program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3400.34.1"}, {"coremlc-version", "3400.42.1"}, {"coremltools-component-torch", "2.3.1"}, {"coremltools-version", "7.1.4"}}), mldb_token = string("mldb-7nwypn9a9o")]
{
    func main<ios18>(tensor<fp32, [1, 4, 1920]> audio, tensor<fp16, [1, 4, 64]> cast_36_in_state, tensor<fp16, [1, 4224, 1]> input1_3_cast_fp16_in_state, tensor<fp16, [1, 256, 2]> input_13_cast_fp16_concat_in_state, tensor<fp16, [1, 256, 4]> input_17_cast_fp16_in_state, tensor<fp16, [1, 256, 4]> input_23_cast_fp16_concat_in_state, tensor<fp16, [1, 256, 8]> input_27_cast_fp16_in_state, tensor<fp16, [1, 256, 8]> input_33_cast_fp16_concat_in_state, tensor<fp16, [1, 256, 16]> input_37_cast_fp16_in_state, tensor<fp16, [1, 256, 32]> input_47_cast_fp16_in_state, tensor<fp16, [1, 256, 256]> input_4_cast_fp16_in_state, tensor<fp16, [1, 256, 64]> input_57_cast_fp16_in_state, tensor<fp16, [1, 256, 128]> input_67_cast_fp16_in_state, tensor<fp16, [1, 256, 1]> input_77_cast_fp16_concat_in_state, tensor<fp16, [1, 256, 2]> input_7_cast_fp16_in_state, tensor<fp16, [1, 1, 384, 15]> var_508_cast_fp16_concat_in_state) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"audio", [1, 4, 1920]}}), ("RangeDims", {{"audio", [[1, 1], [4, 4], [1920, 1920]]}}))), UserMetadata = dict<string, string>({{"iteration", "743046"}, {"taskid", "cz76r37itk"}})] {
            int32 var_17 = const()[name = string("op_17"), val = int32(1)];
            tensor<int32, [1]> var_21 = const()[name = string("op_21"), val = tensor<int32, [1]>([64])];
            tensor<int32, [1]> var_23 = const()[name = string("op_23"), val = tensor<int32, [1]>([1])];
            string input0_1_pad_type_0 = const()[name = string("input0_1_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input0_1_pad_0 = const()[name = string("input0_1_pad_0"), val = tensor<int32, [2]>([64, 64])];
            string audio_to_fp16_dtype_0 = const()[name = string("audio_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [384, 4, 128]> front_end_0_weight_to_fp16 = const()[name = string("front_end_0_weight_to_fp16"), val = tensor<fp16, [384, 4, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
            tensor<fp16, [1, 4, 1920]> cast_36 = cast(dtype = audio_to_fp16_dtype_0, x = audio)[name = string("cast_36")];
            tensor<fp16, [1, 4, 1984]> cast_36_expanded = concat(axis = int32(-1), interleave = bool(false), values = (cast_36_in_state, cast_36));
            tensor<fp16, [1, 4, 64]> cast_36_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 4, 64]), x = cast_36_expanded);
            tensor<fp16, [1, 384, 30]> input0_1_cast_fp16 = conv(dilations = var_23, groups = var_17, pad = tensor<int32, [2]>([0, 0]), pad_type = input0_1_pad_type_0, strides = var_21, weight = front_end_0_weight_to_fp16, x = cast_36_expanded);
            tensor<fp16, [1, 384, 30]> var_26_cast_fp16 = relu(x = input0_1_cast_fp16)[name = string("op_26_cast_fp16")];
            bool var_29 = const()[name = string("op_29"), val = bool(true)];
            tensor<int32, [1]> var_34 = const()[name = string("op_34"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_4_cast_fp16 = reduce_mean(axes = var_34, keep_dims = var_29, x = var_26_cast_fp16)[name = string("mean_y_4_cast_fp16")];
            tensor<fp16, [1, 384, 30]> sub_0_cast_fp16 = sub(x = var_26_cast_fp16, y = mean_y_4_cast_fp16)[name = string("sub_0_cast_fp16")];
            tensor<fp16, [1, 384, 30]> square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = string("square_0_cast_fp16")];
            tensor<int32, [1]> reduce_mean_1_axes_0 = const()[name = string("reduce_mean_1_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_1_keep_dims_0 = const()[name = string("reduce_mean_1_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_1_cast_fp16 = reduce_mean(axes = reduce_mean_1_axes_0, keep_dims = reduce_mean_1_keep_dims_0, x = square_0_cast_fp16)[name = string("reduce_mean_1_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_0_cast_fp16 = sqrt(x = reduce_mean_1_cast_fp16)[name = string("sqrt_0_cast_fp16")];
            fp16 mul_0_y_0_to_fp16 = const()[name = string("mul_0_y_0_to_fp16"), val = fp16(0x1.004p+0)];
            tensor<fp16, [1, 1, 30]> mul_0_cast_fp16 = mul(x = sqrt_0_cast_fp16, y = mul_0_y_0_to_fp16)[name = string("mul_0_cast_fp16")];
            fp16 var_38_to_fp16 = const()[name = string("op_38_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_4_cast_fp16 = add(x = mul_0_cast_fp16, y = var_38_to_fp16)[name = string("std_y_4_cast_fp16")];
            tensor<fp16, [1, 384, 1]> front_norm_norm_gamma_to_fp16 = const()[name = string("front_norm_norm_gamma_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(393344)))];
            tensor<fp16, [1, 384, 30]> var_41_cast_fp16 = mul(x = front_norm_norm_gamma_to_fp16, y = sub_0_cast_fp16)[name = string("op_41_cast_fp16")];
            tensor<fp16, [1, 384, 30]> var_42_cast_fp16 = real_div(x = var_41_cast_fp16, y = std_y_4_cast_fp16)[name = string("op_42_cast_fp16")];
            tensor<fp16, [1, 384, 1]> front_norm_norm_beta_to_fp16 = const()[name = string("front_norm_norm_beta_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394176)))];
            tensor<fp16, [1, 384, 30]> input_73_cast_fp16 = add(x = var_42_cast_fp16, y = front_norm_norm_beta_to_fp16)[name = string("input_73_cast_fp16")];
            int32 var_45 = const()[name = string("op_45"), val = int32(1)];
            tensor<int32, [1]> var_50 = const()[name = string("op_50"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_52 = const()[name = string("op_52"), val = tensor<int32, [1]>([1])];
            string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 384, 1]> to_latent_weight_to_fp16 = const()[name = string("to_latent_weight_to_fp16"), val = tensor<fp16, [256, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(395008)))];
            tensor<fp16, [1, 256, 30]> input_77_cast_fp16 = conv(dilations = var_52, groups = var_45, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = var_50, weight = to_latent_weight_to_fp16, x = input_73_cast_fp16)[name = string("input_77_cast_fp16")];
            int32 var_62 = const()[name = string("op_62"), val = int32(1)];
            int32 var_63 = const()[name = string("op_63"), val = int32(256)];
            bool var_66 = const()[name = string("op_66"), val = bool(true)];
            tensor<int32, [1]> var_86 = const()[name = string("op_86"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_88 = const()[name = string("op_88"), val = tensor<int32, [1]>([1])];
            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_0_tcn_0_weight_to_fp16 = const()[name = string("sep_module_0_tcn_0_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(591680)))];
            tensor<fp16, [1, 256, 30]> input_5_cast_fp16 = conv(dilations = var_88, groups = var_62, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = var_86, weight = sep_module_0_tcn_0_weight_to_fp16, x = input_77_cast_fp16)[name = string("input_5_cast_fp16")];
            fp32 var_92_alpha_1 = const()[name = string("op_92_alpha_1"), val = fp32(0x1.1daf5cp-2)];
            tensor<fp16, [1, 256, 30]> var_92_cast_fp16 = leaky_relu(alpha = var_92_alpha_1, x = input_5_cast_fp16)[name = string("op_92_cast_fp16")];
            tensor<int32, [1]> var_96 = const()[name = string("op_96"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_3_cast_fp16 = reduce_mean(axes = var_96, keep_dims = var_66, x = var_92_cast_fp16)[name = string("mean_y_3_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_1_cast_fp16 = sub(x = var_92_cast_fp16, y = mean_y_3_cast_fp16)[name = string("sub_1_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_1_cast_fp16 = square(x = sub_1_cast_fp16)[name = string("square_1_cast_fp16")];
            tensor<int32, [1]> reduce_mean_3_axes_0 = const()[name = string("reduce_mean_3_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_3_keep_dims_0 = const()[name = string("reduce_mean_3_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = square_1_cast_fp16)[name = string("reduce_mean_3_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_1_cast_fp16 = sqrt(x = reduce_mean_3_cast_fp16)[name = string("sqrt_1_cast_fp16")];
            fp16 mul_1_y_0_to_fp16 = const()[name = string("mul_1_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_1_cast_fp16 = mul(x = sqrt_1_cast_fp16, y = mul_1_y_0_to_fp16)[name = string("mul_1_cast_fp16")];
            fp16 var_100_to_fp16 = const()[name = string("op_100_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_3_cast_fp16 = add(x = mul_1_cast_fp16, y = var_100_to_fp16)[name = string("std_y_3_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_0_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_0_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(722816)))];
            tensor<fp16, [1, 256, 30]> var_103_cast_fp16 = mul(x = sep_module_0_tcn_2_norm_gamma_to_fp16, y = sub_1_cast_fp16)[name = string("op_103_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_104_cast_fp16 = real_div(x = var_103_cast_fp16, y = std_y_3_cast_fp16)[name = string("op_104_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_0_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_0_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723392)))];
            tensor<fp16, [1, 256, 30]> input_7_cast_fp16 = add(x = var_104_cast_fp16, y = sep_module_0_tcn_2_norm_beta_to_fp16)[name = string("input_7_cast_fp16")];
            tensor<int32, [6]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 1])];
            string input_9_mode_0 = const()[name = string("input_9_mode_0"), val = string("constant")];
            fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 32]> input_9_cast_fp16 = concat(axis = int32(-1), interleave = bool(false), values = (input_7_cast_fp16_in_state, input_7_cast_fp16));
            tensor<fp16, [1, 256, 2]> input_7_cast_fp16_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 256, 2]), x = input_9_cast_fp16);
            tensor<int32, [1]> var_109 = const()[name = string("op_109"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_111 = const()[name = string("op_111"), val = tensor<int32, [1]>([1])];
            string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_0_tcn_4_weight_to_fp16 = const()[name = string("sep_module_0_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723968)))];
            tensor<fp16, [1, 256, 30]> input_11_cast_fp16 = conv(dilations = var_111, groups = var_63, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_109, weight = sep_module_0_tcn_4_weight_to_fp16, x = input_9_cast_fp16)[name = string("input_11_cast_fp16")];
            fp32 var_115_alpha_1 = const()[name = string("op_115_alpha_1"), val = fp32(-0x1.3f432ap-4)];
            tensor<fp16, [1, 256, 30]> var_115_cast_fp16 = leaky_relu(alpha = var_115_alpha_1, x = input_11_cast_fp16)[name = string("op_115_cast_fp16")];
            tensor<int32, [1]> var_119 = const()[name = string("op_119"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_5_cast_fp16 = reduce_mean(axes = var_119, keep_dims = var_66, x = var_115_cast_fp16)[name = string("mean_y_5_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_2_cast_fp16 = sub(x = var_115_cast_fp16, y = mean_y_5_cast_fp16)[name = string("sub_2_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_2_cast_fp16 = square(x = sub_2_cast_fp16)[name = string("square_2_cast_fp16")];
            tensor<int32, [1]> reduce_mean_5_axes_0 = const()[name = string("reduce_mean_5_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_5_keep_dims_0 = const()[name = string("reduce_mean_5_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_2_cast_fp16)[name = string("reduce_mean_5_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_2_cast_fp16 = sqrt(x = reduce_mean_5_cast_fp16)[name = string("sqrt_2_cast_fp16")];
            fp16 mul_2_y_0_to_fp16 = const()[name = string("mul_2_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_2_cast_fp16 = mul(x = sqrt_2_cast_fp16, y = mul_2_y_0_to_fp16)[name = string("mul_2_cast_fp16")];
            fp16 var_123_to_fp16 = const()[name = string("op_123_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_5_cast_fp16 = add(x = mul_2_cast_fp16, y = var_123_to_fp16)[name = string("std_y_5_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_0_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_0_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(725568)))];
            tensor<fp16, [1, 256, 30]> var_126_cast_fp16 = mul(x = sep_module_0_tcn_6_norm_gamma_to_fp16, y = sub_2_cast_fp16)[name = string("op_126_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_127_cast_fp16 = real_div(x = var_126_cast_fp16, y = std_y_5_cast_fp16)[name = string("op_127_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_0_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_0_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(726144)))];
            tensor<fp16, [1, 256, 30]> y_2_cast_fp16 = add(x = var_127_cast_fp16, y = sep_module_0_tcn_6_norm_beta_to_fp16)[name = string("y_2_cast_fp16")];
            tensor<fp16, [1, 256, 31]> input_77_cast_fp16_concat_expanded = concat(axis = int32(-1), interleave = bool(false), values = (input_77_cast_fp16_concat_in_state, input_77_cast_fp16));
            tensor<fp16, [1, 256, 30]> input_77_cast_fp16_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 256, 30]), x = input_77_cast_fp16_concat_expanded);
            tensor<fp16, [1, 256, 1]> input_77_cast_fp16_concat_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -1]), size = tensor<int32, [3]>([1, 256, 1]), x = input_77_cast_fp16_concat_expanded);
            tensor<fp16, [1, 256, 30]> input_13_cast_fp16 = add(x = input_77_cast_fp16_delayed, y = y_2_cast_fp16)[name = string("input_13_cast_fp16")];
            tensor<int32, [1]> var_138 = const()[name = string("op_138"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_140 = const()[name = string("op_140"), val = tensor<int32, [1]>([1])];
            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_1_tcn_0_weight_to_fp16 = const()[name = string("sep_module_1_tcn_0_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(726720)))];
            tensor<fp16, [1, 256, 30]> input_15_cast_fp16 = conv(dilations = var_140, groups = var_62, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = var_138, weight = sep_module_1_tcn_0_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")];
            fp32 var_144_alpha_1 = const()[name = string("op_144_alpha_1"), val = fp32(0x1.c794a2p-10)];
            tensor<fp16, [1, 256, 30]> var_144_cast_fp16 = leaky_relu(alpha = var_144_alpha_1, x = input_15_cast_fp16)[name = string("op_144_cast_fp16")];
            tensor<int32, [1]> var_148 = const()[name = string("op_148"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_7_cast_fp16 = reduce_mean(axes = var_148, keep_dims = var_66, x = var_144_cast_fp16)[name = string("mean_y_7_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_3_cast_fp16 = sub(x = var_144_cast_fp16, y = mean_y_7_cast_fp16)[name = string("sub_3_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_3_cast_fp16 = square(x = sub_3_cast_fp16)[name = string("square_3_cast_fp16")];
            tensor<int32, [1]> reduce_mean_7_axes_0 = const()[name = string("reduce_mean_7_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_7_keep_dims_0 = const()[name = string("reduce_mean_7_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_7_cast_fp16 = reduce_mean(axes = reduce_mean_7_axes_0, keep_dims = reduce_mean_7_keep_dims_0, x = square_3_cast_fp16)[name = string("reduce_mean_7_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_3_cast_fp16 = sqrt(x = reduce_mean_7_cast_fp16)[name = string("sqrt_3_cast_fp16")];
            fp16 mul_3_y_0_to_fp16 = const()[name = string("mul_3_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_3_cast_fp16 = mul(x = sqrt_3_cast_fp16, y = mul_3_y_0_to_fp16)[name = string("mul_3_cast_fp16")];
            fp16 var_152_to_fp16 = const()[name = string("op_152_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_7_cast_fp16 = add(x = mul_3_cast_fp16, y = var_152_to_fp16)[name = string("std_y_7_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_1_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_1_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(857856)))];
            tensor<fp16, [1, 256, 30]> var_155_cast_fp16 = mul(x = sep_module_1_tcn_2_norm_gamma_to_fp16, y = sub_3_cast_fp16)[name = string("op_155_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_156_cast_fp16 = real_div(x = var_155_cast_fp16, y = std_y_7_cast_fp16)[name = string("op_156_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_1_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_1_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(858432)))];
            tensor<fp16, [1, 256, 30]> input_17_cast_fp16 = add(x = var_156_cast_fp16, y = sep_module_1_tcn_2_norm_beta_to_fp16)[name = string("input_17_cast_fp16")];
            tensor<int32, [6]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            string input_19_mode_0 = const()[name = string("input_19_mode_0"), val = string("constant")];
            fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 34]> input_19_cast_fp16 = concat(axis = int32(-1), interleave = bool(false), values = (input_17_cast_fp16_in_state, input_17_cast_fp16));
            tensor<fp16, [1, 256, 4]> input_17_cast_fp16_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 256, 4]), x = input_19_cast_fp16);
            tensor<int32, [1]> var_161 = const()[name = string("op_161"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_163 = const()[name = string("op_163"), val = tensor<int32, [1]>([2])];
            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_1_tcn_4_weight_to_fp16 = const()[name = string("sep_module_1_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859008)))];
            tensor<fp16, [1, 256, 30]> input_21_cast_fp16 = conv(dilations = var_163, groups = var_63, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = var_161, weight = sep_module_1_tcn_4_weight_to_fp16, x = input_19_cast_fp16)[name = string("input_21_cast_fp16")];
            fp32 var_167_alpha_1 = const()[name = string("op_167_alpha_1"), val = fp32(-0x1.020466p-6)];
            tensor<fp16, [1, 256, 30]> var_167_cast_fp16 = leaky_relu(alpha = var_167_alpha_1, x = input_21_cast_fp16)[name = string("op_167_cast_fp16")];
            tensor<int32, [1]> var_171 = const()[name = string("op_171"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_9_cast_fp16 = reduce_mean(axes = var_171, keep_dims = var_66, x = var_167_cast_fp16)[name = string("mean_y_9_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_4_cast_fp16 = sub(x = var_167_cast_fp16, y = mean_y_9_cast_fp16)[name = string("sub_4_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_4_cast_fp16 = square(x = sub_4_cast_fp16)[name = string("square_4_cast_fp16")];
            tensor<int32, [1]> reduce_mean_9_axes_0 = const()[name = string("reduce_mean_9_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_9_keep_dims_0 = const()[name = string("reduce_mean_9_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = square_4_cast_fp16)[name = string("reduce_mean_9_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_4_cast_fp16 = sqrt(x = reduce_mean_9_cast_fp16)[name = string("sqrt_4_cast_fp16")];
            fp16 mul_4_y_0_to_fp16 = const()[name = string("mul_4_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_4_cast_fp16 = mul(x = sqrt_4_cast_fp16, y = mul_4_y_0_to_fp16)[name = string("mul_4_cast_fp16")];
            fp16 var_175_to_fp16 = const()[name = string("op_175_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_9_cast_fp16 = add(x = mul_4_cast_fp16, y = var_175_to_fp16)[name = string("std_y_9_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_1_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_1_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860608)))];
            tensor<fp16, [1, 256, 30]> var_178_cast_fp16 = mul(x = sep_module_1_tcn_6_norm_gamma_to_fp16, y = sub_4_cast_fp16)[name = string("op_178_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_179_cast_fp16 = real_div(x = var_178_cast_fp16, y = std_y_9_cast_fp16)[name = string("op_179_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_1_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_1_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(861184)))];
            tensor<fp16, [1, 256, 30]> y_4_cast_fp16 = add(x = var_179_cast_fp16, y = sep_module_1_tcn_6_norm_beta_to_fp16)[name = string("y_4_cast_fp16")];
            tensor<fp16, [1, 256, 32]> input_13_cast_fp16_concat_expanded = concat(axis = int32(-1), interleave = bool(false), values = (input_13_cast_fp16_concat_in_state, input_13_cast_fp16));
            tensor<fp16, [1, 256, 30]> input_13_cast_fp16_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 256, 30]), x = input_13_cast_fp16_concat_expanded);
            tensor<fp16, [1, 256, 2]> input_13_cast_fp16_concat_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -2]), size = tensor<int32, [3]>([1, 256, 2]), x = input_13_cast_fp16_concat_expanded);
            tensor<fp16, [1, 256, 30]> input_23_cast_fp16 = add(x = input_13_cast_fp16_delayed, y = y_4_cast_fp16)[name = string("input_23_cast_fp16")];
            tensor<int32, [1]> var_190 = const()[name = string("op_190"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_192 = const()[name = string("op_192"), val = tensor<int32, [1]>([1])];
            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]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_2_tcn_0_weight_to_fp16 = const()[name = string("sep_module_2_tcn_0_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(861760)))];
            tensor<fp16, [1, 256, 30]> input_25_cast_fp16 = conv(dilations = var_192, groups = var_62, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = var_190, weight = sep_module_2_tcn_0_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")];
            fp32 var_196_alpha_1 = const()[name = string("op_196_alpha_1"), val = fp32(-0x1.d951eep-2)];
            tensor<fp16, [1, 256, 30]> var_196_cast_fp16 = leaky_relu(alpha = var_196_alpha_1, x = input_25_cast_fp16)[name = string("op_196_cast_fp16")];
            tensor<int32, [1]> var_200 = const()[name = string("op_200"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_11_cast_fp16 = reduce_mean(axes = var_200, keep_dims = var_66, x = var_196_cast_fp16)[name = string("mean_y_11_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_5_cast_fp16 = sub(x = var_196_cast_fp16, y = mean_y_11_cast_fp16)[name = string("sub_5_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_5_cast_fp16 = square(x = sub_5_cast_fp16)[name = string("square_5_cast_fp16")];
            tensor<int32, [1]> reduce_mean_11_axes_0 = const()[name = string("reduce_mean_11_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_11_keep_dims_0 = const()[name = string("reduce_mean_11_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_5_cast_fp16)[name = string("reduce_mean_11_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_5_cast_fp16 = sqrt(x = reduce_mean_11_cast_fp16)[name = string("sqrt_5_cast_fp16")];
            fp16 mul_5_y_0_to_fp16 = const()[name = string("mul_5_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_5_cast_fp16 = mul(x = sqrt_5_cast_fp16, y = mul_5_y_0_to_fp16)[name = string("mul_5_cast_fp16")];
            fp16 var_204_to_fp16 = const()[name = string("op_204_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_11_cast_fp16 = add(x = mul_5_cast_fp16, y = var_204_to_fp16)[name = string("std_y_11_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_2_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_2_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(992896)))];
            tensor<fp16, [1, 256, 30]> var_207_cast_fp16 = mul(x = sep_module_2_tcn_2_norm_gamma_to_fp16, y = sub_5_cast_fp16)[name = string("op_207_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_208_cast_fp16 = real_div(x = var_207_cast_fp16, y = std_y_11_cast_fp16)[name = string("op_208_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_2_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_2_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993472)))];
            tensor<fp16, [1, 256, 30]> input_27_cast_fp16 = add(x = var_208_cast_fp16, y = sep_module_2_tcn_2_norm_beta_to_fp16)[name = string("input_27_cast_fp16")];
            tensor<int32, [6]> input_29_pad_0 = const()[name = string("input_29_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 4])];
            string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("constant")];
            fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 38]> input_29_cast_fp16 = concat(axis = int32(-1), interleave = bool(false), values = (input_27_cast_fp16_in_state, input_27_cast_fp16));
            tensor<fp16, [1, 256, 8]> input_27_cast_fp16_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 256, 8]), x = input_29_cast_fp16);
            tensor<int32, [1]> var_213 = const()[name = string("op_213"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_215 = const()[name = string("op_215"), val = tensor<int32, [1]>([4])];
            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_2_tcn_4_weight_to_fp16 = const()[name = string("sep_module_2_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(994048)))];
            tensor<fp16, [1, 256, 30]> input_31_cast_fp16 = conv(dilations = var_215, groups = var_63, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = var_213, weight = sep_module_2_tcn_4_weight_to_fp16, x = input_29_cast_fp16)[name = string("input_31_cast_fp16")];
            fp32 var_219_alpha_1 = const()[name = string("op_219_alpha_1"), val = fp32(0x1.4479ep-2)];
            tensor<fp16, [1, 256, 30]> var_219_cast_fp16 = leaky_relu(alpha = var_219_alpha_1, x = input_31_cast_fp16)[name = string("op_219_cast_fp16")];
            tensor<int32, [1]> var_223 = const()[name = string("op_223"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_13_cast_fp16 = reduce_mean(axes = var_223, keep_dims = var_66, x = var_219_cast_fp16)[name = string("mean_y_13_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_6_cast_fp16 = sub(x = var_219_cast_fp16, y = mean_y_13_cast_fp16)[name = string("sub_6_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_6_cast_fp16 = square(x = sub_6_cast_fp16)[name = string("square_6_cast_fp16")];
            tensor<int32, [1]> reduce_mean_13_axes_0 = const()[name = string("reduce_mean_13_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_13_keep_dims_0 = const()[name = string("reduce_mean_13_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_13_cast_fp16 = reduce_mean(axes = reduce_mean_13_axes_0, keep_dims = reduce_mean_13_keep_dims_0, x = square_6_cast_fp16)[name = string("reduce_mean_13_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_6_cast_fp16 = sqrt(x = reduce_mean_13_cast_fp16)[name = string("sqrt_6_cast_fp16")];
            fp16 mul_6_y_0_to_fp16 = const()[name = string("mul_6_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_6_cast_fp16 = mul(x = sqrt_6_cast_fp16, y = mul_6_y_0_to_fp16)[name = string("mul_6_cast_fp16")];
            fp16 var_227_to_fp16 = const()[name = string("op_227_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_13_cast_fp16 = add(x = mul_6_cast_fp16, y = var_227_to_fp16)[name = string("std_y_13_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_2_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_2_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(995648)))];
            tensor<fp16, [1, 256, 30]> var_230_cast_fp16 = mul(x = sep_module_2_tcn_6_norm_gamma_to_fp16, y = sub_6_cast_fp16)[name = string("op_230_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_231_cast_fp16 = real_div(x = var_230_cast_fp16, y = std_y_13_cast_fp16)[name = string("op_231_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_2_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_2_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(996224)))];
            tensor<fp16, [1, 256, 30]> y_6_cast_fp16 = add(x = var_231_cast_fp16, y = sep_module_2_tcn_6_norm_beta_to_fp16)[name = string("y_6_cast_fp16")];
            tensor<fp16, [1, 256, 34]> input_23_cast_fp16_concat_expanded = concat(axis = int32(-1), interleave = bool(false), values = (input_23_cast_fp16_concat_in_state, input_23_cast_fp16));
            tensor<fp16, [1, 256, 30]> input_23_cast_fp16_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 256, 30]), x = input_23_cast_fp16_concat_expanded);
            tensor<fp16, [1, 256, 4]> input_23_cast_fp16_concat_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -4]), size = tensor<int32, [3]>([1, 256, 4]), x = input_23_cast_fp16_concat_expanded);
            tensor<fp16, [1, 256, 30]> input_33_cast_fp16 = add(x = input_23_cast_fp16_delayed, y = y_6_cast_fp16)[name = string("input_33_cast_fp16")];
            tensor<int32, [1]> var_242 = const()[name = string("op_242"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_244 = const()[name = string("op_244"), val = tensor<int32, [1]>([1])];
            string input_35_pad_type_0 = const()[name = string("input_35_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_3_tcn_0_weight_to_fp16 = const()[name = string("sep_module_3_tcn_0_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(996800)))];
            tensor<fp16, [1, 256, 30]> input_35_cast_fp16 = conv(dilations = var_244, groups = var_62, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = var_242, weight = sep_module_3_tcn_0_weight_to_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")];
            fp32 var_248_alpha_1 = const()[name = string("op_248_alpha_1"), val = fp32(0x1.8c56cp-5)];
            tensor<fp16, [1, 256, 30]> var_248_cast_fp16 = leaky_relu(alpha = var_248_alpha_1, x = input_35_cast_fp16)[name = string("op_248_cast_fp16")];
            tensor<int32, [1]> var_252 = const()[name = string("op_252"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_15_cast_fp16 = reduce_mean(axes = var_252, keep_dims = var_66, x = var_248_cast_fp16)[name = string("mean_y_15_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_7_cast_fp16 = sub(x = var_248_cast_fp16, y = mean_y_15_cast_fp16)[name = string("sub_7_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_7_cast_fp16 = square(x = sub_7_cast_fp16)[name = string("square_7_cast_fp16")];
            tensor<int32, [1]> reduce_mean_15_axes_0 = const()[name = string("reduce_mean_15_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_15_keep_dims_0 = const()[name = string("reduce_mean_15_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_15_cast_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = square_7_cast_fp16)[name = string("reduce_mean_15_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_7_cast_fp16 = sqrt(x = reduce_mean_15_cast_fp16)[name = string("sqrt_7_cast_fp16")];
            fp16 mul_7_y_0_to_fp16 = const()[name = string("mul_7_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_7_cast_fp16 = mul(x = sqrt_7_cast_fp16, y = mul_7_y_0_to_fp16)[name = string("mul_7_cast_fp16")];
            fp16 var_256_to_fp16 = const()[name = string("op_256_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_15_cast_fp16 = add(x = mul_7_cast_fp16, y = var_256_to_fp16)[name = string("std_y_15_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_3_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_3_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1127936)))];
            tensor<fp16, [1, 256, 30]> var_259_cast_fp16 = mul(x = sep_module_3_tcn_2_norm_gamma_to_fp16, y = sub_7_cast_fp16)[name = string("op_259_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_260_cast_fp16 = real_div(x = var_259_cast_fp16, y = std_y_15_cast_fp16)[name = string("op_260_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_3_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_3_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1128512)))];
            tensor<fp16, [1, 256, 30]> input_37_cast_fp16 = add(x = var_260_cast_fp16, y = sep_module_3_tcn_2_norm_beta_to_fp16)[name = string("input_37_cast_fp16")];
            tensor<int32, [6]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 8, 8])];
            string input_39_mode_0 = const()[name = string("input_39_mode_0"), val = string("constant")];
            fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 46]> input_39_cast_fp16 = concat(axis = int32(-1), interleave = bool(false), values = (input_37_cast_fp16_in_state, input_37_cast_fp16));
            tensor<fp16, [1, 256, 16]> input_37_cast_fp16_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -16]), size = tensor<int32, [3]>([1, 256, 16]), x = input_39_cast_fp16);
            tensor<int32, [1]> var_265 = const()[name = string("op_265"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_267 = const()[name = string("op_267"), val = tensor<int32, [1]>([8])];
            string input_41_pad_type_0 = const()[name = string("input_41_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_3_tcn_4_weight_to_fp16 = const()[name = string("sep_module_3_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1129088)))];
            tensor<fp16, [1, 256, 30]> input_41_cast_fp16 = conv(dilations = var_267, groups = var_63, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = var_265, weight = sep_module_3_tcn_4_weight_to_fp16, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")];
            fp32 var_271_alpha_1 = const()[name = string("op_271_alpha_1"), val = fp32(0x1.7882f2p-1)];
            tensor<fp16, [1, 256, 30]> var_271_cast_fp16 = leaky_relu(alpha = var_271_alpha_1, x = input_41_cast_fp16)[name = string("op_271_cast_fp16")];
            tensor<int32, [1]> var_275 = const()[name = string("op_275"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_17_cast_fp16 = reduce_mean(axes = var_275, keep_dims = var_66, x = var_271_cast_fp16)[name = string("mean_y_17_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_8_cast_fp16 = sub(x = var_271_cast_fp16, y = mean_y_17_cast_fp16)[name = string("sub_8_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_8_cast_fp16 = square(x = sub_8_cast_fp16)[name = string("square_8_cast_fp16")];
            tensor<int32, [1]> reduce_mean_17_axes_0 = const()[name = string("reduce_mean_17_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_17_keep_dims_0 = const()[name = string("reduce_mean_17_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_17_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_8_cast_fp16)[name = string("reduce_mean_17_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_8_cast_fp16 = sqrt(x = reduce_mean_17_cast_fp16)[name = string("sqrt_8_cast_fp16")];
            fp16 mul_8_y_0_to_fp16 = const()[name = string("mul_8_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_8_cast_fp16 = mul(x = sqrt_8_cast_fp16, y = mul_8_y_0_to_fp16)[name = string("mul_8_cast_fp16")];
            fp16 var_279_to_fp16 = const()[name = string("op_279_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_17_cast_fp16 = add(x = mul_8_cast_fp16, y = var_279_to_fp16)[name = string("std_y_17_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_3_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_3_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1130688)))];
            tensor<fp16, [1, 256, 30]> var_282_cast_fp16 = mul(x = sep_module_3_tcn_6_norm_gamma_to_fp16, y = sub_8_cast_fp16)[name = string("op_282_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_283_cast_fp16 = real_div(x = var_282_cast_fp16, y = std_y_17_cast_fp16)[name = string("op_283_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_3_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_3_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1131264)))];
            tensor<fp16, [1, 256, 30]> y_8_cast_fp16 = add(x = var_283_cast_fp16, y = sep_module_3_tcn_6_norm_beta_to_fp16)[name = string("y_8_cast_fp16")];
            tensor<fp16, [1, 256, 38]> input_33_cast_fp16_concat_expanded = concat(axis = int32(-1), interleave = bool(false), values = (input_33_cast_fp16_concat_in_state, input_33_cast_fp16));
            tensor<fp16, [1, 256, 30]> input_33_cast_fp16_delayed = slice_by_size(begin = tensor<int32, [3]>([0, 0, 0]), size = tensor<int32, [3]>([1, 256, 30]), x = input_33_cast_fp16_concat_expanded);
            tensor<fp16, [1, 256, 8]> input_33_cast_fp16_concat_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -8]), size = tensor<int32, [3]>([1, 256, 8]), x = input_33_cast_fp16_concat_expanded);
            tensor<fp16, [1, 256, 30]> input_43_cast_fp16 = add(x = input_33_cast_fp16_delayed, y = y_8_cast_fp16)[name = string("input_43_cast_fp16")];
            tensor<int32, [1]> var_294 = const()[name = string("op_294"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_296 = const()[name = string("op_296"), val = tensor<int32, [1]>([1])];
            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_4_tcn_0_weight_to_fp16 = const()[name = string("sep_module_4_tcn_0_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1131840)))];
            tensor<fp16, [1, 256, 30]> input_45_cast_fp16 = conv(dilations = var_296, groups = var_62, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_294, weight = sep_module_4_tcn_0_weight_to_fp16, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")];
            fp32 var_300_alpha_1 = const()[name = string("op_300_alpha_1"), val = fp32(0x1.82f882p-2)];
            tensor<fp16, [1, 256, 30]> var_300_cast_fp16 = leaky_relu(alpha = var_300_alpha_1, x = input_45_cast_fp16)[name = string("op_300_cast_fp16")];
            tensor<int32, [1]> var_304 = const()[name = string("op_304"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_19_cast_fp16 = reduce_mean(axes = var_304, keep_dims = var_66, x = var_300_cast_fp16)[name = string("mean_y_19_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_9_cast_fp16 = sub(x = var_300_cast_fp16, y = mean_y_19_cast_fp16)[name = string("sub_9_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_9_cast_fp16 = square(x = sub_9_cast_fp16)[name = string("square_9_cast_fp16")];
            tensor<int32, [1]> reduce_mean_19_axes_0 = const()[name = string("reduce_mean_19_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_19_keep_dims_0 = const()[name = string("reduce_mean_19_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_19_cast_fp16 = reduce_mean(axes = reduce_mean_19_axes_0, keep_dims = reduce_mean_19_keep_dims_0, x = square_9_cast_fp16)[name = string("reduce_mean_19_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_9_cast_fp16 = sqrt(x = reduce_mean_19_cast_fp16)[name = string("sqrt_9_cast_fp16")];
            fp16 mul_9_y_0_to_fp16 = const()[name = string("mul_9_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_9_cast_fp16 = mul(x = sqrt_9_cast_fp16, y = mul_9_y_0_to_fp16)[name = string("mul_9_cast_fp16")];
            fp16 var_308_to_fp16 = const()[name = string("op_308_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_19_cast_fp16 = add(x = mul_9_cast_fp16, y = var_308_to_fp16)[name = string("std_y_19_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_4_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_4_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1262976)))];
            tensor<fp16, [1, 256, 30]> var_311_cast_fp16 = mul(x = sep_module_4_tcn_2_norm_gamma_to_fp16, y = sub_9_cast_fp16)[name = string("op_311_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_312_cast_fp16 = real_div(x = var_311_cast_fp16, y = std_y_19_cast_fp16)[name = string("op_312_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_4_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_4_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1263552)))];
            tensor<fp16, [1, 256, 30]> input_47_cast_fp16 = add(x = var_312_cast_fp16, y = sep_module_4_tcn_2_norm_beta_to_fp16)[name = string("input_47_cast_fp16")];
            tensor<int32, [6]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 32, 0])];
            string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("constant")];
            fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 62]> input_49_cast_fp16 = concat(axis = int32(-1), interleave = bool(false), values = (input_47_cast_fp16_in_state, input_47_cast_fp16));
            tensor<fp16, [1, 256, 32]> input_47_cast_fp16_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -32]), size = tensor<int32, [3]>([1, 256, 32]), x = input_49_cast_fp16);
            tensor<int32, [1]> var_317 = const()[name = string("op_317"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_319 = const()[name = string("op_319"), val = tensor<int32, [1]>([16])];
            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_4_tcn_4_weight_to_fp16 = const()[name = string("sep_module_4_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1264128)))];
            tensor<fp16, [1, 256, 30]> input_51_cast_fp16 = conv(dilations = var_319, groups = var_63, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = var_317, weight = sep_module_4_tcn_4_weight_to_fp16, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")];
            fp32 var_323_alpha_1 = const()[name = string("op_323_alpha_1"), val = fp32(-0x1.543e88p-2)];
            tensor<fp16, [1, 256, 30]> var_323_cast_fp16 = leaky_relu(alpha = var_323_alpha_1, x = input_51_cast_fp16)[name = string("op_323_cast_fp16")];
            tensor<int32, [1]> var_327 = const()[name = string("op_327"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_21_cast_fp16 = reduce_mean(axes = var_327, keep_dims = var_66, x = var_323_cast_fp16)[name = string("mean_y_21_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_10_cast_fp16 = sub(x = var_323_cast_fp16, y = mean_y_21_cast_fp16)[name = string("sub_10_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_10_cast_fp16 = square(x = sub_10_cast_fp16)[name = string("square_10_cast_fp16")];
            tensor<int32, [1]> reduce_mean_21_axes_0 = const()[name = string("reduce_mean_21_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_21_keep_dims_0 = const()[name = string("reduce_mean_21_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_21_cast_fp16 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = square_10_cast_fp16)[name = string("reduce_mean_21_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_10_cast_fp16 = sqrt(x = reduce_mean_21_cast_fp16)[name = string("sqrt_10_cast_fp16")];
            fp16 mul_10_y_0_to_fp16 = const()[name = string("mul_10_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_10_cast_fp16 = mul(x = sqrt_10_cast_fp16, y = mul_10_y_0_to_fp16)[name = string("mul_10_cast_fp16")];
            fp16 var_331_to_fp16 = const()[name = string("op_331_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_21_cast_fp16 = add(x = mul_10_cast_fp16, y = var_331_to_fp16)[name = string("std_y_21_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_4_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_4_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265728)))];
            tensor<fp16, [1, 256, 30]> var_334_cast_fp16 = mul(x = sep_module_4_tcn_6_norm_gamma_to_fp16, y = sub_10_cast_fp16)[name = string("op_334_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_335_cast_fp16 = real_div(x = var_334_cast_fp16, y = std_y_21_cast_fp16)[name = string("op_335_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_4_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_4_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1266304)))];
            tensor<fp16, [1, 256, 30]> y_10_cast_fp16 = add(x = var_335_cast_fp16, y = sep_module_4_tcn_6_norm_beta_to_fp16)[name = string("y_10_cast_fp16")];
            tensor<fp16, [1, 256, 30]> input_53_cast_fp16 = add(x = input_43_cast_fp16, y = y_10_cast_fp16)[name = string("input_53_cast_fp16")];
            tensor<int32, [1]> var_346 = const()[name = string("op_346"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_348 = const()[name = string("op_348"), val = tensor<int32, [1]>([1])];
            string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_5_tcn_0_weight_to_fp16 = const()[name = string("sep_module_5_tcn_0_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1266880)))];
            tensor<fp16, [1, 256, 30]> input_55_cast_fp16 = conv(dilations = var_348, groups = var_62, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = var_346, weight = sep_module_5_tcn_0_weight_to_fp16, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")];
            fp32 var_352_alpha_1 = const()[name = string("op_352_alpha_1"), val = fp32(0x1.4b9b2ep-2)];
            tensor<fp16, [1, 256, 30]> var_352_cast_fp16 = leaky_relu(alpha = var_352_alpha_1, x = input_55_cast_fp16)[name = string("op_352_cast_fp16")];
            tensor<int32, [1]> var_356 = const()[name = string("op_356"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_23_cast_fp16 = reduce_mean(axes = var_356, keep_dims = var_66, x = var_352_cast_fp16)[name = string("mean_y_23_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_11_cast_fp16 = sub(x = var_352_cast_fp16, y = mean_y_23_cast_fp16)[name = string("sub_11_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_11_cast_fp16 = square(x = sub_11_cast_fp16)[name = string("square_11_cast_fp16")];
            tensor<int32, [1]> reduce_mean_23_axes_0 = const()[name = string("reduce_mean_23_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_23_keep_dims_0 = const()[name = string("reduce_mean_23_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_23_cast_fp16 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_11_cast_fp16)[name = string("reduce_mean_23_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_11_cast_fp16 = sqrt(x = reduce_mean_23_cast_fp16)[name = string("sqrt_11_cast_fp16")];
            fp16 mul_11_y_0_to_fp16 = const()[name = string("mul_11_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_11_cast_fp16 = mul(x = sqrt_11_cast_fp16, y = mul_11_y_0_to_fp16)[name = string("mul_11_cast_fp16")];
            fp16 var_360_to_fp16 = const()[name = string("op_360_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_23_cast_fp16 = add(x = mul_11_cast_fp16, y = var_360_to_fp16)[name = string("std_y_23_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_5_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_5_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1398016)))];
            tensor<fp16, [1, 256, 30]> var_363_cast_fp16 = mul(x = sep_module_5_tcn_2_norm_gamma_to_fp16, y = sub_11_cast_fp16)[name = string("op_363_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_364_cast_fp16 = real_div(x = var_363_cast_fp16, y = std_y_23_cast_fp16)[name = string("op_364_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_5_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_5_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1398592)))];
            tensor<fp16, [1, 256, 30]> input_57_cast_fp16 = add(x = var_364_cast_fp16, y = sep_module_5_tcn_2_norm_beta_to_fp16)[name = string("input_57_cast_fp16")];
            tensor<int32, [6]> input_59_pad_0 = const()[name = string("input_59_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 64, 0])];
            string input_59_mode_0 = const()[name = string("input_59_mode_0"), val = string("constant")];
            fp16 const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 94]> input_59_cast_fp16 = concat(axis = int32(-1), interleave = bool(false), values = (input_57_cast_fp16_in_state, input_57_cast_fp16));
            tensor<fp16, [1, 256, 64]> input_57_cast_fp16_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -64]), size = tensor<int32, [3]>([1, 256, 64]), x = input_59_cast_fp16);
            tensor<int32, [1]> var_369 = const()[name = string("op_369"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_371 = const()[name = string("op_371"), val = tensor<int32, [1]>([32])];
            string input_61_pad_type_0 = const()[name = string("input_61_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_5_tcn_4_weight_to_fp16 = const()[name = string("sep_module_5_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1399168)))];
            tensor<fp16, [1, 256, 30]> input_61_cast_fp16 = conv(dilations = var_371, groups = var_63, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = var_369, weight = sep_module_5_tcn_4_weight_to_fp16, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")];
            fp32 var_375_alpha_1 = const()[name = string("op_375_alpha_1"), val = fp32(-0x1.02acap-2)];
            tensor<fp16, [1, 256, 30]> var_375_cast_fp16 = leaky_relu(alpha = var_375_alpha_1, x = input_61_cast_fp16)[name = string("op_375_cast_fp16")];
            tensor<int32, [1]> var_379 = const()[name = string("op_379"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_25_cast_fp16 = reduce_mean(axes = var_379, keep_dims = var_66, x = var_375_cast_fp16)[name = string("mean_y_25_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_12_cast_fp16 = sub(x = var_375_cast_fp16, y = mean_y_25_cast_fp16)[name = string("sub_12_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_12_cast_fp16 = square(x = sub_12_cast_fp16)[name = string("square_12_cast_fp16")];
            tensor<int32, [1]> reduce_mean_25_axes_0 = const()[name = string("reduce_mean_25_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_25_keep_dims_0 = const()[name = string("reduce_mean_25_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_25_cast_fp16 = reduce_mean(axes = reduce_mean_25_axes_0, keep_dims = reduce_mean_25_keep_dims_0, x = square_12_cast_fp16)[name = string("reduce_mean_25_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_12_cast_fp16 = sqrt(x = reduce_mean_25_cast_fp16)[name = string("sqrt_12_cast_fp16")];
            fp16 mul_12_y_0_to_fp16 = const()[name = string("mul_12_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_12_cast_fp16 = mul(x = sqrt_12_cast_fp16, y = mul_12_y_0_to_fp16)[name = string("mul_12_cast_fp16")];
            fp16 var_383_to_fp16 = const()[name = string("op_383_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_25_cast_fp16 = add(x = mul_12_cast_fp16, y = var_383_to_fp16)[name = string("std_y_25_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_5_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_5_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1400768)))];
            tensor<fp16, [1, 256, 30]> var_386_cast_fp16 = mul(x = sep_module_5_tcn_6_norm_gamma_to_fp16, y = sub_12_cast_fp16)[name = string("op_386_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_387_cast_fp16 = real_div(x = var_386_cast_fp16, y = std_y_25_cast_fp16)[name = string("op_387_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_5_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_5_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1401344)))];
            tensor<fp16, [1, 256, 30]> y_12_cast_fp16 = add(x = var_387_cast_fp16, y = sep_module_5_tcn_6_norm_beta_to_fp16)[name = string("y_12_cast_fp16")];
            tensor<fp16, [1, 256, 30]> input_63_cast_fp16 = add(x = input_53_cast_fp16, y = y_12_cast_fp16)[name = string("input_63_cast_fp16")];
            tensor<int32, [1]> var_398 = const()[name = string("op_398"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_400 = const()[name = string("op_400"), val = tensor<int32, [1]>([1])];
            string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_6_tcn_0_weight_to_fp16 = const()[name = string("sep_module_6_tcn_0_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1401920)))];
            tensor<fp16, [1, 256, 30]> input_65_cast_fp16 = conv(dilations = var_400, groups = var_62, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = var_398, weight = sep_module_6_tcn_0_weight_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")];
            fp32 var_404_alpha_1 = const()[name = string("op_404_alpha_1"), val = fp32(0x1.d02688p-2)];
            tensor<fp16, [1, 256, 30]> var_404_cast_fp16 = leaky_relu(alpha = var_404_alpha_1, x = input_65_cast_fp16)[name = string("op_404_cast_fp16")];
            tensor<int32, [1]> var_408 = const()[name = string("op_408"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_27_cast_fp16 = reduce_mean(axes = var_408, keep_dims = var_66, x = var_404_cast_fp16)[name = string("mean_y_27_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_13_cast_fp16 = sub(x = var_404_cast_fp16, y = mean_y_27_cast_fp16)[name = string("sub_13_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_13_cast_fp16 = square(x = sub_13_cast_fp16)[name = string("square_13_cast_fp16")];
            tensor<int32, [1]> reduce_mean_27_axes_0 = const()[name = string("reduce_mean_27_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_27_keep_dims_0 = const()[name = string("reduce_mean_27_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_27_cast_fp16 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = square_13_cast_fp16)[name = string("reduce_mean_27_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_13_cast_fp16 = sqrt(x = reduce_mean_27_cast_fp16)[name = string("sqrt_13_cast_fp16")];
            fp16 mul_13_y_0_to_fp16 = const()[name = string("mul_13_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_13_cast_fp16 = mul(x = sqrt_13_cast_fp16, y = mul_13_y_0_to_fp16)[name = string("mul_13_cast_fp16")];
            fp16 var_412_to_fp16 = const()[name = string("op_412_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_27_cast_fp16 = add(x = mul_13_cast_fp16, y = var_412_to_fp16)[name = string("std_y_27_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_6_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_6_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533056)))];
            tensor<fp16, [1, 256, 30]> var_415_cast_fp16 = mul(x = sep_module_6_tcn_2_norm_gamma_to_fp16, y = sub_13_cast_fp16)[name = string("op_415_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_416_cast_fp16 = real_div(x = var_415_cast_fp16, y = std_y_27_cast_fp16)[name = string("op_416_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_6_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_6_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533632)))];
            tensor<fp16, [1, 256, 30]> input_67_cast_fp16 = add(x = var_416_cast_fp16, y = sep_module_6_tcn_2_norm_beta_to_fp16)[name = string("input_67_cast_fp16")];
            tensor<int32, [6]> input_69_pad_0 = const()[name = string("input_69_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 128, 0])];
            string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("constant")];
            fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 158]> input_69_cast_fp16 = concat(axis = int32(-1), interleave = bool(false), values = (input_67_cast_fp16_in_state, input_67_cast_fp16));
            tensor<fp16, [1, 256, 128]> input_67_cast_fp16_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -128]), size = tensor<int32, [3]>([1, 256, 128]), x = input_69_cast_fp16);
            tensor<int32, [1]> var_421 = const()[name = string("op_421"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_423 = const()[name = string("op_423"), val = tensor<int32, [1]>([64])];
            string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_6_tcn_4_weight_to_fp16 = const()[name = string("sep_module_6_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1534208)))];
            tensor<fp16, [1, 256, 30]> input_71_cast_fp16 = conv(dilations = var_423, groups = var_63, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = var_421, weight = sep_module_6_tcn_4_weight_to_fp16, x = input_69_cast_fp16)[name = string("input_71_cast_fp16")];
            fp32 var_427_alpha_1 = const()[name = string("op_427_alpha_1"), val = fp32(0x1.d0bef4p-2)];
            tensor<fp16, [1, 256, 30]> var_427_cast_fp16 = leaky_relu(alpha = var_427_alpha_1, x = input_71_cast_fp16)[name = string("op_427_cast_fp16")];
            tensor<int32, [1]> var_431 = const()[name = string("op_431"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_29_cast_fp16 = reduce_mean(axes = var_431, keep_dims = var_66, x = var_427_cast_fp16)[name = string("mean_y_29_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_14_cast_fp16 = sub(x = var_427_cast_fp16, y = mean_y_29_cast_fp16)[name = string("sub_14_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_14_cast_fp16 = square(x = sub_14_cast_fp16)[name = string("square_14_cast_fp16")];
            tensor<int32, [1]> reduce_mean_29_axes_0 = const()[name = string("reduce_mean_29_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_29_keep_dims_0 = const()[name = string("reduce_mean_29_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_29_cast_fp16 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_14_cast_fp16)[name = string("reduce_mean_29_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_14_cast_fp16 = sqrt(x = reduce_mean_29_cast_fp16)[name = string("sqrt_14_cast_fp16")];
            fp16 mul_14_y_0_to_fp16 = const()[name = string("mul_14_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_14_cast_fp16 = mul(x = sqrt_14_cast_fp16, y = mul_14_y_0_to_fp16)[name = string("mul_14_cast_fp16")];
            fp16 var_435_to_fp16 = const()[name = string("op_435_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_29_cast_fp16 = add(x = mul_14_cast_fp16, y = var_435_to_fp16)[name = string("std_y_29_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_6_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_6_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1535808)))];
            tensor<fp16, [1, 256, 30]> var_438_cast_fp16 = mul(x = sep_module_6_tcn_6_norm_gamma_to_fp16, y = sub_14_cast_fp16)[name = string("op_438_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_439_cast_fp16 = real_div(x = var_438_cast_fp16, y = std_y_29_cast_fp16)[name = string("op_439_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_6_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_6_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1536384)))];
            tensor<fp16, [1, 256, 30]> y_14_cast_fp16 = add(x = var_439_cast_fp16, y = sep_module_6_tcn_6_norm_beta_to_fp16)[name = string("y_14_cast_fp16")];
            tensor<fp16, [1, 256, 30]> input_3_cast_fp16 = add(x = input_63_cast_fp16, y = y_14_cast_fp16)[name = string("input_3_cast_fp16")];
            tensor<int32, [1]> var_450 = const()[name = string("op_450"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_452 = const()[name = string("op_452"), val = tensor<int32, [1]>([1])];
            string input_2_pad_type_0 = const()[name = string("input_2_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_2_pad_0 = const()[name = string("input_2_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 256, 1]> sep_module_7_tcn_0_weight_to_fp16 = const()[name = string("sep_module_7_tcn_0_weight_to_fp16"), val = tensor<fp16, [256, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1536960)))];
            tensor<fp16, [1, 256, 30]> input_2_cast_fp16 = conv(dilations = var_452, groups = var_62, pad = input_2_pad_0, pad_type = input_2_pad_type_0, strides = var_450, weight = sep_module_7_tcn_0_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_2_cast_fp16")];
            fp32 var_456_alpha_1 = const()[name = string("op_456_alpha_1"), val = fp32(0x1.fffea2p-1)];
            tensor<fp16, [1, 256, 30]> var_456_cast_fp16 = leaky_relu(alpha = var_456_alpha_1, x = input_2_cast_fp16)[name = string("op_456_cast_fp16")];
            tensor<int32, [1]> var_460 = const()[name = string("op_460"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_2_cast_fp16 = reduce_mean(axes = var_460, keep_dims = var_66, x = var_456_cast_fp16)[name = string("mean_y_2_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_15_cast_fp16 = sub(x = var_456_cast_fp16, y = mean_y_2_cast_fp16)[name = string("sub_15_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_15_cast_fp16 = square(x = sub_15_cast_fp16)[name = string("square_15_cast_fp16")];
            tensor<int32, [1]> reduce_mean_31_axes_0 = const()[name = string("reduce_mean_31_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_31_keep_dims_0 = const()[name = string("reduce_mean_31_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_31_cast_fp16 = reduce_mean(axes = reduce_mean_31_axes_0, keep_dims = reduce_mean_31_keep_dims_0, x = square_15_cast_fp16)[name = string("reduce_mean_31_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_15_cast_fp16 = sqrt(x = reduce_mean_31_cast_fp16)[name = string("sqrt_15_cast_fp16")];
            fp16 mul_15_y_0_to_fp16 = const()[name = string("mul_15_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_15_cast_fp16 = mul(x = sqrt_15_cast_fp16, y = mul_15_y_0_to_fp16)[name = string("mul_15_cast_fp16")];
            fp16 var_464_to_fp16 = const()[name = string("op_464_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_2_cast_fp16 = add(x = mul_15_cast_fp16, y = var_464_to_fp16)[name = string("std_y_2_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_7_tcn_2_norm_gamma_to_fp16 = const()[name = string("sep_module_7_tcn_2_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1668096)))];
            tensor<fp16, [1, 256, 30]> var_467_cast_fp16 = mul(x = sep_module_7_tcn_2_norm_gamma_to_fp16, y = sub_15_cast_fp16)[name = string("op_467_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_468_cast_fp16 = real_div(x = var_467_cast_fp16, y = std_y_2_cast_fp16)[name = string("op_468_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_7_tcn_2_norm_beta_to_fp16 = const()[name = string("sep_module_7_tcn_2_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1668672)))];
            tensor<fp16, [1, 256, 30]> input_4_cast_fp16 = add(x = var_468_cast_fp16, y = sep_module_7_tcn_2_norm_beta_to_fp16)[name = string("input_4_cast_fp16")];
            tensor<int32, [6]> input_6_pad_0 = const()[name = string("input_6_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 0])];
            string input_6_mode_0 = const()[name = string("input_6_mode_0"), val = string("constant")];
            fp16 const_7_to_fp16 = const()[name = string("const_7_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 256, 286]> input_6_cast_fp16 = concat(axis = int32(-1), interleave = bool(false), values = (input_4_cast_fp16_in_state, input_4_cast_fp16));
            tensor<fp16, [1, 256, 256]> input_4_cast_fp16_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -256]), size = tensor<int32, [3]>([1, 256, 256]), x = input_6_cast_fp16);
            tensor<int32, [1]> var_473 = const()[name = string("op_473"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> var_475 = const()[name = string("op_475"), val = tensor<int32, [1]>([128])];
            string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<fp16, [256, 1, 3]> sep_module_7_tcn_4_weight_to_fp16 = const()[name = string("sep_module_7_tcn_4_weight_to_fp16"), val = tensor<fp16, [256, 1, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1669248)))];
            tensor<fp16, [1, 256, 30]> input_1_cast_fp16 = conv(dilations = var_475, groups = var_63, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_473, weight = sep_module_7_tcn_4_weight_to_fp16, x = input_6_cast_fp16)[name = string("input_1_cast_fp16")];
            fp32 var_479_alpha_1 = const()[name = string("op_479_alpha_1"), val = fp32(0x1.fffb6p-1)];
            tensor<fp16, [1, 256, 30]> var_479_cast_fp16 = leaky_relu(alpha = var_479_alpha_1, x = input_1_cast_fp16)[name = string("op_479_cast_fp16")];
            tensor<int32, [1]> var_483 = const()[name = string("op_483"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 30]> mean_y_1_cast_fp16 = reduce_mean(axes = var_483, keep_dims = var_66, x = var_479_cast_fp16)[name = string("mean_y_1_cast_fp16")];
            tensor<fp16, [1, 256, 30]> sub_16_cast_fp16 = sub(x = var_479_cast_fp16, y = mean_y_1_cast_fp16)[name = string("sub_16_cast_fp16")];
            tensor<fp16, [1, 256, 30]> square_16_cast_fp16 = square(x = sub_16_cast_fp16)[name = string("square_16_cast_fp16")];
            tensor<int32, [1]> reduce_mean_33_axes_0 = const()[name = string("reduce_mean_33_axes_0"), val = tensor<int32, [1]>([1])];
            bool reduce_mean_33_keep_dims_0 = const()[name = string("reduce_mean_33_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 1, 30]> reduce_mean_33_cast_fp16 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = square_16_cast_fp16)[name = string("reduce_mean_33_cast_fp16")];
            tensor<fp16, [1, 1, 30]> sqrt_16_cast_fp16 = sqrt(x = reduce_mean_33_cast_fp16)[name = string("sqrt_16_cast_fp16")];
            fp16 mul_16_y_0_to_fp16 = const()[name = string("mul_16_y_0_to_fp16"), val = fp16(0x1.008p+0)];
            tensor<fp16, [1, 1, 30]> mul_16_cast_fp16 = mul(x = sqrt_16_cast_fp16, y = mul_16_y_0_to_fp16)[name = string("mul_16_cast_fp16")];
            fp16 var_487_to_fp16 = const()[name = string("op_487_to_fp16"), val = fp16(0x1p-24)];
            tensor<fp16, [1, 1, 30]> std_y_1_cast_fp16 = add(x = mul_16_cast_fp16, y = var_487_to_fp16)[name = string("std_y_1_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_7_tcn_6_norm_gamma_to_fp16 = const()[name = string("sep_module_7_tcn_6_norm_gamma_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1670848)))];
            tensor<fp16, [1, 256, 30]> var_490_cast_fp16 = mul(x = sep_module_7_tcn_6_norm_gamma_to_fp16, y = sub_16_cast_fp16)[name = string("op_490_cast_fp16")];
            tensor<fp16, [1, 256, 30]> var_491_cast_fp16 = real_div(x = var_490_cast_fp16, y = std_y_1_cast_fp16)[name = string("op_491_cast_fp16")];
            tensor<fp16, [1, 256, 1]> sep_module_7_tcn_6_norm_beta_to_fp16 = const()[name = string("sep_module_7_tcn_6_norm_beta_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1671424)))];
            tensor<fp16, [1, 256, 30]> y_1_cast_fp16 = add(x = var_491_cast_fp16, y = sep_module_7_tcn_6_norm_beta_to_fp16)[name = string("y_1_cast_fp16")];
            tensor<fp16, [1, 256, 30]> x_1_cast_fp16 = add(x = input_3_cast_fp16, y = y_1_cast_fp16)[name = string("x_1_cast_fp16")];
            tensor<int32, [1]> input0_3_axes_0 = const()[name = string("input0_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 256, 30]> input0_3_cast_fp16 = expand_dims(axes = input0_3_axes_0, x = x_1_cast_fp16)[name = string("input0_3_cast_fp16")];
            int32 var_497 = const()[name = string("op_497"), val = int32(1)];
            tensor<int32, [2]> var_502 = const()[name = string("op_502"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_504 = const()[name = string("op_504"), val = tensor<int32, [2]>([1, 1])];
            string input1_1_pad_type_0 = const()[name = string("input1_1_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input1_1_pad_0 = const()[name = string("input1_1_pad_0"), val = tensor<int32, [4]>([256, 256, 0, 0])];
            tensor<fp16, [11, 1, 385, 1]> mask_layer_weight_to_fp16 = const()[name = string("mask_layer_weight_to_fp16"), val = tensor<fp16, [11, 1, 385, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672000)))];
            tensor<fp16, [1, 11, 384, 30]> input1_1_cast_fp16 = conv(dilations = var_504, groups = var_497, pad = input1_1_pad_0, pad_type = input1_1_pad_type_0, strides = var_502, weight = mask_layer_weight_to_fp16, x = input0_3_cast_fp16)[name = string("input1_1_cast_fp16")];
            tensor<fp16, [1, 11, 384, 30]> var_507_cast_fp16 = tanh(x = input1_1_cast_fp16)[name = string("op_507_cast_fp16")];
            tensor<int32, [1]> var_508_axes_0 = const()[name = string("op_508_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 384, 30]> var_508_cast_fp16 = expand_dims(axes = var_508_axes_0, x = var_26_cast_fp16)[name = string("op_508_cast_fp16")];
            tensor<fp16, [1, 1, 384, 45]> var_508_cast_fp16_concat_expanded = concat(axis = int32(-1), interleave = bool(false), values = (var_508_cast_fp16_concat_in_state, var_508_cast_fp16));
            tensor<fp16, [1, 1, 384, 30]> var_508_cast_fp16_delayed = slice_by_size(begin = tensor<int32, [4]>([0, 0, 0, 0]), size = tensor<int32, [4]>([1, 1, 384, 30]), x = var_508_cast_fp16_concat_expanded);
            tensor<fp16, [1, 1, 384, 15]> var_508_cast_fp16_concat_out_state = slice_by_size(begin = tensor<int32, [4]>([0, 0, 0, -15]), size = tensor<int32, [4]>([1, 1, 384, 15]), x = var_508_cast_fp16_concat_expanded);
            tensor<fp16, [1, 11, 384, 30]> x_11_cast_fp16 = mul(x = var_507_cast_fp16, y = var_508_cast_fp16_delayed)[name = string("x_11_cast_fp16")];
            tensor<int32, [3]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [3]>([1, 4224, -1])];
            tensor<fp16, [1, 4224, 30]> input1_3_cast_fp16 = reshape(shape = concat_0x, x = x_11_cast_fp16)[name = string("input1_3_cast_fp16")];
            int32 var_524 = const()[name = string("op_524"), val = int32(11)];
            tensor<int32, [1]> var_532 = const()[name = string("op_532"), val = tensor<int32, [1]>([64])];
            tensor<int32, [1]> var_534 = const()[name = string("op_534"), val = tensor<int32, [1]>([1])];
            string var_536_pad_type_0 = const()[name = string("op_536_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> var_536_pad_0 = const()[name = string("op_536_pad_0"), val = tensor<int32, [2]>([64, 64])];
            tensor<fp16, [4224, 1, 128]> resynthesizer_weight_to_fp16 = const()[name = string("resynthesizer_weight_to_fp16"), val = tensor<fp16, [4224, 1, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1680576)))];
            tensor<fp16, [1, 4224, 31]> tmp_0 = concat(axis = int32(-1), interleave = bool(false), values = (input1_3_cast_fp16_in_state, input1_3_cast_fp16));
            tensor<fp16, [1, 4224, 1]> input1_3_cast_fp16_out_state = slice_by_size(begin = tensor<int32, [3]>([0, 0, -1]), size = tensor<int32, [3]>([1, 4224, 1]), x = tmp_0);
            tensor<fp16, [1, 11, 1920]> var_536_cast_fp16 = conv_transpose(dilations = var_534, groups = var_524, pad = var_536_pad_0, pad_type = var_536_pad_type_0, strides = var_532, weight = resynthesizer_weight_to_fp16, x = tmp_0);
            string var_536_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_536_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
            tensor<fp32, [1, 11, 1920]> var_536 = cast(dtype = var_536_cast_fp16_to_fp32_dtype_0, x = var_536_cast_fp16)[name = string("cast_35")];
        } -> (var_536, cast_36_out_state, input_7_cast_fp16_out_state, input_77_cast_fp16_concat_out_state, input_17_cast_fp16_out_state, input_13_cast_fp16_concat_out_state, input_27_cast_fp16_out_state, input_23_cast_fp16_concat_out_state, input_37_cast_fp16_out_state, input_33_cast_fp16_concat_out_state, input_47_cast_fp16_out_state, input_57_cast_fp16_out_state, input_67_cast_fp16_out_state, input_4_cast_fp16_out_state, var_508_cast_fp16_concat_out_state, input1_3_cast_fp16_out_state);
}