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