_proj_out_conv_weight_to_fp16, x = input_105_cast_fp16)[name = string("op_744_cast_fp16")];
+            tensor<fp16, [1, 64, 1]> sentence_emb_cast_fp16 = mul(x = var_744_cast_fp16, y = sentence_mask_cast_fp16)[name = string("sentence_emb_cast_fp16")];
+            tensor<int32, [2]> var_752 = const()[name = string("op_752"), val = tensor<int32, [2]>([1, -1])];
+            tensor<fp16, [1, 64]> s_cast_fp16 = reshape(shape = var_752, x = sentence_emb_cast_fp16)[name = string("s_cast_fp16")];
+            tensor<int32, [2]> var_754 = const()[name = string("op_754"), val = tensor<int32, [2]>([1, -1])];
+            string style_dp_to_fp16_dtype_0 = const()[name = string("style_dp_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1, 8, 16]> style_dp_to_fp16 = cast(dtype = style_dp_to_fp16_dtype_0, x = style_dp)[name = string("cast_21")];
+            tensor<fp16, [1, 128]> v_cast_fp16 = reshape(shape = var_754, x = style_dp_to_fp16)[name = string("v_cast_fp16")];
+            bool input_107_interleave_0 = const()[name = string("input_107_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 192]> input_107_cast_fp16 = concat(axis = var_26, interleave = input_107_interleave_0, values = (s_cast_fp16, v_cast_fp16))[name = string("input_107_cast_fp16")];
+            tensor<fp16, [128, 192]> m_predictor_layers_0_weight_to_fp16 = const()[name = string("m_predictor_layers_0_weight_to_fp16"), val = tensor<fp16, [128, 192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1748096)))];
+            tensor<fp16, [128]> m_predictor_layers_0_bias_to_fp16 = const()[name = string("m_predictor_layers_0_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797312)))];
+            tensor<fp16, [1, 128]> linear_0_cast_fp16 = linear(bias = m_predictor_layers_0_bias_to_fp16, weight = m_predictor_layers_0_weight_to_fp16, x = input_107_cast_fp16)[name = string("linear_0_cast_fp16")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 128, 1]> expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = linear_0_cast_fp16)[name = string("expand_dims_0_cast_fp16")];
+            fp32 prelu_0_alpha_1 = const()[name = string("prelu_0_alpha_1"), val = fp32(0x1.b19f5ap-3)];
+            tensor<fp16, [1, 128, 1]> prelu_0_cast_fp16 = leaky_relu(alpha = prelu_0_alpha_1, x = expand_dims_0_cast_fp16)[name = string("prelu_0_cast_fp16")];
+            tensor<int32, [1]> input_axes_0 = const()[name = string("input_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 128]> input_cast_fp16 = squeeze(axes = input_axes_0, x = prelu_0_cast_fp16)[name = string("input_cast_fp16")];
+            tensor<fp16, [1, 128]> m_predictor_layers_1_weight_to_fp16 = const()[name = string("m_predictor_layers_1_weight_to_fp16"), val = tensor<fp16, [1, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797632)))];
+            tensor<fp16, [1]> m_predictor_layers_1_bias_to_fp16 = const()[name = string("m_predictor_layers_1_bias_to_fp16"), val = tensor<fp16, [1]>([0x1.60cp-6])];
+            tensor<fp16, [1, 1]> linear_1_cast_fp16 = linear(bias = m_predictor_layers_1_bias_to_fp16, weight = m_predictor_layers_1_weight_to_fp16, x = input_cast_fp16)[name = string("linear_1_cast_fp16")];
+            tensor<fp16, [1, 1]> x_cast_fp16 = exp(x = linear_1_cast_fp16)[name = string("x_cast_fp16")];
+            tensor<int32, [1]> var_767_axes_0 = const()[name = string("op_767_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1]> var_767_cast_fp16 = squeeze(axes = var_767_axes_0, x = x_cast_fp16)[name = string("op_767_cast_fp16")];
+            string var_767_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_767_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [1]> duration = cast(dtype = var_767_cast_fp16_to_fp32_dtype_0, x = var_767_cast_fp16)[name = string("cast_20")];
+        } -> (duration);
+}

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+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "51A57480-22F2-40DD-9B71-02466AEB3397": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "6B8FFD11-C3E0-4F96-B464-7BC65C9BAE5A": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "6B8FFD11-C3E0-4F96-B464-7BC65C9BAE5A"
+}

README.md

@@ -0,0 +1,202 @@
+---
+license: openrail++
+track_downloads: true
+language:
+- en
+- ko
+- ja
+- ar
+- bg
+- cs
+- da
+- de
+- el
+- es
+- et
+- fi
+- fr
+- hi
+- hr
+- hu
+- id
+- it
+- lt
+- lv
+- nl
+- pl
+- pt
+- ro
+- ru
+- sk
+- sl
+- sv
+- tr
+- uk
+- vi
+pipeline_tag: text-to-speech
+library_name: coreml
+datasets: []
+thumbnail: null
+tags:
+- text-to-speech
+- speech
+- audio
+- tts
+- coreml
+- ane
+- apple-silicon
+- flow-matching
+- diffusion
+- multilingual
+- supertonic
+base_model:
+- Supertone/supertonic-3
+---
+
+# **<span style="color:#5DAF8D"> 🧃 supertonic-3: Multilingual Text-to-Speech CoreML </span>**
+
+<style>
+img {
+ display: inline;
+}
+</style>
+
+[![Model architecture](https://img.shields.io/badge/Model_Arch-Flow--Matching%20Diffusion-blue#model-badge)](#model-architecture)
+| [![Sampling rate](https://img.shields.io/badge/Sample_Rate-44.1kHz-green#model-badge)](#model-details)
+| [![Language](https://img.shields.io/badge/Languages-31-blue#model-badge)](#supported-languages)
+| [![Discord](https://img.shields.io/badge/Discord-Join%20Chat-7289da.svg)](https://discord.gg/WNsvaCtmDe)
+| [![GitHub Repo stars](https://img.shields.io/github/stars/FluidInference/FluidAudio?style=flat&logo=github)](https://github.com/FluidInference/FluidAudio)
+
+On‑device multilingual TTS model converted to Core ML for Apple platforms.
+This is a hand‑port of [Supertone Supertonic‑3 v1.7.3](https://huggingface.co/Supertone/supertonic-3)
+from ONNX → PyTorch → Core ML, suitable for FluidAudio's TTS pipeline on
+macOS/iOS. 31 languages, 44.1 kHz output, flow‑matching diffusion with
+classifier‑free guidance (8 denoising steps).
+
+The conversion script is here:
+https://github.com/FluidInference/mobius/tree/main/models/tts/supertonic-3/coreml
+
+And the FluidAudio integration is here:
+https://github.com/FluidInference/FluidAudio/tree/main/Sources/FluidAudio/TTS/Supertonic3
+
+## Highlights
+
+- **Core ML**: Runs on‑device (ANE + CPU) on Apple Silicon.
+- **Multilingual**: 31 languages — see [Supported Languages](#supported-languages).
+- **High quality**: 44.1 kHz output via flow‑matching diffusion + ConvNeXt vocoder.
+- **Voice styling**: zero‑shot voice style embeddings (single JSON per voice).
+- **Performance**: end‑to‑end RTFx ≈ 8.5× on M2 (CoreML), ≈ 17–19× on M2 with current ANE assignment (3 of 4 modules on ANE).
+- **Privacy**: No network calls required once models are downloaded.
+
+## Intended Use
+
+- **Batch TTS** for full text segments on macOS/iOS.
+- **Local voice synthesis** for note‑taking, accessibility, and creative tools.
+- **Embedded TTS** in production apps via the FluidAudio Swift framework.
+
+## Supported Platforms
+
+- macOS 14+ (Apple Silicon recommended)
+- iOS 17+
+
+## Model Details
+
+- **Architecture**: Supertonic‑3 v1.7.3 — 4‑stage pipeline:
+  1. `text_encoder` — token embeddings → contextual text features `[B, 256, T]`.
+  2. `duration_predictor` — predicts utterance duration from text features.
+  3. `vector_estimator` — flow‑matching diffusion in latent space
+     (8 steps, classifier‑free guidance via batch‑2 duplication, ConvNeXt + cross‑attention to text + style attention).
+  4. `vocoder` — ConvNeXt decoder → 44.1 kHz waveform.
+- **Output audio**: 44.1 kHz mono, Float32 PCM.
+- **Languages**: 31 (see below).
+- **Precision**: FP16 weights and activations (mlprogram, iOS 18+ minimum deployment target).
+- **Granularity**: vocoder frame ≈ 11.6 ms; latent tick ≈ 69.7 ms.
+
+## Supported Languages
+
+English, Korean, Japanese, Arabic, Bulgarian, Czech, Danish, German, Greek,
+Spanish, Estonian, Finnish, French, Hindi, Croatian, Hungarian, Indonesian,
+Italian, Lithuanian, Latvian, Dutch, Polish, Portuguese, Romanian, Russian,
+Slovak, Slovenian, Swedish, Turkish, Ukrainian, Vietnamese.
+
+## Performance (Apple M2, macOS 26.5, FP16)
+
+| Module             | Size  | Predict | Compute placement |
+| ------------------ | ----- | ------- | ----------------- |
+| duration_predictor | 1.8 MB| 0.82 ms | CPU (tiny)        |
+| text_encoder       | 17 MB | 2.15 ms | 62 % ANE          |
+| vocoder            | 48 MB | 1.17 ms | 100 % ANE         |
+| vector_estimator   | 122 MB| 9.29 ms | CPU + GPU (see notes) |
+
+End‑to‑end on M2: ≈ 0.74 s to synthesize 6.32 s of audio for a single English
+sentence (RTFx ≈ 8.5×), 8 denoising steps. Output verified against
+FluidAudio Parakeet TDT ASR.
+
+**Note on `vector_estimator`**: 100 % of its ops are ANE‑eligible after
+the float‑mask + precompute refactor, but Apple's ANECCompile currently
+returns opaque error 11 on this graph and silently falls back to CPU/GPU.
+See `coreml/trials.md` in the conversion repo for the full investigation.
+
+## Files
+
+Both `.mlpackage` (Core ML source bundle, includes weights + spec) and the
+precompiled `.mlmodelc` (ready for direct `MLModel(contentsOf:)` load) are
+shipped — use `.mlmodelc` to skip the on‑device compile step on first load.
+
+- `TextEncoder.mlpackage` / `TextEncoder.mlmodelc`               — fixed `T=128` text input.
+- `DurationPredictor.mlpackage` / `DurationPredictor.mlmodelc`   — fixed `T=128` text input.
+- `VectorEstimator.mlpackage` / `VectorEstimator.mlmodelc`       — `latent.L` and `text.T` as RangeDim(17..512).
+- `Vocoder.mlpackage` / `Vocoder.mlmodelc`                       — `latent.L_ttl` as RangeDim(4..512).
+- `tts.json`                     — token / text frontend configuration.
+- `unicode_indexer.json`         — Unicode → token id mapping (multilingual frontend).
+- `voice_styles/M1.json`         — example voice style embedding (single male reference).
+- `manifest.json`                — file inventory (sha256 + sizes) for both `.mlpackage` and `.mlmodelc`.
+
+## Usage
+
+For quickest integration, use the FluidAudio Swift framework which handles
+model loading, text frontend, and the diffusion / vocoder loop.
+
+### Swift (FluidAudio)
+
+```swift
+import AVFoundation
+import FluidAudio
+
+Task {
+    // Download and load Supertonic-3 models (first run only)
+    let models = try await Supertonic3Models.downloadAndLoad()
+
+    // Initialize the TTS manager
+    let tts = Supertonic3Manager(config: .default)
+    try await tts.initialize(models: models)
+
+    // Synthesize speech for some text with a voice style
+    let style = try VoiceStyle.load(path: "voice_styles/M1.json")
+    let audio = try await tts.synthesize(text: "Hello, world.", style: style)
+
+    // audio.samples is 44.1 kHz Float32 PCM in [-1, 1]
+    try AudioWriter.writeWav(audio.samples, sampleRate: 44_100, to: "hello.wav")
+
+    tts.cleanup()
+}
+```
+
+For more examples (including CLI usage and benchmarking), see the FluidAudio
+repository: https://github.com/FluidInference/FluidAudio
+
+## Limitations
+
+- 44.1 kHz output is high quality but heavier than 16/22.05 kHz TTS — plan
+  for the bandwidth and storage cost.
+- `vector_estimator` currently runs on CPU + GPU instead of ANE due to an
+  Apple‑side ANE compiler limitation (see [Performance](#performance-apple-m2-macos-265-fp16)).
+- Text frontend currently uses fixed `T=128` token windows; longer text
+  must be segmented by the caller.
+
+## License
+
+OpenRAIL‑M (inherited from upstream [Supertone/supertonic-3](https://huggingface.co/Supertone/supertonic-3)).
+The Core ML conversion tooling and FluidAudio integration are MIT‑licensed.
+See the [FluidAudio repository](https://github.com/FluidInference/FluidAudio)
+for details and usage guidance.

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+        }
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Vocoder.mlmodelc/model.mil

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+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 144, ?]> latent) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"latent", [1, 144, 4]}}), ("RangeDims", {{"latent", [[1, 1], [144, 144], [4, 512]]}})))] {
+            string latent_to_fp16_dtype_0 = const()[name = string("latent_to_fp16_dtype_0"), val = string("fp16")];
+            fp16 _inversed_x_1_y_0_to_fp16 = const()[name = string("_inversed_x_1_y_0_to_fp16"), val = fp16(0x1p+2)];
+            tensor<fp16, [1, 144, ?]> latent_to_fp16 = cast(dtype = latent_to_fp16_dtype_0, x = latent)[name = string("cast_6")];
+            tensor<fp16, [1, 144, ?]> _inversed_x_1_cast_fp16 = mul(x = latent_to_fp16, y = _inversed_x_1_y_0_to_fp16)[name = string("_inversed_x_1_cast_fp16")];
+            tensor<int32, [4]> var_33 = const()[name = string("op_33"), val = tensor<int32, [4]>([1, 24, 6, -1])];
+            tensor<fp16, [1, 24, 6, ?]> x_3_cast_fp16 = reshape(shape = var_33, x = _inversed_x_1_cast_fp16)[name = string("x_3_cast_fp16")];
+            tensor<int32, [4]> var_37_perm_0 = const()[name = string("op_37_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
+            tensor<int32, [3]> var_43 = const()[name = string("op_43"), val = tensor<int32, [3]>([1, 24, -1])];
+            tensor<fp16, [1, 24, ?, 6]> var_37_cast_fp16 = transpose(perm = var_37_perm_0, x = x_3_cast_fp16)[name = string("transpose_21")];
+            tensor<fp16, [1, 24, ?]> x_7_cast_fp16 = reshape(shape = var_43, x = var_37_cast_fp16)[name = string("x_7_cast_fp16")];
+            tensor<fp16, [1, 24, 1]> latent_std_to_fp16 = const()[name = string("latent_std_to_fp16"), val = tensor<fp16, [1, 24, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [1, 24, ?]> var_45_cast_fp16 = mul(x = x_7_cast_fp16, y = latent_std_to_fp16)[name = string("op_45_cast_fp16")];
+            tensor<fp16, [1, 24, 1]> latent_mean_to_fp16 = const()[name = string("latent_mean_to_fp16"), val = tensor<fp16, [1, 24, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192)))];
+            tensor<fp16, [1, 24, ?]> input_1_cast_fp16 = add(x = var_45_cast_fp16, y = latent_mean_to_fp16)[name = string("input_1_cast_fp16")];
+            tensor<int32, [6]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])];
+            string input_3_mode_0 = const()[name = string("input_3_mode_0"), val = string("replicate")];
+            fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 24, ?]> input_3_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = input_3_mode_0, pad = input_3_pad_0, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")];
+            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 24, 7]> embed_net_weight_to_fp16 = const()[name = string("embed_net_weight_to_fp16"), val = tensor<fp16, [512, 24, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320)))];
+            tensor<fp16, [512]> embed_net_bias_to_fp16 = const()[name = string("embed_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172416)))];
+            tensor<fp16, [1, 512, ?]> input_5_cast_fp16 = conv(bias = embed_net_bias_to_fp16, dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = embed_net_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")];
+            tensor<int32, [6]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])];
+            string input_7_mode_0 = const()[name = string("input_7_mode_0"), val = string("replicate")];
+            fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 512, ?]> input_7_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_7_mode_0, pad = input_7_pad_0, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")];
+            string x_9_pad_type_0 = const()[name = string("x_9_pad_type_0"), val = string("valid")];
+            int32 x_9_groups_0 = const()[name = string("x_9_groups_0"), val = int32(512)];
+            tensor<int32, [1]> x_9_strides_0 = const()[name = string("x_9_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_9_dilations_0 = const()[name = string("x_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [512, 1, 7]> convnext_0_dwconv_net_weight_to_fp16 = const()[name = string("convnext_0_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173504)))];
+            tensor<fp16, [512]> convnext_0_dwconv_net_bias_to_fp16 = const()[name = string("convnext_0_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180736)))];
+            tensor<fp16, [1, 512, ?]> x_9_cast_fp16 = conv(bias = convnext_0_dwconv_net_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 = convnext_0_dwconv_net_weight_to_fp16, x = input_7_cast_fp16)[name = string("x_9_cast_fp16")];
+            tensor<int32, [3]> input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_96_axes_0 = const()[name = string("op_96_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> convnext_0_norm_norm_weight_to_fp16 = const()[name = string("convnext_0_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181824)))];
+            tensor<fp16, [512]> convnext_0_norm_norm_bias_to_fp16 = const()[name = string("convnext_0_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182912)))];
+            fp16 var_67_to_fp16 = const()[name = string("op_67_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_9_cast_fp16)[name = string("transpose_20")];
+            tensor<fp16, [1, ?, 512]> var_96_cast_fp16 = layer_norm(axes = var_96_axes_0, beta = convnext_0_norm_norm_bias_to_fp16, epsilon = var_67_to_fp16, gamma = convnext_0_norm_norm_weight_to_fp16, x = input_9_cast_fp16)[name = string("op_96_cast_fp16")];
+            tensor<int32, [3]> input_11_perm_0 = const()[name = string("input_11_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_1_pad_type_0 = const()[name = string("h_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_1_strides_0 = const()[name = string("h_1_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_1_pad_0 = const()[name = string("h_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_1_dilations_0 = const()[name = string("h_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_1_groups_0 = const()[name = string("h_1_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 512, 1]> convnext_0_pwconv1_weight_to_fp16 = const()[name = string("convnext_0_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184000)))];
+            tensor<fp16, [2048]> convnext_0_pwconv1_bias_to_fp16 = const()[name = string("convnext_0_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2281216)))];
+            tensor<fp16, [1, 512, ?]> input_11_cast_fp16 = transpose(perm = input_11_perm_0, x = var_96_cast_fp16)[name = string("transpose_19")];
+            tensor<fp16, [1, 2048, ?]> h_1_cast_fp16 = conv(bias = convnext_0_pwconv1_bias_to_fp16, dilations = h_1_dilations_0, groups = h_1_groups_0, pad = h_1_pad_0, pad_type = h_1_pad_type_0, strides = h_1_strides_0, weight = convnext_0_pwconv1_weight_to_fp16, x = input_11_cast_fp16)[name = string("h_1_cast_fp16")];
+            string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 2048, ?]> input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = h_1_cast_fp16)[name = string("input_13_cast_fp16")];
+            string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 2048, 1]> var_113_weight_0_to_fp16 = const()[name = string("op_113_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2285376)))];
+            tensor<fp16, [512]> var_113_bias_0_to_fp16 = const()[name = string("op_113_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4382592)))];
+            tensor<fp16, [1, 512, ?]> var_113_cast_fp16 = conv(bias = var_113_bias_0_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = var_113_weight_0_to_fp16, x = input_13_cast_fp16)[name = string("op_113_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_15_cast_fp16 = add(x = input_5_cast_fp16, y = var_113_cast_fp16)[name = string("input_15_cast_fp16")];
+            tensor<int32, [6]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 12, 0])];
+            string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("replicate")];
+            fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 512, ?]> input_17_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = input_17_mode_0, pad = input_17_pad_0, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")];
+            string x_11_pad_type_0 = const()[name = string("x_11_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_11_dilations_0 = const()[name = string("x_11_dilations_0"), val = tensor<int32, [1]>([2])];
+            int32 x_11_groups_0 = const()[name = string("x_11_groups_0"), val = int32(512)];
+            tensor<int32, [1]> x_11_strides_0 = const()[name = string("x_11_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_11_pad_0 = const()[name = string("x_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<fp16, [512, 1, 7]> convnext_1_dwconv_net_weight_to_fp16 = const()[name = string("convnext_1_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4383680)))];
+            tensor<fp16, [512]> convnext_1_dwconv_net_bias_to_fp16 = const()[name = string("convnext_1_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4390912)))];
+            tensor<fp16, [1, 512, ?]> x_11_cast_fp16 = conv(bias = convnext_1_dwconv_net_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 = convnext_1_dwconv_net_weight_to_fp16, x = input_17_cast_fp16)[name = string("x_11_cast_fp16")];
+            tensor<int32, [3]> input_19_perm_0 = const()[name = string("input_19_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_146_axes_0 = const()[name = string("op_146_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> convnext_1_norm_norm_weight_to_fp16 = const()[name = string("convnext_1_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4392000)))];
+            tensor<fp16, [512]> convnext_1_norm_norm_bias_to_fp16 = const()[name = string("convnext_1_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4393088)))];
+            fp16 var_116_to_fp16 = const()[name = string("op_116_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> input_19_cast_fp16 = transpose(perm = input_19_perm_0, x = x_11_cast_fp16)[name = string("transpose_18")];
+            tensor<fp16, [1, ?, 512]> var_146_cast_fp16 = layer_norm(axes = var_146_axes_0, beta = convnext_1_norm_norm_bias_to_fp16, epsilon = var_116_to_fp16, gamma = convnext_1_norm_norm_weight_to_fp16, x = input_19_cast_fp16)[name = string("op_146_cast_fp16")];
+            tensor<int32, [3]> input_21_perm_0 = const()[name = string("input_21_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 512, 1]> convnext_1_pwconv1_weight_to_fp16 = const()[name = string("convnext_1_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4394176)))];
+            tensor<fp16, [2048]> convnext_1_pwconv1_bias_to_fp16 = const()[name = string("convnext_1_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6491392)))];
+            tensor<fp16, [1, 512, ?]> input_21_cast_fp16 = transpose(perm = input_21_perm_0, x = var_146_cast_fp16)[name = string("transpose_17")];
+            tensor<fp16, [1, 2048, ?]> h_5_cast_fp16 = conv(bias = convnext_1_pwconv1_bias_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = convnext_1_pwconv1_weight_to_fp16, x = input_21_cast_fp16)[name = string("h_5_cast_fp16")];
+            string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 2048, ?]> input_23_cast_fp16 = gelu(mode = input_23_mode_0, x = h_5_cast_fp16)[name = string("input_23_cast_fp16")];
+            string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 2048, 1]> var_163_weight_0_to_fp16 = const()[name = string("op_163_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6495552)))];
+            tensor<fp16, [512]> var_163_bias_0_to_fp16 = const()[name = string("op_163_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8592768)))];
+            tensor<fp16, [1, 512, ?]> var_163_cast_fp16 = conv(bias = var_163_bias_0_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = var_163_weight_0_to_fp16, x = input_23_cast_fp16)[name = string("op_163_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_25_cast_fp16 = add(x = input_15_cast_fp16, y = var_163_cast_fp16)[name = string("input_25_cast_fp16")];
+            tensor<int32, [6]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 24, 0])];
+            string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("replicate")];
+            fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 512, ?]> input_27_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_27_mode_0, pad = input_27_pad_0, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")];
+            string x_13_pad_type_0 = const()[name = string("x_13_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_13_dilations_0 = const()[name = string("x_13_dilations_0"), val = tensor<int32, [1]>([4])];
+            int32 x_13_groups_0 = const()[name = string("x_13_groups_0"), val = int32(512)];
+            tensor<int32, [1]> x_13_strides_0 = const()[name = string("x_13_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_13_pad_0 = const()[name = string("x_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<fp16, [512, 1, 7]> convnext_2_dwconv_net_weight_to_fp16 = const()[name = string("convnext_2_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8593856)))];
+            tensor<fp16, [512]> convnext_2_dwconv_net_bias_to_fp16 = const()[name = string("convnext_2_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8601088)))];
+            tensor<fp16, [1, 512, ?]> x_13_cast_fp16 = conv(bias = convnext_2_dwconv_net_bias_to_fp16, dilations = x_13_dilations_0, groups = x_13_groups_0, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = x_13_strides_0, weight = convnext_2_dwconv_net_weight_to_fp16, x = input_27_cast_fp16)[name = string("x_13_cast_fp16")];
+            tensor<int32, [3]> input_29_perm_0 = const()[name = string("input_29_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_197_axes_0 = const()[name = string("op_197_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> convnext_2_norm_norm_weight_to_fp16 = const()[name = string("convnext_2_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8602176)))];
+            tensor<fp16, [512]> convnext_2_norm_norm_bias_to_fp16 = const()[name = string("convnext_2_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8603264)))];
+            fp16 var_167_to_fp16 = const()[name = string("op_167_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> input_29_cast_fp16 = transpose(perm = input_29_perm_0, x = x_13_cast_fp16)[name = string("transpose_16")];
+            tensor<fp16, [1, ?, 512]> var_197_cast_fp16 = layer_norm(axes = var_197_axes_0, beta = convnext_2_norm_norm_bias_to_fp16, epsilon = var_167_to_fp16, gamma = convnext_2_norm_norm_weight_to_fp16, x = input_29_cast_fp16)[name = string("op_197_cast_fp16")];
+            tensor<int32, [3]> input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 512, 1]> convnext_2_pwconv1_weight_to_fp16 = const()[name = string("convnext_2_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8604352)))];
+            tensor<fp16, [2048]> convnext_2_pwconv1_bias_to_fp16 = const()[name = string("convnext_2_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10701568)))];
+            tensor<fp16, [1, 512, ?]> input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = var_197_cast_fp16)[name = string("transpose_15")];
+            tensor<fp16, [1, 2048, ?]> h_9_cast_fp16 = conv(bias = convnext_2_pwconv1_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = convnext_2_pwconv1_weight_to_fp16, x = input_31_cast_fp16)[name = string("h_9_cast_fp16")];
+            string input_33_mode_0 = const()[name = string("input_33_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 2048, ?]> input_33_cast_fp16 = gelu(mode = input_33_mode_0, x = h_9_cast_fp16)[name = string("input_33_cast_fp16")];
+            string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 2048, 1]> var_214_weight_0_to_fp16 = const()[name = string("op_214_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10705728)))];
+            tensor<fp16, [512]> var_214_bias_0_to_fp16 = const()[name = string("op_214_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12802944)))];
+            tensor<fp16, [1, 512, ?]> var_214_cast_fp16 = conv(bias = var_214_bias_0_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = var_214_weight_0_to_fp16, x = input_33_cast_fp16)[name = string("op_214_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_35_cast_fp16 = add(x = input_25_cast_fp16, y = var_214_cast_fp16)[name = string("input_35_cast_fp16")];
+            tensor<int32, [6]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])];
+            string input_37_mode_0 = const()[name = string("input_37_mode_0"), val = string("replicate")];
+            fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 512, ?]> input_37_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_37_mode_0, pad = input_37_pad_0, x = input_35_cast_fp16)[name = string("input_37_cast_fp16")];
+            string x_15_pad_type_0 = const()[name = string("x_15_pad_type_0"), val = string("valid")];
+            int32 x_15_groups_0 = const()[name = string("x_15_groups_0"), val = int32(512)];
+            tensor<int32, [1]> x_15_strides_0 = const()[name = string("x_15_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_15_pad_0 = const()[name = string("x_15_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_15_dilations_0 = const()[name = string("x_15_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [512, 1, 7]> convnext_3_dwconv_net_weight_to_fp16 = const()[name = string("convnext_3_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12804032)))];
+            tensor<fp16, [512]> convnext_3_dwconv_net_bias_to_fp16 = const()[name = string("convnext_3_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12811264)))];
+            tensor<fp16, [1, 512, ?]> x_15_cast_fp16 = conv(bias = convnext_3_dwconv_net_bias_to_fp16, dilations = x_15_dilations_0, groups = x_15_groups_0, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = x_15_strides_0, weight = convnext_3_dwconv_net_weight_to_fp16, x = input_37_cast_fp16)[name = string("x_15_cast_fp16")];
+            tensor<int32, [3]> input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_247_axes_0 = const()[name = string("op_247_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> convnext_3_norm_norm_weight_to_fp16 = const()[name = string("convnext_3_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12812352)))];
+            tensor<fp16, [512]> convnext_3_norm_norm_bias_to_fp16 = const()[name = string("convnext_3_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12813440)))];
+            fp16 var_218_to_fp16 = const()[name = string("op_218_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = x_15_cast_fp16)[name = string("transpose_14")];
+            tensor<fp16, [1, ?, 512]> var_247_cast_fp16 = layer_norm(axes = var_247_axes_0, beta = convnext_3_norm_norm_bias_to_fp16, epsilon = var_218_to_fp16, gamma = convnext_3_norm_norm_weight_to_fp16, x = input_39_cast_fp16)[name = string("op_247_cast_fp16")];
+            tensor<int32, [3]> input_41_perm_0 = const()[name = string("input_41_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 512, 1]> convnext_3_pwconv1_weight_to_fp16 = const()[name = string("convnext_3_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12814528)))];
+            tensor<fp16, [2048]> convnext_3_pwconv1_bias_to_fp16 = const()[name = string("convnext_3_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14911744)))];
+            tensor<fp16, [1, 512, ?]> input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = var_247_cast_fp16)[name = string("transpose_13")];
+            tensor<fp16, [1, 2048, ?]> h_13_cast_fp16 = conv(bias = convnext_3_pwconv1_bias_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = convnext_3_pwconv1_weight_to_fp16, x = input_41_cast_fp16)[name = string("h_13_cast_fp16")];
+            string input_43_mode_0 = const()[name = string("input_43_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 2048, ?]> input_43_cast_fp16 = gelu(mode = input_43_mode_0, x = h_13_cast_fp16)[name = string("input_43_cast_fp16")];
+            string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 2048, 1]> var_264_weight_0_to_fp16 = const()[name = string("op_264_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14915904)))];
+            tensor<fp16, [512]> var_264_bias_0_to_fp16 = const()[name = string("op_264_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17013120)))];
+            tensor<fp16, [1, 512, ?]> var_264_cast_fp16 = conv(bias = var_264_bias_0_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = var_264_weight_0_to_fp16, x = input_43_cast_fp16)[name = string("op_264_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_45_cast_fp16 = add(x = input_35_cast_fp16, y = var_264_cast_fp16)[name = string("input_45_cast_fp16")];
+            tensor<int32, [6]> input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 12, 0])];
+            string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")];
+            fp16 const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 512, ?]> input_47_cast_fp16 = pad(constant_val = const_5_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")];
+            string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor<int32, [1]>([2])];
+            int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(512)];
+            tensor<int32, [1]> x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<fp16, [512, 1, 7]> convnext_4_dwconv_net_weight_to_fp16 = const()[name = string("convnext_4_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17014208)))];
+            tensor<fp16, [512]> convnext_4_dwconv_net_bias_to_fp16 = const()[name = string("convnext_4_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17021440)))];
+            tensor<fp16, [1, 512, ?]> x_17_cast_fp16 = conv(bias = convnext_4_dwconv_net_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = convnext_4_dwconv_net_weight_to_fp16, x = input_47_cast_fp16)[name = string("x_17_cast_fp16")];
+            tensor<int32, [3]> input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_297_axes_0 = const()[name = string("op_297_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> convnext_4_norm_norm_weight_to_fp16 = const()[name = string("convnext_4_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17022528)))];
+            tensor<fp16, [512]> convnext_4_norm_norm_bias_to_fp16 = const()[name = string("convnext_4_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17023616)))];
+            fp16 var_267_to_fp16 = const()[name = string("op_267_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_17_cast_fp16)[name = string("transpose_12")];
+            tensor<fp16, [1, ?, 512]> var_297_cast_fp16 = layer_norm(axes = var_297_axes_0, beta = convnext_4_norm_norm_bias_to_fp16, epsilon = var_267_to_fp16, gamma = convnext_4_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_297_cast_fp16")];
+            tensor<int32, [3]> input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 512, 1]> convnext_4_pwconv1_weight_to_fp16 = const()[name = string("convnext_4_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17024704)))];
+            tensor<fp16, [2048]> convnext_4_pwconv1_bias_to_fp16 = const()[name = string("convnext_4_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19121920)))];
+            tensor<fp16, [1, 512, ?]> input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_297_cast_fp16)[name = string("transpose_11")];
+            tensor<fp16, [1, 2048, ?]> h_17_cast_fp16 = conv(bias = convnext_4_pwconv1_bias_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = convnext_4_pwconv1_weight_to_fp16, x = input_51_cast_fp16)[name = string("h_17_cast_fp16")];
+            string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 2048, ?]> input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_17_cast_fp16)[name = string("input_53_cast_fp16")];
+            string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 2048, 1]> var_314_weight_0_to_fp16 = const()[name = string("op_314_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19126080)))];
+            tensor<fp16, [512]> var_314_bias_0_to_fp16 = const()[name = string("op_314_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21223296)))];
+            tensor<fp16, [1, 512, ?]> var_314_cast_fp16 = conv(bias = var_314_bias_0_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = var_314_weight_0_to_fp16, x = input_53_cast_fp16)[name = string("op_314_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_55_cast_fp16 = add(x = input_45_cast_fp16, y = var_314_cast_fp16)[name = string("input_55_cast_fp16")];
+            tensor<int32, [6]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 24, 0])];
+            string input_57_mode_0 = const()[name = string("input_57_mode_0"), val = string("replicate")];
+            fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 512, ?]> input_57_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_57_mode_0, pad = input_57_pad_0, x = input_55_cast_fp16)[name = string("input_57_cast_fp16")];
+            string x_19_pad_type_0 = const()[name = string("x_19_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_19_dilations_0 = const()[name = string("x_19_dilations_0"), val = tensor<int32, [1]>([4])];
+            int32 x_19_groups_0 = const()[name = string("x_19_groups_0"), val = int32(512)];
+            tensor<int32, [1]> x_19_strides_0 = const()[name = string("x_19_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_19_pad_0 = const()[name = string("x_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<fp16, [512, 1, 7]> convnext_5_dwconv_net_weight_to_fp16 = const()[name = string("convnext_5_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21224384)))];
+            tensor<fp16, [512]> convnext_5_dwconv_net_bias_to_fp16 = const()[name = string("convnext_5_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21231616)))];
+            tensor<fp16, [1, 512, ?]> x_19_cast_fp16 = conv(bias = convnext_5_dwconv_net_bias_to_fp16, dilations = x_19_dilations_0, groups = x_19_groups_0, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = x_19_strides_0, weight = convnext_5_dwconv_net_weight_to_fp16, x = input_57_cast_fp16)[name = string("x_19_cast_fp16")];
+            tensor<int32, [3]> input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_348_axes_0 = const()[name = string("op_348_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> convnext_5_norm_norm_weight_to_fp16 = const()[name = string("convnext_5_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21232704)))];
+            tensor<fp16, [512]> convnext_5_norm_norm_bias_to_fp16 = const()[name = string("convnext_5_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21233792)))];
+            fp16 var_318_to_fp16 = const()[name = string("op_318_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_19_cast_fp16)[name = string("transpose_10")];
+            tensor<fp16, [1, ?, 512]> var_348_cast_fp16 = layer_norm(axes = var_348_axes_0, beta = convnext_5_norm_norm_bias_to_fp16, epsilon = var_318_to_fp16, gamma = convnext_5_norm_norm_weight_to_fp16, x = input_59_cast_fp16)[name = string("op_348_cast_fp16")];
+            tensor<int32, [3]> input_61_perm_0 = const()[name = string("input_61_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 512, 1]> convnext_5_pwconv1_weight_to_fp16 = const()[name = string("convnext_5_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21234880)))];
+            tensor<fp16, [2048]> convnext_5_pwconv1_bias_to_fp16 = const()[name = string("convnext_5_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23332096)))];
+            tensor<fp16, [1, 512, ?]> input_61_cast_fp16 = transpose(perm = input_61_perm_0, x = var_348_cast_fp16)[name = string("transpose_9")];
+            tensor<fp16, [1, 2048, ?]> h_21_cast_fp16 = conv(bias = convnext_5_pwconv1_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = convnext_5_pwconv1_weight_to_fp16, x = input_61_cast_fp16)[name = string("h_21_cast_fp16")];
+            string input_63_mode_0 = const()[name = string("input_63_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 2048, ?]> input_63_cast_fp16 = gelu(mode = input_63_mode_0, x = h_21_cast_fp16)[name = string("input_63_cast_fp16")];
+            string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 2048, 1]> var_365_weight_0_to_fp16 = const()[name = string("op_365_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23336256)))];
+            tensor<fp16, [512]> var_365_bias_0_to_fp16 = const()[name = string("op_365_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25433472)))];
+            tensor<fp16, [1, 512, ?]> var_365_cast_fp16 = conv(bias = var_365_bias_0_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = var_365_weight_0_to_fp16, x = input_63_cast_fp16)[name = string("op_365_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_65_cast_fp16 = add(x = input_55_cast_fp16, y = var_365_cast_fp16)[name = string("input_65_cast_fp16")];
+            tensor<int32, [6]> input_67_pad_0 = const()[name = string("input_67_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])];
+            string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("replicate")];
+            fp16 const_7_to_fp16 = const()[name = string("const_7_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 512, ?]> input_67_cast_fp16 = pad(constant_val = const_7_to_fp16, mode = input_67_mode_0, pad = input_67_pad_0, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")];
+            string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("valid")];
+            int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(512)];
+            tensor<int32, [1]> x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [512, 1, 7]> convnext_6_dwconv_net_weight_to_fp16 = const()[name = string("convnext_6_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25434560)))];
+            tensor<fp16, [512]> convnext_6_dwconv_net_bias_to_fp16 = const()[name = string("convnext_6_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25441792)))];
+            tensor<fp16, [1, 512, ?]> x_21_cast_fp16 = conv(bias = convnext_6_dwconv_net_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = convnext_6_dwconv_net_weight_to_fp16, x = input_67_cast_fp16)[name = string("x_21_cast_fp16")];
+            tensor<int32, [3]> input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_398_axes_0 = const()[name = string("op_398_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> convnext_6_norm_norm_weight_to_fp16 = const()[name = string("convnext_6_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25442880)))];
+            tensor<fp16, [512]> convnext_6_norm_norm_bias_to_fp16 = const()[name = string("convnext_6_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25443968)))];
+            fp16 var_369_to_fp16 = const()[name = string("op_369_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_21_cast_fp16)[name = string("transpose_8")];
+            tensor<fp16, [1, ?, 512]> var_398_cast_fp16 = layer_norm(axes = var_398_axes_0, beta = convnext_6_norm_norm_bias_to_fp16, epsilon = var_369_to_fp16, gamma = convnext_6_norm_norm_weight_to_fp16, x = input_69_cast_fp16)[name = string("op_398_cast_fp16")];
+            tensor<int32, [3]> input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 512, 1]> convnext_6_pwconv1_weight_to_fp16 = const()[name = string("convnext_6_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25445056)))];
+            tensor<fp16, [2048]> convnext_6_pwconv1_bias_to_fp16 = const()[name = string("convnext_6_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27542272)))];
+            tensor<fp16, [1, 512, ?]> input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = var_398_cast_fp16)[name = string("transpose_7")];
+            tensor<fp16, [1, 2048, ?]> h_25_cast_fp16 = conv(bias = convnext_6_pwconv1_bias_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = convnext_6_pwconv1_weight_to_fp16, x = input_71_cast_fp16)[name = string("h_25_cast_fp16")];
+            string input_73_mode_0 = const()[name = string("input_73_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 2048, ?]> input_73_cast_fp16 = gelu(mode = input_73_mode_0, x = h_25_cast_fp16)[name = string("input_73_cast_fp16")];
+            string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 2048, 1]> var_415_weight_0_to_fp16 = const()[name = string("op_415_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27546432)))];
+            tensor<fp16, [512]> var_415_bias_0_to_fp16 = const()[name = string("op_415_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29643648)))];
+            tensor<fp16, [1, 512, ?]> var_415_cast_fp16 = conv(bias = var_415_bias_0_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = var_415_weight_0_to_fp16, x = input_73_cast_fp16)[name = string("op_415_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_75_cast_fp16 = add(x = input_65_cast_fp16, y = var_415_cast_fp16)[name = string("input_75_cast_fp16")];
+            tensor<int32, [6]> input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])];
+            string input_77_mode_0 = const()[name = string("input_77_mode_0"), val = string("replicate")];
+            fp16 const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 512, ?]> input_77_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = input_77_mode_0, pad = input_77_pad_0, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")];
+            string x_23_pad_type_0 = const()[name = string("x_23_pad_type_0"), val = string("valid")];
+            int32 x_23_groups_0 = const()[name = string("x_23_groups_0"), val = int32(512)];
+            tensor<int32, [1]> x_23_strides_0 = const()[name = string("x_23_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_23_pad_0 = const()[name = string("x_23_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_23_dilations_0 = const()[name = string("x_23_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [512, 1, 7]> convnext_7_dwconv_net_weight_to_fp16 = const()[name = string("convnext_7_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29644736)))];
+            tensor<fp16, [512]> convnext_7_dwconv_net_bias_to_fp16 = const()[name = string("convnext_7_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29651968)))];
+            tensor<fp16, [1, 512, ?]> x_23_cast_fp16 = conv(bias = convnext_7_dwconv_net_bias_to_fp16, dilations = x_23_dilations_0, groups = x_23_groups_0, pad = x_23_pad_0, pad_type = x_23_pad_type_0, strides = x_23_strides_0, weight = convnext_7_dwconv_net_weight_to_fp16, x = input_77_cast_fp16)[name = string("x_23_cast_fp16")];
+            tensor<int32, [3]> input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_448_axes_0 = const()[name = string("op_448_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> convnext_7_norm_norm_weight_to_fp16 = const()[name = string("convnext_7_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29653056)))];
+            tensor<fp16, [512]> convnext_7_norm_norm_bias_to_fp16 = const()[name = string("convnext_7_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29654144)))];
+            fp16 var_419_to_fp16 = const()[name = string("op_419_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_23_cast_fp16)[name = string("transpose_6")];
+            tensor<fp16, [1, ?, 512]> var_448_cast_fp16 = layer_norm(axes = var_448_axes_0, beta = convnext_7_norm_norm_bias_to_fp16, epsilon = var_419_to_fp16, gamma = convnext_7_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_448_cast_fp16")];
+            tensor<int32, [3]> input_81_perm_0 = const()[name = string("input_81_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 512, 1]> convnext_7_pwconv1_weight_to_fp16 = const()[name = string("convnext_7_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29655232)))];
+            tensor<fp16, [2048]> convnext_7_pwconv1_bias_to_fp16 = const()[name = string("convnext_7_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31752448)))];
+            tensor<fp16, [1, 512, ?]> input_81_cast_fp16 = transpose(perm = input_81_perm_0, x = var_448_cast_fp16)[name = string("transpose_5")];
+            tensor<fp16, [1, 2048, ?]> h_29_cast_fp16 = conv(bias = convnext_7_pwconv1_bias_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = convnext_7_pwconv1_weight_to_fp16, x = input_81_cast_fp16)[name = string("h_29_cast_fp16")];
+            string input_83_mode_0 = const()[name = string("input_83_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 2048, ?]> input_83_cast_fp16 = gelu(mode = input_83_mode_0, x = h_29_cast_fp16)[name = string("input_83_cast_fp16")];
+            string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 2048, 1]> var_465_weight_0_to_fp16 = const()[name = string("op_465_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31756608)))];
+            tensor<fp16, [512]> var_465_bias_0_to_fp16 = const()[name = string("op_465_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33853824)))];
+            tensor<fp16, [1, 512, ?]> var_465_cast_fp16 = conv(bias = var_465_bias_0_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = var_465_weight_0_to_fp16, x = input_83_cast_fp16)[name = string("op_465_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_85_cast_fp16 = add(x = input_75_cast_fp16, y = var_465_cast_fp16)[name = string("input_85_cast_fp16")];
+            tensor<int32, [6]> input_87_pad_0 = const()[name = string("input_87_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])];
+            string input_87_mode_0 = const()[name = string("input_87_mode_0"), val = string("replicate")];
+            fp16 const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 512, ?]> input_87_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_87_mode_0, pad = input_87_pad_0, x = input_85_cast_fp16)[name = string("input_87_cast_fp16")];
+            string x_25_pad_type_0 = const()[name = string("x_25_pad_type_0"), val = string("valid")];
+            int32 x_25_groups_0 = const()[name = string("x_25_groups_0"), val = int32(512)];
+            tensor<int32, [1]> x_25_strides_0 = const()[name = string("x_25_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_25_pad_0 = const()[name = string("x_25_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_25_dilations_0 = const()[name = string("x_25_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [512, 1, 7]> convnext_8_dwconv_net_weight_to_fp16 = const()[name = string("convnext_8_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33854912)))];
+            tensor<fp16, [512]> convnext_8_dwconv_net_bias_to_fp16 = const()[name = string("convnext_8_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33862144)))];
+            tensor<fp16, [1, 512, ?]> x_25_cast_fp16 = conv(bias = convnext_8_dwconv_net_bias_to_fp16, dilations = x_25_dilations_0, groups = x_25_groups_0, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = x_25_strides_0, weight = convnext_8_dwconv_net_weight_to_fp16, x = input_87_cast_fp16)[name = string("x_25_cast_fp16")];
+            tensor<int32, [3]> input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_498_axes_0 = const()[name = string("op_498_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> convnext_8_norm_norm_weight_to_fp16 = const()[name = string("convnext_8_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33863232)))];
+            tensor<fp16, [512]> convnext_8_norm_norm_bias_to_fp16 = const()[name = string("convnext_8_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33864320)))];
+            fp16 var_469_to_fp16 = const()[name = string("op_469_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = x_25_cast_fp16)[name = string("transpose_4")];
+            tensor<fp16, [1, ?, 512]> var_498_cast_fp16 = layer_norm(axes = var_498_axes_0, beta = convnext_8_norm_norm_bias_to_fp16, epsilon = var_469_to_fp16, gamma = convnext_8_norm_norm_weight_to_fp16, x = input_89_cast_fp16)[name = string("op_498_cast_fp16")];
+            tensor<int32, [3]> input_91_perm_0 = const()[name = string("input_91_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 512, 1]> convnext_8_pwconv1_weight_to_fp16 = const()[name = string("convnext_8_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33865408)))];
+            tensor<fp16, [2048]> convnext_8_pwconv1_bias_to_fp16 = const()[name = string("convnext_8_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35962624)))];
+            tensor<fp16, [1, 512, ?]> input_91_cast_fp16 = transpose(perm = input_91_perm_0, x = var_498_cast_fp16)[name = string("transpose_3")];
+            tensor<fp16, [1, 2048, ?]> h_33_cast_fp16 = conv(bias = convnext_8_pwconv1_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = convnext_8_pwconv1_weight_to_fp16, x = input_91_cast_fp16)[name = string("h_33_cast_fp16")];
+            string input_93_mode_0 = const()[name = string("input_93_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 2048, ?]> input_93_cast_fp16 = gelu(mode = input_93_mode_0, x = h_33_cast_fp16)[name = string("input_93_cast_fp16")];
+            string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 2048, 1]> var_515_weight_0_to_fp16 = const()[name = string("op_515_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35966784)))];
+            tensor<fp16, [512]> var_515_bias_0_to_fp16 = const()[name = string("op_515_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38064000)))];
+            tensor<fp16, [1, 512, ?]> var_515_cast_fp16 = conv(bias = var_515_bias_0_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = var_515_weight_0_to_fp16, x = input_93_cast_fp16)[name = string("op_515_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_95_cast_fp16 = add(x = input_85_cast_fp16, y = var_515_cast_fp16)[name = string("input_95_cast_fp16")];
+            tensor<int32, [6]> input_97_pad_0 = const()[name = string("input_97_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])];
+            string input_97_mode_0 = const()[name = string("input_97_mode_0"), val = string("replicate")];
+            fp16 const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 512, ?]> input_97_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_97_mode_0, pad = input_97_pad_0, x = input_95_cast_fp16)[name = string("input_97_cast_fp16")];
+            string x_27_pad_type_0 = const()[name = string("x_27_pad_type_0"), val = string("valid")];
+            int32 x_27_groups_0 = const()[name = string("x_27_groups_0"), val = int32(512)];
+            tensor<int32, [1]> x_27_strides_0 = const()[name = string("x_27_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_27_pad_0 = const()[name = string("x_27_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_27_dilations_0 = const()[name = string("x_27_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [512, 1, 7]> convnext_9_dwconv_net_weight_to_fp16 = const()[name = string("convnext_9_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38065088)))];
+            tensor<fp16, [512]> convnext_9_dwconv_net_bias_to_fp16 = const()[name = string("convnext_9_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38072320)))];
+            tensor<fp16, [1, 512, ?]> x_27_cast_fp16 = conv(bias = convnext_9_dwconv_net_bias_to_fp16, dilations = x_27_dilations_0, groups = x_27_groups_0, pad = x_27_pad_0, pad_type = x_27_pad_type_0, strides = x_27_strides_0, weight = convnext_9_dwconv_net_weight_to_fp16, x = input_97_cast_fp16)[name = string("x_27_cast_fp16")];
+            tensor<int32, [3]> input_99_perm_0 = const()[name = string("input_99_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> var_548_axes_0 = const()[name = string("op_548_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [512]> convnext_9_norm_norm_weight_to_fp16 = const()[name = string("convnext_9_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38073408)))];
+            tensor<fp16, [512]> convnext_9_norm_norm_bias_to_fp16 = const()[name = string("convnext_9_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38074496)))];
+            fp16 var_519_to_fp16 = const()[name = string("op_519_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, ?, 512]> input_99_cast_fp16 = transpose(perm = input_99_perm_0, x = x_27_cast_fp16)[name = string("transpose_2")];
+            tensor<fp16, [1, ?, 512]> var_548_cast_fp16 = layer_norm(axes = var_548_axes_0, beta = convnext_9_norm_norm_bias_to_fp16, epsilon = var_519_to_fp16, gamma = convnext_9_norm_norm_weight_to_fp16, x = input_99_cast_fp16)[name = string("op_548_cast_fp16")];
+            tensor<int32, [3]> input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 512, 1]> convnext_9_pwconv1_weight_to_fp16 = const()[name = string("convnext_9_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38075584)))];
+            tensor<fp16, [2048]> convnext_9_pwconv1_bias_to_fp16 = const()[name = string("convnext_9_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40172800)))];
+            tensor<fp16, [1, 512, ?]> input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = var_548_cast_fp16)[name = string("transpose_1")];
+            tensor<fp16, [1, 2048, ?]> h_37_cast_fp16 = conv(bias = convnext_9_pwconv1_bias_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = convnext_9_pwconv1_weight_to_fp16, x = input_101_cast_fp16)[name = string("h_37_cast_fp16")];
+            string input_103_mode_0 = const()[name = string("input_103_mode_0"), val = string("EXACT")];
+            tensor<fp16, [1, 2048, ?]> input_103_cast_fp16 = gelu(mode = input_103_mode_0, x = h_37_cast_fp16)[name = string("input_103_cast_fp16")];
+            string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> h_strides_0 = const()[name = string("h_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> h_pad_0 = const()[name = string("h_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 2048, 1]> var_565_weight_0_to_fp16 = const()[name = string("op_565_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40176960)))];
+            tensor<fp16, [512]> var_565_bias_0_to_fp16 = const()[name = string("op_565_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42274176)))];
+            tensor<fp16, [1, 512, ?]> var_565_cast_fp16 = conv(bias = var_565_bias_0_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = var_565_weight_0_to_fp16, x = input_103_cast_fp16)[name = string("op_565_cast_fp16")];
+            tensor<fp16, [1, 512, ?]> input_105_cast_fp16 = add(x = input_95_cast_fp16, y = var_565_cast_fp16)[name = string("input_105_cast_fp16")];
+            tensor<fp16, [512]> final_norm_norm_running_mean_to_fp16 = const()[name = string("final_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42275264)))];
+            tensor<fp16, [512]> final_norm_norm_running_var_to_fp16 = const()[name = string("final_norm_norm_running_var_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42276352)))];
+            tensor<fp16, [512]> final_norm_norm_weight_to_fp16 = const()[name = string("final_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42277440)))];
+            tensor<fp16, [512]> final_norm_norm_bias_to_fp16 = const()[name = string("final_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42278528)))];
+            fp16 var_569_to_fp16 = const()[name = string("op_569_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [1, 512, ?]> input_107_cast_fp16 = batch_norm(beta = final_norm_norm_bias_to_fp16, epsilon = var_569_to_fp16, gamma = final_norm_norm_weight_to_fp16, mean = final_norm_norm_running_mean_to_fp16, variance = final_norm_norm_running_var_to_fp16, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")];
+            tensor<int32, [6]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
+            string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("replicate")];
+            fp16 const_11_to_fp16 = const()[name = string("const_11_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [1, 512, ?]> input_109_cast_fp16 = pad(constant_val = const_11_to_fp16, mode = input_109_mode_0, pad = input_109_pad_0, x = input_107_cast_fp16)[name = string("input_109_cast_fp16")];
+            string input_111_pad_type_0 = const()[name = string("input_111_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> input_111_strides_0 = const()[name = string("input_111_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_111_pad_0 = const()[name = string("input_111_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_111_dilations_0 = const()[name = string("input_111_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 input_111_groups_0 = const()[name = string("input_111_groups_0"), val = int32(1)];
+            tensor<fp16, [2048, 512, 3]> head_layer1_net_weight_to_fp16 = const()[name = string("head_layer1_net_weight_to_fp16"), val = tensor<fp16, [2048, 512, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42279616)))];
+            tensor<fp16, [2048]> head_layer1_net_bias_to_fp16 = const()[name = string("head_layer1_net_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48571136)))];
+            tensor<fp16, [1, 2048, ?]> input_111_cast_fp16 = conv(bias = head_layer1_net_bias_to_fp16, dilations = input_111_dilations_0, groups = input_111_groups_0, pad = input_111_pad_0, pad_type = input_111_pad_type_0, strides = input_111_strides_0, weight = head_layer1_net_weight_to_fp16, x = input_109_cast_fp16)[name = string("input_111_cast_fp16")];
+            fp32 input_alpha_1 = const()[name = string("input_alpha_1"), val = fp32(0x1.9814fap-13)];
+            tensor<fp16, [1, 2048, ?]> input_cast_fp16 = leaky_relu(alpha = input_alpha_1, x = input_111_cast_fp16)[name = string("input_cast_fp16")];
+            string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)];
+            tensor<fp16, [512, 2048, 1]> head_layer2_weight_to_fp16 = const()[name = string("head_layer2_weight_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48575296)))];
+            tensor<fp16, [1, 512, ?]> x_29_cast_fp16 = conv(dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = head_layer2_weight_to_fp16, x = input_cast_fp16)[name = string("x_29_cast_fp16")];
+            tensor<int32, [3]> var_607_perm_0 = const()[name = string("op_607_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [2]> var_612 = const()[name = string("op_612"), val = tensor<int32, [2]>([1, -1])];
+            tensor<fp16, [1, ?, 512]> var_607_cast_fp16 = transpose(perm = var_607_perm_0, x = x_29_cast_fp16)[name = string("transpose_0")];
+            tensor<fp16, [1, ?]> var_613_cast_fp16 = reshape(shape = var_612, x = var_607_cast_fp16)[name = string("op_613_cast_fp16")];
+            string var_613_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_613_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [1, ?]> wav = cast(dtype = var_613_cast_fp16_to_fp32_dtype_0, x = var_613_cast_fp16)[name = string("cast_5")];
+        } -> (wav);
+}

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Vocoder.mlpackage/Manifest.json

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+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "25CD729D-5964-4AA7-8FCA-CFCB279429B1": {
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+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "53EE82CD-FCB9-4071-BACB-71F6BA6D3B60": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "53EE82CD-FCB9-4071-BACB-71F6BA6D3B60"
+}

manifest.json

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+{
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+  "conversion_repo": "https://github.com/FluidInference/mobius/tree/main/models/tts/supertonic-3/coreml",
+  "integration": "https://github.com/FluidInference/FluidAudio/tree/main/Sources/FluidAudio/TTS/Supertonic3",
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+    "ru",
+    "sk",
+    "sl",
+    "sv",
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+}

tts.json

@@ -0,0 +1,311 @@
+{
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+}