program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
{
    func main<ios18>(tensor<fp32, [1, 8, 16]> style_dp, tensor<int32, [1, 128]> text_ids, tensor<fp32, [1, 1, 128]> text_mask) {
            int32 var_25 = const()[name = string("op_25"), val = int32(2)];
            int32 var_26 = const()[name = string("op_26"), val = int32(1)];
            int32 var_27 = const()[name = string("op_27"), val = int32(-1)];
            int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)];
            bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)];
            tensor<fp16, [8322, 64]> m_char_embedder_weight_to_fp16 = const()[name = string("m_char_embedder_weight_to_fp16"), val = tensor<fp16, [8322, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
            string text_ids_to_int16_dtype_0 = const()[name = string("text_ids_to_int16_dtype_0"), val = string("int16")];
            string cast_19_dtype_0 = const()[name = string("cast_19_dtype_0"), val = string("int32")];
            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
            tensor<int16, [1, 128]> text_ids_to_int16 = cast(dtype = text_ids_to_int16_dtype_0, x = text_ids)[name = string("cast_25")];
            tensor<int32, [1, 128]> cast_19 = cast(dtype = cast_19_dtype_0, x = text_ids_to_int16)[name = string("cast_24")];
            tensor<bool, [1, 128]> greater_equal_0 = greater_equal(x = cast_19, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
            int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(8322)];
            tensor<int32, [1, 128]> add_0 = add(x = cast_19, y = slice_by_index_0)[name = string("add_0")];
            tensor<int32, [1, 128]> select_0 = select(a = cast_19, b = add_0, cond = greater_equal_0)[name = string("select_0")];
            int32 x_1_cast_fp16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_axis_0"), val = int32(0)];
            string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")];
            tensor<int16, [1, 128]> select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_23")];
            tensor<fp16, [1, 128, 64]> x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_to_int16, validate_indices = x_1_validate_indices_0, x = m_char_embedder_weight_to_fp16)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")];
            tensor<int32, [3]> var_53_perm_0 = const()[name = string("op_53_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [1, 1, 128]> text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_22")];
            tensor<fp16, [1, 64, 128]> var_53_cast_fp16 = transpose(perm = var_53_perm_0, x = x_1_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_24")];
            tensor<fp16, [1, 64, 128]> x_3_cast_fp16 = mul(x = var_53_cast_fp16, y = text_mask_to_fp16)[name = string("x_3_cast_fp16")];
            bool x_5_interleave_0 = const()[name = string("x_5_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 64, 1]> m_sentence_token_to_fp16 = const()[name = string("m_sentence_token_to_fp16"), val = tensor<fp16, [1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1065344)))];
            tensor<fp16, [1, 64, 129]> x_5_cast_fp16 = concat(axis = var_25, interleave = x_5_interleave_0, values = (m_sentence_token_to_fp16, x_3_cast_fp16))[name = string("x_5_cast_fp16")];
            bool mask_interleave_0 = const()[name = string("mask_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 1, 1]> fill_0_to_fp16 = const()[name = string("fill_0_to_fp16"), val = tensor<fp16, [1, 1, 1]>([[[0x1p+0]]])];
            tensor<fp16, [1, 1, 129]> mask_cast_fp16 = concat(axis = var_25, interleave = mask_interleave_0, values = (fill_0_to_fp16, text_mask_to_fp16))[name = string("mask_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_7_cast_fp16 = mul(x = x_5_cast_fp16, y = mask_cast_fp16)[name = string("x_7_cast_fp16")];
            tensor<int32, [3]> input_1_begin_0 = const()[name = string("input_1_begin_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<int32, [3]> input_1_end_0 = const()[name = string("input_1_end_0"), val = tensor<int32, [3]>([1, 1, 129])];
            tensor<bool, [3]> input_1_end_mask_0 = const()[name = string("input_1_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 1, 128]> input_1_cast_fp16 = slice_by_index(begin = input_1_begin_0, end = input_1_end_0, end_mask = input_1_end_mask_0, x = mask_cast_fp16)[name = string("input_1_cast_fp16")];
            tensor<int32, [6]> var_71_pad_0 = const()[name = string("op_71_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 1])];
            string var_71_mode_0 = const()[name = string("op_71_mode_0"), val = string("constant")];
            fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1, 129]> var_71_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = var_71_mode_0, pad = var_71_pad_0, x = input_1_cast_fp16)[name = string("op_71_cast_fp16")];
            fp16 var_16_to_fp16 = const()[name = string("op_16_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [1, 1, 129]> var_72_cast_fp16 = sub(x = var_16_to_fp16, y = var_71_cast_fp16)[name = string("op_72_cast_fp16")];
            tensor<fp16, [1, 1, 129]> last_1_cast_fp16 = mul(x = mask_cast_fp16, y = var_72_cast_fp16)[name = string("last_1_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_74_cast_fp16 = mul(x = x_7_cast_fp16, y = last_1_cast_fp16)[name = string("op_74_cast_fp16")];
            tensor<int32, [1]> x_last_1_axes_0 = const()[name = string("x_last_1_axes_0"), val = tensor<int32, [1]>([-1])];
            bool x_last_1_keep_dims_0 = const()[name = string("x_last_1_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1]> x_last_1_cast_fp16 = reduce_sum(axes = x_last_1_axes_0, keep_dims = x_last_1_keep_dims_0, x = var_74_cast_fp16)[name = string("x_last_1_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_77_cast_fp16 = mul(x = x_7_cast_fp16, y = mask_cast_fp16)[name = string("op_77_cast_fp16")];
            tensor<fp16, [1, 1, 129]> var_78_cast_fp16 = sub(x = var_16_to_fp16, y = mask_cast_fp16)[name = string("op_78_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_79_cast_fp16 = mul(x = var_78_cast_fp16, y = x_last_1_cast_fp16)[name = string("op_79_cast_fp16")];
            tensor<fp16, [1, 64, 129]> input_3_cast_fp16 = add(x = var_77_cast_fp16, y = var_79_cast_fp16)[name = string("input_3_cast_fp16")];
            tensor<int32, [6]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            string input_5_mode_0 = const()[name = string("input_5_mode_0"), val = string("replicate")];
            fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 64, 133]> input_5_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")];
            string h_1_pad_type_0 = const()[name = string("h_1_pad_type_0"), val = string("valid")];
            int32 h_1_groups_0 = const()[name = string("h_1_groups_0"), val = int32(64)];
            tensor<int32, [1]> h_1_strides_0 = const()[name = string("h_1_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_1_pad_0 = const()[name = string("h_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_1_dilations_0 = const()[name = string("h_1_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [64, 1, 5]> m_convnext_0_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_0_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1065536)))];
            tensor<fp16, [64]> m_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066240)))];
            tensor<fp16, [1, 64, 129]> h_1_cast_fp16 = conv(bias = m_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_1_dilations_0, groups = h_1_groups_0, pad = h_1_pad_0, pad_type = h_1_pad_type_0, strides = h_1_strides_0, weight = m_convnext_0_dwconv__conv_weight_to_fp16, x = input_5_cast_fp16)[name = string("h_1_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_9_cast_fp16 = mul(x = h_1_cast_fp16, y = mask_cast_fp16)[name = string("x_9_cast_fp16")];
            tensor<int32, [3]> input_7_perm_0 = const()[name = string("input_7_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<int32, [1]> var_97_axes_0 = const()[name = string("op_97_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [64]> m_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_0_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066432)))];
            tensor<fp16, [64]> m_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_0_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066624)))];
            fp16 var_20_to_fp16 = const()[name = string("op_20_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 129, 64]> input_7_cast_fp16 = transpose(perm = input_7_perm_0, x = x_9_cast_fp16)[name = string("transpose_23")];
            tensor<fp16, [1, 129, 64]> var_97_cast_fp16 = layer_norm(axes = var_97_axes_0, beta = m_convnext_0_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_0_norm_norm_weight_to_fp16, x = input_7_cast_fp16)[name = string("op_97_cast_fp16")];
            tensor<int32, [3]> input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(1)];
            tensor<fp16, [256, 64, 1]> m_convnext_0_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_0_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066816)))];
            tensor<fp16, [256]> m_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_0_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1099648)))];
            tensor<fp16, [1, 64, 129]> input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = var_97_cast_fp16)[name = string("transpose_22")];
            tensor<fp16, [1, 256, 129]> h_3_cast_fp16 = conv(bias = m_convnext_0_pwconv1_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = m_convnext_0_pwconv1_weight_to_fp16, x = input_9_cast_fp16)[name = string("h_3_cast_fp16")];
            string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 256, 129]> input_11_cast_fp16 = gelu(mode = input_11_mode_0, x = h_3_cast_fp16)[name = string("input_11_cast_fp16")];
            string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)];
            tensor<fp16, [64, 256, 1]> var_114_weight_0_to_fp16 = const()[name = string("op_114_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1100224)))];
            tensor<fp16, [64]> var_114_bias_0_to_fp16 = const()[name = string("op_114_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1133056)))];
            tensor<fp16, [1, 64, 129]> var_114_cast_fp16 = conv(bias = var_114_bias_0_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = var_114_weight_0_to_fp16, x = input_11_cast_fp16)[name = string("op_114_cast_fp16")];
            tensor<fp16, [1, 64, 129]> out_1_cast_fp16 = add(x = x_7_cast_fp16, y = var_114_cast_fp16)[name = string("out_1_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_11_cast_fp16 = mul(x = out_1_cast_fp16, y = mask_cast_fp16)[name = string("x_11_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_13_cast_fp16 = mul(x = x_11_cast_fp16, y = mask_cast_fp16)[name = string("x_13_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_128_cast_fp16 = mul(x = x_13_cast_fp16, y = last_1_cast_fp16)[name = string("op_128_cast_fp16")];
            tensor<int32, [1]> x_last_3_axes_0 = const()[name = string("x_last_3_axes_0"), val = tensor<int32, [1]>([-1])];
            bool x_last_3_keep_dims_0 = const()[name = string("x_last_3_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1]> x_last_3_cast_fp16 = reduce_sum(axes = x_last_3_axes_0, keep_dims = x_last_3_keep_dims_0, x = var_128_cast_fp16)[name = string("x_last_3_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_131_cast_fp16 = mul(x = x_13_cast_fp16, y = mask_cast_fp16)[name = string("op_131_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_133_cast_fp16 = mul(x = var_78_cast_fp16, y = x_last_3_cast_fp16)[name = string("op_133_cast_fp16")];
            tensor<fp16, [1, 64, 129]> input_15_cast_fp16 = add(x = var_131_cast_fp16, y = var_133_cast_fp16)[name = string("input_15_cast_fp16")];
            tensor<int32, [6]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("replicate")];
            fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 64, 133]> input_17_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_17_mode_0, pad = input_17_pad_0, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")];
            string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")];
            int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(64)];
            tensor<int32, [1]> h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [64, 1, 5]> m_convnext_1_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_1_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1133248)))];
            tensor<fp16, [64]> m_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1133952)))];
            tensor<fp16, [1, 64, 129]> h_7_cast_fp16 = conv(bias = m_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = m_convnext_1_dwconv__conv_weight_to_fp16, x = input_17_cast_fp16)[name = string("h_7_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_15_cast_fp16 = mul(x = h_7_cast_fp16, y = mask_cast_fp16)[name = string("x_15_cast_fp16")];
            tensor<int32, [3]> input_19_perm_0 = const()[name = string("input_19_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<int32, [1]> var_151_axes_0 = const()[name = string("op_151_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [64]> m_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_1_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1134144)))];
            tensor<fp16, [64]> m_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_1_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1134336)))];
            tensor<fp16, [1, 129, 64]> input_19_cast_fp16 = transpose(perm = input_19_perm_0, x = x_15_cast_fp16)[name = string("transpose_21")];
            tensor<fp16, [1, 129, 64]> var_151_cast_fp16 = layer_norm(axes = var_151_axes_0, beta = m_convnext_1_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_1_norm_norm_weight_to_fp16, x = input_19_cast_fp16)[name = string("op_151_cast_fp16")];
            tensor<int32, [3]> input_21_perm_0 = const()[name = string("input_21_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(1)];
            tensor<fp16, [256, 64, 1]> m_convnext_1_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_1_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1134528)))];
            tensor<fp16, [256]> m_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_1_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1167360)))];
            tensor<fp16, [1, 64, 129]> input_21_cast_fp16 = transpose(perm = input_21_perm_0, x = var_151_cast_fp16)[name = string("transpose_20")];
            tensor<fp16, [1, 256, 129]> h_9_cast_fp16 = conv(bias = m_convnext_1_pwconv1_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = m_convnext_1_pwconv1_weight_to_fp16, x = input_21_cast_fp16)[name = string("h_9_cast_fp16")];
            string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 256, 129]> input_23_cast_fp16 = gelu(mode = input_23_mode_0, x = h_9_cast_fp16)[name = string("input_23_cast_fp16")];
            string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)];
            tensor<fp16, [64, 256, 1]> var_168_weight_0_to_fp16 = const()[name = string("op_168_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1167936)))];
            tensor<fp16, [64]> var_168_bias_0_to_fp16 = const()[name = string("op_168_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1200768)))];
            tensor<fp16, [1, 64, 129]> var_168_cast_fp16 = conv(bias = var_168_bias_0_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = var_168_weight_0_to_fp16, x = input_23_cast_fp16)[name = string("op_168_cast_fp16")];
            tensor<fp16, [1, 64, 129]> out_3_cast_fp16 = add(x = x_13_cast_fp16, y = var_168_cast_fp16)[name = string("out_3_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_17_cast_fp16 = mul(x = out_3_cast_fp16, y = mask_cast_fp16)[name = string("x_17_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_19_cast_fp16 = mul(x = x_17_cast_fp16, y = mask_cast_fp16)[name = string("x_19_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_182_cast_fp16 = mul(x = x_19_cast_fp16, y = last_1_cast_fp16)[name = string("op_182_cast_fp16")];
            tensor<int32, [1]> x_last_5_axes_0 = const()[name = string("x_last_5_axes_0"), val = tensor<int32, [1]>([-1])];
            bool x_last_5_keep_dims_0 = const()[name = string("x_last_5_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1]> x_last_5_cast_fp16 = reduce_sum(axes = x_last_5_axes_0, keep_dims = x_last_5_keep_dims_0, x = var_182_cast_fp16)[name = string("x_last_5_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_185_cast_fp16 = mul(x = x_19_cast_fp16, y = mask_cast_fp16)[name = string("op_185_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_187_cast_fp16 = mul(x = var_78_cast_fp16, y = x_last_5_cast_fp16)[name = string("op_187_cast_fp16")];
            tensor<fp16, [1, 64, 129]> input_27_cast_fp16 = add(x = var_185_cast_fp16, y = var_187_cast_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, 2, 2])];
            string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("replicate")];
            fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 64, 133]> input_29_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_29_mode_0, pad = input_29_pad_0, x = input_27_cast_fp16)[name = string("input_29_cast_fp16")];
            string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")];
            int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(64)];
            tensor<int32, [1]> h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [64, 1, 5]> m_convnext_2_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_2_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1200960)))];
            tensor<fp16, [64]> m_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1201664)))];
            tensor<fp16, [1, 64, 129]> h_13_cast_fp16 = conv(bias = m_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = m_convnext_2_dwconv__conv_weight_to_fp16, x = input_29_cast_fp16)[name = string("h_13_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_21_cast_fp16 = mul(x = h_13_cast_fp16, y = mask_cast_fp16)[name = string("x_21_cast_fp16")];
            tensor<int32, [3]> input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<int32, [1]> var_205_axes_0 = const()[name = string("op_205_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [64]> m_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_2_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1201856)))];
            tensor<fp16, [64]> m_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_2_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1202048)))];
            tensor<fp16, [1, 129, 64]> input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_21_cast_fp16)[name = string("transpose_19")];
            tensor<fp16, [1, 129, 64]> var_205_cast_fp16 = layer_norm(axes = var_205_axes_0, beta = m_convnext_2_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_2_norm_norm_weight_to_fp16, x = input_31_cast_fp16)[name = string("op_205_cast_fp16")];
            tensor<int32, [3]> input_33_perm_0 = const()[name = string("input_33_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(1)];
            tensor<fp16, [256, 64, 1]> m_convnext_2_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_2_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1202240)))];
            tensor<fp16, [256]> m_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_2_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1235072)))];
            tensor<fp16, [1, 64, 129]> input_33_cast_fp16 = transpose(perm = input_33_perm_0, x = var_205_cast_fp16)[name = string("transpose_18")];
            tensor<fp16, [1, 256, 129]> h_15_cast_fp16 = conv(bias = m_convnext_2_pwconv1_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = m_convnext_2_pwconv1_weight_to_fp16, x = input_33_cast_fp16)[name = string("h_15_cast_fp16")];
            string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 256, 129]> input_35_cast_fp16 = gelu(mode = input_35_mode_0, x = h_15_cast_fp16)[name = string("input_35_cast_fp16")];
            string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)];
            tensor<fp16, [64, 256, 1]> var_222_weight_0_to_fp16 = const()[name = string("op_222_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1235648)))];
            tensor<fp16, [64]> var_222_bias_0_to_fp16 = const()[name = string("op_222_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1268480)))];
            tensor<fp16, [1, 64, 129]> var_222_cast_fp16 = conv(bias = var_222_bias_0_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = var_222_weight_0_to_fp16, x = input_35_cast_fp16)[name = string("op_222_cast_fp16")];
            tensor<fp16, [1, 64, 129]> out_5_cast_fp16 = add(x = x_19_cast_fp16, y = var_222_cast_fp16)[name = string("out_5_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_23_cast_fp16 = mul(x = out_5_cast_fp16, y = mask_cast_fp16)[name = string("x_23_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_25_cast_fp16 = mul(x = x_23_cast_fp16, y = mask_cast_fp16)[name = string("x_25_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_236_cast_fp16 = mul(x = x_25_cast_fp16, y = last_1_cast_fp16)[name = string("op_236_cast_fp16")];
            tensor<int32, [1]> x_last_7_axes_0 = const()[name = string("x_last_7_axes_0"), val = tensor<int32, [1]>([-1])];
            bool x_last_7_keep_dims_0 = const()[name = string("x_last_7_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1]> x_last_7_cast_fp16 = reduce_sum(axes = x_last_7_axes_0, keep_dims = x_last_7_keep_dims_0, x = var_236_cast_fp16)[name = string("x_last_7_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_239_cast_fp16 = mul(x = x_25_cast_fp16, y = mask_cast_fp16)[name = string("op_239_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_241_cast_fp16 = mul(x = var_78_cast_fp16, y = x_last_7_cast_fp16)[name = string("op_241_cast_fp16")];
            tensor<fp16, [1, 64, 129]> input_39_cast_fp16 = add(x = var_239_cast_fp16, y = var_241_cast_fp16)[name = string("input_39_cast_fp16")];
            tensor<int32, [6]> input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("replicate")];
            fp16 const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 64, 133]> input_41_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = input_41_mode_0, pad = input_41_pad_0, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")];
            string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")];
            int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(64)];
            tensor<int32, [1]> h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [64, 1, 5]> m_convnext_3_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_3_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1268672)))];
            tensor<fp16, [64]> m_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269376)))];
            tensor<fp16, [1, 64, 129]> h_19_cast_fp16 = conv(bias = m_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = m_convnext_3_dwconv__conv_weight_to_fp16, x = input_41_cast_fp16)[name = string("h_19_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_27_cast_fp16 = mul(x = h_19_cast_fp16, y = mask_cast_fp16)[name = string("x_27_cast_fp16")];
            tensor<int32, [3]> input_43_perm_0 = const()[name = string("input_43_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<int32, [1]> var_259_axes_0 = const()[name = string("op_259_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [64]> m_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_3_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269568)))];
            tensor<fp16, [64]> m_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_3_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269760)))];
            tensor<fp16, [1, 129, 64]> input_43_cast_fp16 = transpose(perm = input_43_perm_0, x = x_27_cast_fp16)[name = string("transpose_17")];
            tensor<fp16, [1, 129, 64]> var_259_cast_fp16 = layer_norm(axes = var_259_axes_0, beta = m_convnext_3_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_3_norm_norm_weight_to_fp16, x = input_43_cast_fp16)[name = string("op_259_cast_fp16")];
            tensor<int32, [3]> input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(1)];
            tensor<fp16, [256, 64, 1]> m_convnext_3_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_3_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269952)))];
            tensor<fp16, [256]> m_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_3_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1302784)))];
            tensor<fp16, [1, 64, 129]> input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = var_259_cast_fp16)[name = string("transpose_16")];
            tensor<fp16, [1, 256, 129]> h_21_cast_fp16 = conv(bias = m_convnext_3_pwconv1_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = m_convnext_3_pwconv1_weight_to_fp16, x = input_45_cast_fp16)[name = string("h_21_cast_fp16")];
            string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 256, 129]> input_47_cast_fp16 = gelu(mode = input_47_mode_0, x = h_21_cast_fp16)[name = string("input_47_cast_fp16")];
            string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)];
            tensor<fp16, [64, 256, 1]> var_276_weight_0_to_fp16 = const()[name = string("op_276_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1303360)))];
            tensor<fp16, [64]> var_276_bias_0_to_fp16 = const()[name = string("op_276_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1336192)))];
            tensor<fp16, [1, 64, 129]> var_276_cast_fp16 = conv(bias = var_276_bias_0_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = var_276_weight_0_to_fp16, x = input_47_cast_fp16)[name = string("op_276_cast_fp16")];
            tensor<fp16, [1, 64, 129]> out_7_cast_fp16 = add(x = x_25_cast_fp16, y = var_276_cast_fp16)[name = string("out_7_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_29_cast_fp16 = mul(x = out_7_cast_fp16, y = mask_cast_fp16)[name = string("x_29_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = mask_cast_fp16)[name = string("x_31_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_290_cast_fp16 = mul(x = x_31_cast_fp16, y = last_1_cast_fp16)[name = string("op_290_cast_fp16")];
            tensor<int32, [1]> x_last_9_axes_0 = const()[name = string("x_last_9_axes_0"), val = tensor<int32, [1]>([-1])];
            bool x_last_9_keep_dims_0 = const()[name = string("x_last_9_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1]> x_last_9_cast_fp16 = reduce_sum(axes = x_last_9_axes_0, keep_dims = x_last_9_keep_dims_0, x = var_290_cast_fp16)[name = string("x_last_9_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_293_cast_fp16 = mul(x = x_31_cast_fp16, y = mask_cast_fp16)[name = string("op_293_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_295_cast_fp16 = mul(x = var_78_cast_fp16, y = x_last_9_cast_fp16)[name = string("op_295_cast_fp16")];
            tensor<fp16, [1, 64, 129]> input_51_cast_fp16 = add(x = var_293_cast_fp16, y = var_295_cast_fp16)[name = string("input_51_cast_fp16")];
            tensor<int32, [6]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("replicate")];
            fp16 const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 64, 133]> input_53_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_53_mode_0, pad = input_53_pad_0, x = input_51_cast_fp16)[name = string("input_53_cast_fp16")];
            string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")];
            int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(64)];
            tensor<int32, [1]> h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [64, 1, 5]> m_convnext_4_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_4_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1336384)))];
            tensor<fp16, [64]> m_convnext_4_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_4_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337088)))];
            tensor<fp16, [1, 64, 129]> h_25_cast_fp16 = conv(bias = m_convnext_4_dwconv__conv_bias_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = m_convnext_4_dwconv__conv_weight_to_fp16, x = input_53_cast_fp16)[name = string("h_25_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_33_cast_fp16 = mul(x = h_25_cast_fp16, y = mask_cast_fp16)[name = string("x_33_cast_fp16")];
            tensor<int32, [3]> input_55_perm_0 = const()[name = string("input_55_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<int32, [1]> var_313_axes_0 = const()[name = string("op_313_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [64]> m_convnext_4_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_4_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337280)))];
            tensor<fp16, [64]> m_convnext_4_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_4_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337472)))];
            tensor<fp16, [1, 129, 64]> input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_33_cast_fp16)[name = string("transpose_15")];
            tensor<fp16, [1, 129, 64]> var_313_cast_fp16 = layer_norm(axes = var_313_axes_0, beta = m_convnext_4_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_4_norm_norm_weight_to_fp16, x = input_55_cast_fp16)[name = string("op_313_cast_fp16")];
            tensor<int32, [3]> input_57_perm_0 = const()[name = string("input_57_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(1)];
            tensor<fp16, [256, 64, 1]> m_convnext_4_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_4_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337664)))];
            tensor<fp16, [256]> m_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_4_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1370496)))];
            tensor<fp16, [1, 64, 129]> input_57_cast_fp16 = transpose(perm = input_57_perm_0, x = var_313_cast_fp16)[name = string("transpose_14")];
            tensor<fp16, [1, 256, 129]> h_27_cast_fp16 = conv(bias = m_convnext_4_pwconv1_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = m_convnext_4_pwconv1_weight_to_fp16, x = input_57_cast_fp16)[name = string("h_27_cast_fp16")];
            string input_59_mode_0 = const()[name = string("input_59_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 256, 129]> input_59_cast_fp16 = gelu(mode = input_59_mode_0, x = h_27_cast_fp16)[name = string("input_59_cast_fp16")];
            string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)];
            tensor<fp16, [64, 256, 1]> var_330_weight_0_to_fp16 = const()[name = string("op_330_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1371072)))];
            tensor<fp16, [64]> var_330_bias_0_to_fp16 = const()[name = string("op_330_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1403904)))];
            tensor<fp16, [1, 64, 129]> var_330_cast_fp16 = conv(bias = var_330_bias_0_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = var_330_weight_0_to_fp16, x = input_59_cast_fp16)[name = string("op_330_cast_fp16")];
            tensor<fp16, [1, 64, 129]> out_9_cast_fp16 = add(x = x_31_cast_fp16, y = var_330_cast_fp16)[name = string("out_9_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_35_cast_fp16 = mul(x = out_9_cast_fp16, y = mask_cast_fp16)[name = string("x_35_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_37_cast_fp16 = mul(x = x_35_cast_fp16, y = mask_cast_fp16)[name = string("x_37_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_344_cast_fp16 = mul(x = x_37_cast_fp16, y = last_1_cast_fp16)[name = string("op_344_cast_fp16")];
            tensor<int32, [1]> x_last_axes_0 = const()[name = string("x_last_axes_0"), val = tensor<int32, [1]>([-1])];
            bool x_last_keep_dims_0 = const()[name = string("x_last_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 64, 1]> x_last_cast_fp16 = reduce_sum(axes = x_last_axes_0, keep_dims = x_last_keep_dims_0, x = var_344_cast_fp16)[name = string("x_last_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_347_cast_fp16 = mul(x = x_37_cast_fp16, y = mask_cast_fp16)[name = string("op_347_cast_fp16")];
            tensor<fp16, [1, 64, 129]> var_349_cast_fp16 = mul(x = var_78_cast_fp16, y = x_last_cast_fp16)[name = string("op_349_cast_fp16")];
            tensor<fp16, [1, 64, 129]> input_63_cast_fp16 = add(x = var_347_cast_fp16, y = var_349_cast_fp16)[name = string("input_63_cast_fp16")];
            tensor<int32, [6]> input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
            string input_65_mode_0 = const()[name = string("input_65_mode_0"), val = string("replicate")];
            fp16 const_12_to_fp16 = const()[name = string("const_12_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 64, 133]> input_65_cast_fp16 = pad(constant_val = const_12_to_fp16, mode = input_65_mode_0, pad = input_65_pad_0, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")];
            string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")];
            int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(64)];
            tensor<int32, [1]> h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [64, 1, 5]> m_convnext_5_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_5_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1404096)))];
            tensor<fp16, [64]> m_convnext_5_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_5_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1404800)))];
            tensor<fp16, [1, 64, 129]> h_31_cast_fp16 = conv(bias = m_convnext_5_dwconv__conv_bias_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = m_convnext_5_dwconv__conv_weight_to_fp16, x = input_65_cast_fp16)[name = string("h_31_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_39_cast_fp16 = mul(x = h_31_cast_fp16, y = mask_cast_fp16)[name = string("x_39_cast_fp16")];
            tensor<int32, [3]> input_67_perm_0 = const()[name = string("input_67_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<int32, [1]> var_367_axes_0 = const()[name = string("op_367_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [64]> m_convnext_5_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_5_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1404992)))];
            tensor<fp16, [64]> m_convnext_5_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_5_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405184)))];
            tensor<fp16, [1, 129, 64]> input_67_cast_fp16 = transpose(perm = input_67_perm_0, x = x_39_cast_fp16)[name = string("transpose_13")];
            tensor<fp16, [1, 129, 64]> var_367_cast_fp16 = layer_norm(axes = var_367_axes_0, beta = m_convnext_5_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_5_norm_norm_weight_to_fp16, x = input_67_cast_fp16)[name = string("op_367_cast_fp16")];
            tensor<int32, [3]> input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(1)];
            tensor<fp16, [256, 64, 1]> m_convnext_5_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_5_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405376)))];
            tensor<fp16, [256]> m_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_5_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1438208)))];
            tensor<fp16, [1, 64, 129]> input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = var_367_cast_fp16)[name = string("transpose_12")];
            tensor<fp16, [1, 256, 129]> h_33_cast_fp16 = conv(bias = m_convnext_5_pwconv1_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = m_convnext_5_pwconv1_weight_to_fp16, x = input_69_cast_fp16)[name = string("h_33_cast_fp16")];
            string input_71_mode_0 = const()[name = string("input_71_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 256, 129]> input_71_cast_fp16 = gelu(mode = input_71_mode_0, x = h_33_cast_fp16)[name = string("input_71_cast_fp16")];
            string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)];
            tensor<fp16, [64, 256, 1]> var_384_weight_0_to_fp16 = const()[name = string("op_384_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1438784)))];
            tensor<fp16, [64]> var_384_bias_0_to_fp16 = const()[name = string("op_384_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1471616)))];
            tensor<fp16, [1, 64, 129]> var_384_cast_fp16 = conv(bias = var_384_bias_0_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = var_384_weight_0_to_fp16, x = input_71_cast_fp16)[name = string("op_384_cast_fp16")];
            tensor<fp16, [1, 64, 129]> out_11_cast_fp16 = add(x = x_37_cast_fp16, y = var_384_cast_fp16)[name = string("out_11_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_41_cast_fp16 = mul(x = out_11_cast_fp16, y = mask_cast_fp16)[name = string("x_41_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_43_cast_fp16 = mul(x = x_41_cast_fp16, y = mask_cast_fp16)[name = string("x_43_cast_fp16")];
            string var_407_pad_type_0 = const()[name = string("op_407_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_407_strides_0 = const()[name = string("op_407_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_407_pad_0 = const()[name = string("op_407_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_407_dilations_0 = const()[name = string("op_407_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_407_groups_0 = const()[name = string("op_407_groups_0"), val = int32(1)];
            tensor<fp16, [64, 64, 1]> m_attn_layers_0_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_q_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1471808)))];
            tensor<fp16, [64]> m_attn_layers_0_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_q_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1480064)))];
            tensor<fp16, [1, 64, 129]> var_407_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_q_bias_to_fp16, dilations = var_407_dilations_0, groups = var_407_groups_0, pad = var_407_pad_0, pad_type = var_407_pad_type_0, strides = var_407_strides_0, weight = m_attn_layers_0_attn_conv_q_weight_to_fp16, x = x_43_cast_fp16)[name = string("op_407_cast_fp16")];
            tensor<int32, [4]> var_408 = const()[name = string("op_408"), val = tensor<int32, [4]>([1, 2, 32, 129])];
            tensor<fp16, [1, 2, 32, 129]> var_409_cast_fp16 = reshape(shape = var_408, x = var_407_cast_fp16)[name = string("op_409_cast_fp16")];
            tensor<int32, [4]> q_1_perm_0 = const()[name = string("q_1_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            string var_417_pad_type_0 = const()[name = string("op_417_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_417_strides_0 = const()[name = string("op_417_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_417_pad_0 = const()[name = string("op_417_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_417_dilations_0 = const()[name = string("op_417_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_417_groups_0 = const()[name = string("op_417_groups_0"), val = int32(1)];
            tensor<fp16, [64, 64, 1]> m_attn_layers_0_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_k_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1480256)))];
            tensor<fp16, [64]> m_attn_layers_0_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_k_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1488512)))];
            tensor<fp16, [1, 64, 129]> var_417_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_k_bias_to_fp16, dilations = var_417_dilations_0, groups = var_417_groups_0, pad = var_417_pad_0, pad_type = var_417_pad_type_0, strides = var_417_strides_0, weight = m_attn_layers_0_attn_conv_k_weight_to_fp16, x = x_43_cast_fp16)[name = string("op_417_cast_fp16")];
            tensor<int32, [4]> var_418 = const()[name = string("op_418"), val = tensor<int32, [4]>([1, 2, 32, 129])];
            tensor<fp16, [1, 2, 32, 129]> var_419_cast_fp16 = reshape(shape = var_418, x = var_417_cast_fp16)[name = string("op_419_cast_fp16")];
            string var_427_pad_type_0 = const()[name = string("op_427_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_427_strides_0 = const()[name = string("op_427_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_427_pad_0 = const()[name = string("op_427_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_427_dilations_0 = const()[name = string("op_427_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_427_groups_0 = const()[name = string("op_427_groups_0"), val = int32(1)];
            tensor<fp16, [64, 64, 1]> m_attn_layers_0_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_v_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1488704)))];
            tensor<fp16, [64]> m_attn_layers_0_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_v_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1496960)))];
            tensor<fp16, [1, 64, 129]> var_427_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_v_bias_to_fp16, dilations = var_427_dilations_0, groups = var_427_groups_0, pad = var_427_pad_0, pad_type = var_427_pad_type_0, strides = var_427_strides_0, weight = m_attn_layers_0_attn_conv_v_weight_to_fp16, x = x_43_cast_fp16)[name = string("op_427_cast_fp16")];
            tensor<int32, [4]> var_428 = const()[name = string("op_428"), val = tensor<int32, [4]>([1, 2, 32, 129])];
            tensor<fp16, [1, 2, 32, 129]> var_429_cast_fp16 = reshape(shape = var_428, x = var_427_cast_fp16)[name = string("op_429_cast_fp16")];
            fp16 var_431_to_fp16 = const()[name = string("op_431_to_fp16"), val = fp16(0x1.6ap-3)];
            tensor<fp16, [1, 2, 129, 32]> q_1_cast_fp16 = transpose(perm = q_1_perm_0, x = var_409_cast_fp16)[name = string("transpose_11")];
            tensor<fp16, [1, 2, 129, 32]> var_432_cast_fp16 = mul(x = q_1_cast_fp16, y = var_431_to_fp16)[name = string("op_432_cast_fp16")];
            bool scores_1_transpose_x_0 = const()[name = string("scores_1_transpose_x_0"), val = bool(false)];
            bool scores_1_transpose_y_0 = const()[name = string("scores_1_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 2, 129, 129]> scores_1_cast_fp16 = matmul(transpose_x = scores_1_transpose_x_0, transpose_y = scores_1_transpose_y_0, x = var_432_cast_fp16, y = var_419_cast_fp16)[name = string("scores_1_cast_fp16")];
            bool x_45_transpose_x_0 = const()[name = string("x_45_transpose_x_0"), val = bool(false)];
            bool x_45_transpose_y_0 = const()[name = string("x_45_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 1, 32, 257]> var_456_to_fp16 = const()[name = string("op_456_to_fp16"), val = tensor<fp16, [1, 1, 32, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1497152)))];
            tensor<fp16, [1, 2, 129, 257]> x_45_cast_fp16 = matmul(transpose_x = x_45_transpose_x_0, transpose_y = x_45_transpose_y_0, x = var_432_cast_fp16, y = var_456_to_fp16)[name = string("x_45_cast_fp16")];
            tensor<int32, [8]> x_47_pad_0 = const()[name = string("x_47_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 1])];
            string x_47_mode_0 = const()[name = string("x_47_mode_0"), val = string("constant")];
            fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 2, 129, 258]> x_47_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = x_47_mode_0, pad = x_47_pad_0, x = x_45_cast_fp16)[name = string("x_47_cast_fp16")];
            tensor<int32, [3]> var_468 = const()[name = string("op_468"), val = tensor<int32, [3]>([1, 2, 33282])];
            tensor<fp16, [1, 2, 33282]> input_75_cast_fp16 = reshape(shape = var_468, x = x_47_cast_fp16)[name = string("input_75_cast_fp16")];
            tensor<int32, [6]> x_49_pad_0 = const()[name = string("x_49_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 128])];
            string x_49_mode_0 = const()[name = string("x_49_mode_0"), val = string("constant")];
            fp16 const_20_to_fp16 = const()[name = string("const_20_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 2, 33410]> x_49_cast_fp16 = pad(constant_val = const_20_to_fp16, mode = x_49_mode_0, pad = x_49_pad_0, x = input_75_cast_fp16)[name = string("x_49_cast_fp16")];
            tensor<int32, [4]> var_483 = const()[name = string("op_483"), val = tensor<int32, [4]>([1, 2, 130, 257])];
            tensor<fp16, [1, 2, 130, 257]> x_51_cast_fp16 = reshape(shape = var_483, x = x_49_cast_fp16)[name = string("x_51_cast_fp16")];
            tensor<int32, [4]> var_490_begin_0 = const()[name = string("op_490_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_490_end_0 = const()[name = string("op_490_end_0"), val = tensor<int32, [4]>([1, 2, 129, 257])];
            tensor<bool, [4]> var_490_end_mask_0 = const()[name = string("op_490_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
            tensor<fp16, [1, 2, 129, 257]> var_490_cast_fp16 = slice_by_index(begin = var_490_begin_0, end = var_490_end_0, end_mask = var_490_end_mask_0, x = x_51_cast_fp16)[name = string("op_490_cast_fp16")];
            tensor<int32, [4]> var_491_begin_0 = const()[name = string("op_491_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 128])];
            tensor<int32, [4]> var_491_end_0 = const()[name = string("op_491_end_0"), val = tensor<int32, [4]>([1, 2, 129, 257])];
            tensor<bool, [4]> var_491_end_mask_0 = const()[name = string("op_491_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 129, 129]> var_491_cast_fp16 = slice_by_index(begin = var_491_begin_0, end = var_491_end_0, end_mask = var_491_end_mask_0, x = var_490_cast_fp16)[name = string("op_491_cast_fp16")];
            tensor<fp16, [1, 2, 129, 129]> scores_3_cast_fp16 = add(x = scores_1_cast_fp16, y = var_491_cast_fp16)[name = string("scores_3_cast_fp16")];
            tensor<int32, [1]> var_493_axes_0 = const()[name = string("op_493_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp16, [1, 1, 1, 129]> var_493_cast_fp16 = expand_dims(axes = var_493_axes_0, x = mask_cast_fp16)[name = string("op_493_cast_fp16")];
            tensor<fp16, [1, 1, 1, 129]> var_494_cast_fp16 = sub(x = var_16_to_fp16, y = var_493_cast_fp16)[name = string("op_494_cast_fp16")];
            fp16 var_495_to_fp16 = const()[name = string("op_495_to_fp16"), val = fp16(0x1.388p+13)];
            tensor<fp16, [1, 1, 1, 129]> var_496_cast_fp16 = mul(x = var_494_cast_fp16, y = var_495_to_fp16)[name = string("op_496_cast_fp16")];
            tensor<fp16, [1, 2, 129, 129]> input_77_cast_fp16 = sub(x = scores_3_cast_fp16, y = var_496_cast_fp16)[name = string("input_77_cast_fp16")];
            tensor<fp16, [1, 2, 129, 129]> p_attn_1_cast_fp16 = softmax(axis = var_27, x = input_77_cast_fp16)[name = string("p_attn_1_cast_fp16")];
            bool out_13_transpose_x_1 = const()[name = string("out_13_transpose_x_1"), val = bool(false)];
            bool out_13_transpose_y_1 = const()[name = string("out_13_transpose_y_1"), val = bool(true)];
            tensor<fp16, [1, 2, 129, 32]> out_13_cast_fp16 = matmul(transpose_x = out_13_transpose_x_1, transpose_y = out_13_transpose_y_1, x = p_attn_1_cast_fp16, y = var_429_cast_fp16)[name = string("out_13_cast_fp16")];
            tensor<int32, [8]> x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 128])];
            string x_53_mode_0 = const()[name = string("x_53_mode_0"), val = string("constant")];
            fp16 const_25_to_fp16 = const()[name = string("const_25_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 2, 129, 257]> x_53_cast_fp16 = pad(constant_val = const_25_to_fp16, mode = x_53_mode_0, pad = x_53_pad_0, x = p_attn_1_cast_fp16)[name = string("x_53_cast_fp16")];
            tensor<int32, [3]> var_533 = const()[name = string("op_533"), val = tensor<int32, [3]>([1, 2, 33153])];
            tensor<fp16, [1, 2, 33153]> input_81_cast_fp16 = reshape(shape = var_533, x = x_53_cast_fp16)[name = string("input_81_cast_fp16")];
            tensor<int32, [6]> x_55_pad_0 = const()[name = string("x_55_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 129, 0])];
            string x_55_mode_0 = const()[name = string("x_55_mode_0"), val = string("constant")];
            fp16 const_26_to_fp16 = const()[name = string("const_26_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 2, 33282]> x_55_cast_fp16 = pad(constant_val = const_26_to_fp16, mode = x_55_mode_0, pad = x_55_pad_0, x = input_81_cast_fp16)[name = string("x_55_cast_fp16")];
            tensor<int32, [4]> var_540 = const()[name = string("op_540"), val = tensor<int32, [4]>([1, 2, 129, 258])];
            tensor<fp16, [1, 2, 129, 258]> x_57_cast_fp16 = reshape(shape = var_540, x = x_55_cast_fp16)[name = string("x_57_cast_fp16")];
            tensor<int32, [4]> rel_weights_1_begin_0 = const()[name = string("rel_weights_1_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
            tensor<int32, [4]> rel_weights_1_end_0 = const()[name = string("rel_weights_1_end_0"), val = tensor<int32, [4]>([1, 2, 129, 258])];
            tensor<bool, [4]> rel_weights_1_end_mask_0 = const()[name = string("rel_weights_1_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 129, 257]> rel_weights_1_cast_fp16 = slice_by_index(begin = rel_weights_1_begin_0, end = rel_weights_1_end_0, end_mask = rel_weights_1_end_mask_0, x = x_57_cast_fp16)[name = string("rel_weights_1_cast_fp16")];
            bool var_547_transpose_x_0 = const()[name = string("op_547_transpose_x_0"), val = bool(false)];
            bool var_547_transpose_y_0 = const()[name = string("op_547_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 1, 257, 32]> var_546_to_fp16 = const()[name = string("op_546_to_fp16"), val = tensor<fp16, [1, 1, 257, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1513664)))];
            tensor<fp16, [1, 2, 129, 32]> var_547_cast_fp16 = matmul(transpose_x = var_547_transpose_x_0, transpose_y = var_547_transpose_y_0, x = rel_weights_1_cast_fp16, y = var_546_to_fp16)[name = string("op_547_cast_fp16")];
            tensor<fp16, [1, 2, 129, 32]> out_15_cast_fp16 = add(x = out_13_cast_fp16, y = var_547_cast_fp16)[name = string("out_15_cast_fp16")];
            tensor<int32, [4]> var_549_perm_0 = const()[name = string("op_549_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> var_550 = const()[name = string("op_550"), val = tensor<int32, [3]>([1, 64, 129])];
            tensor<fp16, [1, 2, 32, 129]> var_549_cast_fp16 = transpose(perm = var_549_perm_0, x = out_15_cast_fp16)[name = string("transpose_10")];
            tensor<fp16, [1, 64, 129]> input_83_cast_fp16 = reshape(shape = var_550, x = var_549_cast_fp16)[name = string("input_83_cast_fp16")];
            string y_1_pad_type_0 = const()[name = string("y_1_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> y_1_strides_0 = const()[name = string("y_1_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> y_1_pad_0 = const()[name = string("y_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> y_1_dilations_0 = const()[name = string("y_1_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 y_1_groups_0 = const()[name = string("y_1_groups_0"), val = int32(1)];
            tensor<fp16, [64, 64, 1]> m_attn_layers_0_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_o_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530176)))];
            tensor<fp16, [64]> m_attn_layers_0_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_o_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1538432)))];
            tensor<fp16, [1, 64, 129]> y_1_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_o_bias_to_fp16, dilations = y_1_dilations_0, groups = y_1_groups_0, pad = y_1_pad_0, pad_type = y_1_pad_type_0, strides = y_1_strides_0, weight = m_attn_layers_0_attn_conv_o_weight_to_fp16, x = input_83_cast_fp16)[name = string("y_1_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_59_cast_fp16 = add(x = x_43_cast_fp16, y = y_1_cast_fp16)[name = string("x_59_cast_fp16")];
            tensor<int32, [3]> input_85_perm_0 = const()[name = string("input_85_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<int32, [1]> var_565_axes_0 = const()[name = string("op_565_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [64]> m_attn_layers_0_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_0_norm_1_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1538624)))];
            tensor<fp16, [64]> m_attn_layers_0_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_0_norm_1_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1538816)))];
            tensor<fp16, [1, 129, 64]> input_85_cast_fp16 = transpose(perm = input_85_perm_0, x = x_59_cast_fp16)[name = string("transpose_9")];
            tensor<fp16, [1, 129, 64]> var_565_cast_fp16 = layer_norm(axes = var_565_axes_0, beta = m_attn_layers_0_norm_1_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_0_norm_1_norm_weight_to_fp16, x = input_85_cast_fp16)[name = string("op_565_cast_fp16")];
            tensor<int32, [3]> x_61_perm_0 = const()[name = string("x_61_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 64, 129]> x_61_cast_fp16 = transpose(perm = x_61_perm_0, x = var_565_cast_fp16)[name = string("transpose_8")];
            tensor<fp16, [1, 64, 129]> input_87_cast_fp16 = mul(x = x_61_cast_fp16, y = mask_cast_fp16)[name = string("input_87_cast_fp16")];
            string input_89_pad_type_0 = const()[name = string("input_89_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_89_strides_0 = const()[name = string("input_89_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_89_pad_0 = const()[name = string("input_89_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_89_dilations_0 = const()[name = string("input_89_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_89_groups_0 = const()[name = string("input_89_groups_0"), val = int32(1)];
            tensor<fp16, [256, 64, 1]> m_attn_layers_0_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1539008)))];
            tensor<fp16, [256]> m_attn_layers_0_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1571840)))];
            tensor<fp16, [1, 256, 129]> input_89_cast_fp16 = conv(bias = m_attn_layers_0_ffn_conv_1_bias_to_fp16, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = m_attn_layers_0_ffn_conv_1_weight_to_fp16, x = input_87_cast_fp16)[name = string("input_89_cast_fp16")];
            tensor<fp16, [1, 256, 129]> h_37_cast_fp16 = relu(x = input_89_cast_fp16)[name = string("h_37_cast_fp16")];
            tensor<fp16, [1, 256, 129]> input_91_cast_fp16 = mul(x = h_37_cast_fp16, y = mask_cast_fp16)[name = string("input_91_cast_fp16")];
            string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(1)];
            tensor<fp16, [64, 256, 1]> m_attn_layers_0_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_2_weight_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1572416)))];
            tensor<fp16, [64]> m_attn_layers_0_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_2_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605248)))];
            tensor<fp16, [1, 64, 129]> h_39_cast_fp16 = conv(bias = m_attn_layers_0_ffn_conv_2_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = m_attn_layers_0_ffn_conv_2_weight_to_fp16, x = input_91_cast_fp16)[name = string("h_39_cast_fp16")];
            tensor<fp16, [1, 64, 129]> y_3_cast_fp16 = mul(x = h_39_cast_fp16, y = mask_cast_fp16)[name = string("y_3_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_63_cast_fp16 = add(x = x_61_cast_fp16, y = y_3_cast_fp16)[name = string("x_63_cast_fp16")];
            tensor<int32, [3]> input_93_perm_0 = const()[name = string("input_93_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<int32, [1]> var_593_axes_0 = const()[name = string("op_593_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [64]> m_attn_layers_0_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_0_norm_2_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605440)))];
            tensor<fp16, [64]> m_attn_layers_0_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_0_norm_2_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605632)))];
            tensor<fp16, [1, 129, 64]> input_93_cast_fp16 = transpose(perm = input_93_perm_0, x = x_63_cast_fp16)[name = string("transpose_7")];
            tensor<fp16, [1, 129, 64]> var_593_cast_fp16 = layer_norm(axes = var_593_axes_0, beta = m_attn_layers_0_norm_2_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_0_norm_2_norm_weight_to_fp16, x = input_93_cast_fp16)[name = string("op_593_cast_fp16")];
            tensor<int32, [3]> x_65_perm_0 = const()[name = string("x_65_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 64, 129]> x_65_cast_fp16 = transpose(perm = x_65_perm_0, x = var_593_cast_fp16)[name = string("transpose_6")];
            tensor<fp16, [1, 64, 129]> x_67_cast_fp16 = mul(x = x_65_cast_fp16, y = mask_cast_fp16)[name = string("x_67_cast_fp16")];
            string var_615_pad_type_0 = const()[name = string("op_615_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_615_strides_0 = const()[name = string("op_615_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_615_pad_0 = const()[name = string("op_615_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_615_dilations_0 = const()[name = string("op_615_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_615_groups_0 = const()[name = string("op_615_groups_0"), val = int32(1)];
            tensor<fp16, [64, 64, 1]> m_attn_layers_1_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_q_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605824)))];
            tensor<fp16, [64]> m_attn_layers_1_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_q_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1614080)))];
            tensor<fp16, [1, 64, 129]> var_615_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_q_bias_to_fp16, dilations = var_615_dilations_0, groups = var_615_groups_0, pad = var_615_pad_0, pad_type = var_615_pad_type_0, strides = var_615_strides_0, weight = m_attn_layers_1_attn_conv_q_weight_to_fp16, x = x_67_cast_fp16)[name = string("op_615_cast_fp16")];
            tensor<int32, [4]> var_616 = const()[name = string("op_616"), val = tensor<int32, [4]>([1, 2, 32, 129])];
            tensor<fp16, [1, 2, 32, 129]> var_617_cast_fp16 = reshape(shape = var_616, x = var_615_cast_fp16)[name = string("op_617_cast_fp16")];
            tensor<int32, [4]> q_perm_0 = const()[name = string("q_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            string var_625_pad_type_0 = const()[name = string("op_625_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_625_strides_0 = const()[name = string("op_625_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_625_pad_0 = const()[name = string("op_625_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_625_dilations_0 = const()[name = string("op_625_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_625_groups_0 = const()[name = string("op_625_groups_0"), val = int32(1)];
            tensor<fp16, [64, 64, 1]> m_attn_layers_1_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_k_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1614272)))];
            tensor<fp16, [64]> m_attn_layers_1_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_k_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1622528)))];
            tensor<fp16, [1, 64, 129]> var_625_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_k_bias_to_fp16, dilations = var_625_dilations_0, groups = var_625_groups_0, pad = var_625_pad_0, pad_type = var_625_pad_type_0, strides = var_625_strides_0, weight = m_attn_layers_1_attn_conv_k_weight_to_fp16, x = x_67_cast_fp16)[name = string("op_625_cast_fp16")];
            tensor<int32, [4]> var_626 = const()[name = string("op_626"), val = tensor<int32, [4]>([1, 2, 32, 129])];
            tensor<fp16, [1, 2, 32, 129]> var_627_cast_fp16 = reshape(shape = var_626, x = var_625_cast_fp16)[name = string("op_627_cast_fp16")];
            string var_635_pad_type_0 = const()[name = string("op_635_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_635_strides_0 = const()[name = string("op_635_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_635_pad_0 = const()[name = string("op_635_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_635_dilations_0 = const()[name = string("op_635_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_635_groups_0 = const()[name = string("op_635_groups_0"), val = int32(1)];
            tensor<fp16, [64, 64, 1]> m_attn_layers_1_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_v_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1622720)))];
            tensor<fp16, [64]> m_attn_layers_1_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_v_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1630976)))];
            tensor<fp16, [1, 64, 129]> var_635_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_v_bias_to_fp16, dilations = var_635_dilations_0, groups = var_635_groups_0, pad = var_635_pad_0, pad_type = var_635_pad_type_0, strides = var_635_strides_0, weight = m_attn_layers_1_attn_conv_v_weight_to_fp16, x = x_67_cast_fp16)[name = string("op_635_cast_fp16")];
            tensor<int32, [4]> var_636 = const()[name = string("op_636"), val = tensor<int32, [4]>([1, 2, 32, 129])];
            tensor<fp16, [1, 2, 32, 129]> var_637_cast_fp16 = reshape(shape = var_636, x = var_635_cast_fp16)[name = string("op_637_cast_fp16")];
            fp16 var_639_to_fp16 = const()[name = string("op_639_to_fp16"), val = fp16(0x1.6ap-3)];
            tensor<fp16, [1, 2, 129, 32]> q_cast_fp16 = transpose(perm = q_perm_0, x = var_617_cast_fp16)[name = string("transpose_5")];
            tensor<fp16, [1, 2, 129, 32]> var_640_cast_fp16 = mul(x = q_cast_fp16, y = var_639_to_fp16)[name = string("op_640_cast_fp16")];
            bool scores_5_transpose_x_0 = const()[name = string("scores_5_transpose_x_0"), val = bool(false)];
            bool scores_5_transpose_y_0 = const()[name = string("scores_5_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 2, 129, 129]> scores_5_cast_fp16 = matmul(transpose_x = scores_5_transpose_x_0, transpose_y = scores_5_transpose_y_0, x = var_640_cast_fp16, y = var_627_cast_fp16)[name = string("scores_5_cast_fp16")];
            bool x_69_transpose_x_0 = const()[name = string("x_69_transpose_x_0"), val = bool(false)];
            bool x_69_transpose_y_0 = const()[name = string("x_69_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 1, 32, 257]> var_664_to_fp16 = const()[name = string("op_664_to_fp16"), val = tensor<fp16, [1, 1, 32, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1631168)))];
            tensor<fp16, [1, 2, 129, 257]> x_69_cast_fp16 = matmul(transpose_x = x_69_transpose_x_0, transpose_y = x_69_transpose_y_0, x = var_640_cast_fp16, y = var_664_to_fp16)[name = string("x_69_cast_fp16")];
            tensor<int32, [8]> x_71_pad_0 = const()[name = string("x_71_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 1])];
            string x_71_mode_0 = const()[name = string("x_71_mode_0"), val = string("constant")];
            fp16 const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 2, 129, 258]> x_71_cast_fp16 = pad(constant_val = const_33_to_fp16, mode = x_71_mode_0, pad = x_71_pad_0, x = x_69_cast_fp16)[name = string("x_71_cast_fp16")];
            tensor<int32, [3]> var_676 = const()[name = string("op_676"), val = tensor<int32, [3]>([1, 2, 33282])];
            tensor<fp16, [1, 2, 33282]> input_97_cast_fp16 = reshape(shape = var_676, x = x_71_cast_fp16)[name = string("input_97_cast_fp16")];
            tensor<int32, [6]> x_73_pad_0 = const()[name = string("x_73_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 128])];
            string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("constant")];
            fp16 const_34_to_fp16 = const()[name = string("const_34_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 2, 33410]> x_73_cast_fp16 = pad(constant_val = const_34_to_fp16, mode = x_73_mode_0, pad = x_73_pad_0, x = input_97_cast_fp16)[name = string("x_73_cast_fp16")];
            tensor<int32, [4]> var_691 = const()[name = string("op_691"), val = tensor<int32, [4]>([1, 2, 130, 257])];
            tensor<fp16, [1, 2, 130, 257]> x_75_cast_fp16 = reshape(shape = var_691, x = x_73_cast_fp16)[name = string("x_75_cast_fp16")];
            tensor<int32, [4]> var_698_begin_0 = const()[name = string("op_698_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_698_end_0 = const()[name = string("op_698_end_0"), val = tensor<int32, [4]>([1, 2, 129, 257])];
            tensor<bool, [4]> var_698_end_mask_0 = const()[name = string("op_698_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
            tensor<fp16, [1, 2, 129, 257]> var_698_cast_fp16 = slice_by_index(begin = var_698_begin_0, end = var_698_end_0, end_mask = var_698_end_mask_0, x = x_75_cast_fp16)[name = string("op_698_cast_fp16")];
            tensor<int32, [4]> var_699_begin_0 = const()[name = string("op_699_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 128])];
            tensor<int32, [4]> var_699_end_0 = const()[name = string("op_699_end_0"), val = tensor<int32, [4]>([1, 2, 129, 257])];
            tensor<bool, [4]> var_699_end_mask_0 = const()[name = string("op_699_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 129, 129]> var_699_cast_fp16 = slice_by_index(begin = var_699_begin_0, end = var_699_end_0, end_mask = var_699_end_mask_0, x = var_698_cast_fp16)[name = string("op_699_cast_fp16")];
            tensor<fp16, [1, 2, 129, 129]> scores_cast_fp16 = add(x = scores_5_cast_fp16, y = var_699_cast_fp16)[name = string("scores_cast_fp16")];
            tensor<fp16, [1, 2, 129, 129]> input_99_cast_fp16 = sub(x = scores_cast_fp16, y = var_496_cast_fp16)[name = string("input_99_cast_fp16")];
            tensor<fp16, [1, 2, 129, 129]> p_attn_cast_fp16 = softmax(axis = var_27, x = input_99_cast_fp16)[name = string("p_attn_cast_fp16")];
            bool out_17_transpose_x_1 = const()[name = string("out_17_transpose_x_1"), val = bool(false)];
            bool out_17_transpose_y_1 = const()[name = string("out_17_transpose_y_1"), val = bool(true)];
            tensor<fp16, [1, 2, 129, 32]> out_17_cast_fp16 = matmul(transpose_x = out_17_transpose_x_1, transpose_y = out_17_transpose_y_1, x = p_attn_cast_fp16, y = var_637_cast_fp16)[name = string("out_17_cast_fp16")];
            tensor<int32, [8]> x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 128])];
            string x_77_mode_0 = const()[name = string("x_77_mode_0"), val = string("constant")];
            fp16 const_39_to_fp16 = const()[name = string("const_39_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 2, 129, 257]> x_77_cast_fp16 = pad(constant_val = const_39_to_fp16, mode = x_77_mode_0, pad = x_77_pad_0, x = p_attn_cast_fp16)[name = string("x_77_cast_fp16")];
            tensor<int32, [3]> var_741 = const()[name = string("op_741"), val = tensor<int32, [3]>([1, 2, 33153])];
            tensor<fp16, [1, 2, 33153]> input_103_cast_fp16 = reshape(shape = var_741, x = x_77_cast_fp16)[name = string("input_103_cast_fp16")];
            tensor<int32, [6]> x_79_pad_0 = const()[name = string("x_79_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 129, 0])];
            string x_79_mode_0 = const()[name = string("x_79_mode_0"), val = string("constant")];
            fp16 const_40_to_fp16 = const()[name = string("const_40_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 2, 33282]> x_79_cast_fp16 = pad(constant_val = const_40_to_fp16, mode = x_79_mode_0, pad = x_79_pad_0, x = input_103_cast_fp16)[name = string("x_79_cast_fp16")];
            tensor<int32, [4]> var_748 = const()[name = string("op_748"), val = tensor<int32, [4]>([1, 2, 129, 258])];
            tensor<fp16, [1, 2, 129, 258]> x_81_cast_fp16 = reshape(shape = var_748, x = x_79_cast_fp16)[name = string("x_81_cast_fp16")];
            tensor<int32, [4]> rel_weights_begin_0 = const()[name = string("rel_weights_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
            tensor<int32, [4]> rel_weights_end_0 = const()[name = string("rel_weights_end_0"), val = tensor<int32, [4]>([1, 2, 129, 258])];
            tensor<bool, [4]> rel_weights_end_mask_0 = const()[name = string("rel_weights_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 129, 257]> rel_weights_cast_fp16 = slice_by_index(begin = rel_weights_begin_0, end = rel_weights_end_0, end_mask = rel_weights_end_mask_0, x = x_81_cast_fp16)[name = string("rel_weights_cast_fp16")];
            bool var_755_transpose_x_0 = const()[name = string("op_755_transpose_x_0"), val = bool(false)];
            bool var_755_transpose_y_0 = const()[name = string("op_755_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 1, 257, 32]> var_754_to_fp16 = const()[name = string("op_754_to_fp16"), val = tensor<fp16, [1, 1, 257, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1647680)))];
            tensor<fp16, [1, 2, 129, 32]> var_755_cast_fp16 = matmul(transpose_x = var_755_transpose_x_0, transpose_y = var_755_transpose_y_0, x = rel_weights_cast_fp16, y = var_754_to_fp16)[name = string("op_755_cast_fp16")];
            tensor<fp16, [1, 2, 129, 32]> out_cast_fp16 = add(x = out_17_cast_fp16, y = var_755_cast_fp16)[name = string("out_cast_fp16")];
            tensor<int32, [4]> var_757_perm_0 = const()[name = string("op_757_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> var_758 = const()[name = string("op_758"), val = tensor<int32, [3]>([1, 64, 129])];
            tensor<fp16, [1, 2, 32, 129]> var_757_cast_fp16 = transpose(perm = var_757_perm_0, x = out_cast_fp16)[name = string("transpose_4")];
            tensor<fp16, [1, 64, 129]> input_105_cast_fp16 = reshape(shape = var_758, x = var_757_cast_fp16)[name = string("input_105_cast_fp16")];
            string y_5_pad_type_0 = const()[name = string("y_5_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> y_5_strides_0 = const()[name = string("y_5_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> y_5_pad_0 = const()[name = string("y_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> y_5_dilations_0 = const()[name = string("y_5_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 y_5_groups_0 = const()[name = string("y_5_groups_0"), val = int32(1)];
            tensor<fp16, [64, 64, 1]> m_attn_layers_1_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_o_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1664192)))];
            tensor<fp16, [64]> m_attn_layers_1_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_o_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672448)))];
            tensor<fp16, [1, 64, 129]> y_5_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_o_bias_to_fp16, dilations = y_5_dilations_0, groups = y_5_groups_0, pad = y_5_pad_0, pad_type = y_5_pad_type_0, strides = y_5_strides_0, weight = m_attn_layers_1_attn_conv_o_weight_to_fp16, x = input_105_cast_fp16)[name = string("y_5_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_83_cast_fp16 = add(x = x_67_cast_fp16, y = y_5_cast_fp16)[name = string("x_83_cast_fp16")];
            tensor<int32, [3]> input_107_perm_0 = const()[name = string("input_107_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<int32, [1]> var_773_axes_0 = const()[name = string("op_773_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [64]> m_attn_layers_1_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_1_norm_1_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672640)))];
            tensor<fp16, [64]> m_attn_layers_1_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_1_norm_1_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832)))];
            tensor<fp16, [1, 129, 64]> input_107_cast_fp16 = transpose(perm = input_107_perm_0, x = x_83_cast_fp16)[name = string("transpose_3")];
            tensor<fp16, [1, 129, 64]> var_773_cast_fp16 = layer_norm(axes = var_773_axes_0, beta = m_attn_layers_1_norm_1_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_1_norm_1_norm_weight_to_fp16, x = input_107_cast_fp16)[name = string("op_773_cast_fp16")];
            tensor<int32, [3]> x_85_perm_0 = const()[name = string("x_85_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 64, 129]> x_85_cast_fp16 = transpose(perm = x_85_perm_0, x = var_773_cast_fp16)[name = string("transpose_2")];
            tensor<fp16, [1, 64, 129]> input_109_cast_fp16 = mul(x = x_85_cast_fp16, y = mask_cast_fp16)[name = string("input_109_cast_fp16")];
            string input_111_pad_type_0 = const()[name = string("input_111_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_111_strides_0 = const()[name = string("input_111_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_111_pad_0 = const()[name = string("input_111_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_111_dilations_0 = const()[name = string("input_111_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_111_groups_0 = const()[name = string("input_111_groups_0"), val = int32(1)];
            tensor<fp16, [256, 64, 1]> m_attn_layers_1_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673024)))];
            tensor<fp16, [256]> m_attn_layers_1_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1705856)))];
            tensor<fp16, [1, 256, 129]> input_111_cast_fp16 = conv(bias = m_attn_layers_1_ffn_conv_1_bias_to_fp16, dilations = input_111_dilations_0, groups = input_111_groups_0, pad = input_111_pad_0, pad_type = input_111_pad_type_0, strides = input_111_strides_0, weight = m_attn_layers_1_ffn_conv_1_weight_to_fp16, x = input_109_cast_fp16)[name = string("input_111_cast_fp16")];
            tensor<fp16, [1, 256, 129]> h_41_cast_fp16 = relu(x = input_111_cast_fp16)[name = string("h_41_cast_fp16")];
            tensor<fp16, [1, 256, 129]> input_113_cast_fp16 = mul(x = h_41_cast_fp16, y = mask_cast_fp16)[name = string("input_113_cast_fp16")];
            string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> h_strides_0 = const()[name = string("h_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> h_pad_0 = const()[name = string("h_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)];
            tensor<fp16, [64, 256, 1]> m_attn_layers_1_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_2_weight_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1706432)))];
            tensor<fp16, [64]> m_attn_layers_1_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_2_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739264)))];
            tensor<fp16, [1, 64, 129]> h_cast_fp16 = conv(bias = m_attn_layers_1_ffn_conv_2_bias_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = m_attn_layers_1_ffn_conv_2_weight_to_fp16, x = input_113_cast_fp16)[name = string("h_cast_fp16")];
            tensor<fp16, [1, 64, 129]> y_cast_fp16 = mul(x = h_cast_fp16, y = mask_cast_fp16)[name = string("y_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_87_cast_fp16 = add(x = x_85_cast_fp16, y = y_cast_fp16)[name = string("x_87_cast_fp16")];
            tensor<int32, [3]> input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<int32, [1]> var_801_axes_0 = const()[name = string("op_801_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [64]> m_attn_layers_1_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_1_norm_2_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739456)))];
            tensor<fp16, [64]> m_attn_layers_1_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_1_norm_2_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739648)))];
            tensor<fp16, [1, 129, 64]> input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_87_cast_fp16)[name = string("transpose_1")];
            tensor<fp16, [1, 129, 64]> var_801_cast_fp16 = layer_norm(axes = var_801_axes_0, beta = m_attn_layers_1_norm_2_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_1_norm_2_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("op_801_cast_fp16")];
            tensor<int32, [3]> x_89_perm_0 = const()[name = string("x_89_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp16, [1, 64, 129]> x_89_cast_fp16 = transpose(perm = x_89_perm_0, x = var_801_cast_fp16)[name = string("transpose_0")];
            tensor<fp16, [1, 64, 129]> x_91_cast_fp16 = mul(x = x_89_cast_fp16, y = mask_cast_fp16)[name = string("x_91_cast_fp16")];
            tensor<fp16, [1, 64, 129]> x_93_cast_fp16 = add(x = x_91_cast_fp16, y = x_41_cast_fp16)[name = string("x_93_cast_fp16")];
            tensor<int32, [3]> input_117_begin_0 = const()[name = string("input_117_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> input_117_end_0 = const()[name = string("input_117_end_0"), val = tensor<int32, [3]>([1, 64, 1])];
            tensor<bool, [3]> input_117_end_mask_0 = const()[name = string("input_117_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp16, [1, 64, 1]> input_117_cast_fp16 = slice_by_index(begin = input_117_begin_0, end = input_117_end_0, end_mask = input_117_end_mask_0, x = x_93_cast_fp16)[name = string("input_117_cast_fp16")];
            tensor<int32, [3]> sentence_mask_begin_0 = const()[name = string("sentence_mask_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> sentence_mask_end_0 = const()[name = string("sentence_mask_end_0"), val = tensor<int32, [3]>([1, 1, 1])];
            tensor<bool, [3]> sentence_mask_end_mask_0 = const()[name = string("sentence_mask_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp16, [1, 1, 1]> sentence_mask_cast_fp16 = slice_by_index(begin = sentence_mask_begin_0, end = sentence_mask_end_0, end_mask = sentence_mask_end_mask_0, x = mask_cast_fp16)[name = string("sentence_mask_cast_fp16")];
            string var_816_pad_type_0 = const()[name = string("op_816_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_816_strides_0 = const()[name = string("op_816_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_816_pad_0 = const()[name = string("op_816_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_816_dilations_0 = const()[name = string("op_816_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_816_groups_0 = const()[name = string("op_816_groups_0"), val = int32(1)];
            tensor<fp16, [64, 64, 1]> m_proj_out_conv_weight_to_fp16 = const()[name = string("m_proj_out_conv_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739840)))];
            tensor<fp16, [1, 64, 1]> var_816_cast_fp16 = conv(dilations = var_816_dilations_0, groups = var_816_groups_0, pad = var_816_pad_0, pad_type = var_816_pad_type_0, strides = var_816_strides_0, weight = m_proj_out_conv_weight_to_fp16, x = input_117_cast_fp16)[name = string("op_816_cast_fp16")];
            tensor<fp16, [1, 64, 1]> sentence_emb_cast_fp16 = mul(x = var_816_cast_fp16, y = sentence_mask_cast_fp16)[name = string("sentence_emb_cast_fp16")];
            tensor<int32, [2]> var_824 = const()[name = string("op_824"), val = tensor<int32, [2]>([1, -1])];
            tensor<fp16, [1, 64]> s_cast_fp16 = reshape(shape = var_824, x = sentence_emb_cast_fp16)[name = string("s_cast_fp16")];
            tensor<int32, [2]> var_826 = const()[name = string("op_826"), val = tensor<int32, [2]>([1, -1])];
            string style_dp_to_fp16_dtype_0 = const()[name = string("style_dp_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [1, 8, 16]> style_dp_to_fp16 = cast(dtype = style_dp_to_fp16_dtype_0, x = style_dp)[name = string("cast_21")];
            tensor<fp16, [1, 128]> v_cast_fp16 = reshape(shape = var_826, x = style_dp_to_fp16)[name = string("v_cast_fp16")];
            bool input_119_interleave_0 = const()[name = string("input_119_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 192]> input_119_cast_fp16 = concat(axis = var_26, interleave = input_119_interleave_0, values = (s_cast_fp16, v_cast_fp16))[name = string("input_119_cast_fp16")];
            tensor<fp16, [128, 192]> m_predictor_layers_0_weight_to_fp16 = const()[name = string("m_predictor_layers_0_weight_to_fp16"), val = tensor<fp16, [128, 192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1748096)))];
            tensor<fp16, [128]> m_predictor_layers_0_bias_to_fp16 = const()[name = string("m_predictor_layers_0_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797312)))];
            tensor<fp16, [1, 128]> linear_0_cast_fp16 = linear(bias = m_predictor_layers_0_bias_to_fp16, weight = m_predictor_layers_0_weight_to_fp16, x = input_119_cast_fp16)[name = string("linear_0_cast_fp16")];
            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp16, [1, 128, 1]> expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = linear_0_cast_fp16)[name = string("expand_dims_0_cast_fp16")];
            fp32 prelu_0_alpha_1 = const()[name = string("prelu_0_alpha_1"), val = fp32(0x1.b19f5ap-3)];
            tensor<fp16, [1, 128, 1]> prelu_0_cast_fp16 = leaky_relu(alpha = prelu_0_alpha_1, x = expand_dims_0_cast_fp16)[name = string("prelu_0_cast_fp16")];
            tensor<int32, [1]> input_axes_0 = const()[name = string("input_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp16, [1, 128]> input_cast_fp16 = squeeze(axes = input_axes_0, x = prelu_0_cast_fp16)[name = string("input_cast_fp16")];
            tensor<fp16, [1, 128]> m_predictor_layers_1_weight_to_fp16 = const()[name = string("m_predictor_layers_1_weight_to_fp16"), val = tensor<fp16, [1, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797632)))];
            tensor<fp16, [1]> m_predictor_layers_1_bias_to_fp16 = const()[name = string("m_predictor_layers_1_bias_to_fp16"), val = tensor<fp16, [1]>([0x1.60cp-6])];
            tensor<fp16, [1, 1]> linear_1_cast_fp16 = linear(bias = m_predictor_layers_1_bias_to_fp16, weight = m_predictor_layers_1_weight_to_fp16, x = input_cast_fp16)[name = string("linear_1_cast_fp16")];
            tensor<fp16, [1, 1]> x_cast_fp16 = exp(x = linear_1_cast_fp16)[name = string("x_cast_fp16")];
            tensor<int32, [1]> var_839_axes_0 = const()[name = string("op_839_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1]> var_839_cast_fp16 = squeeze(axes = var_839_axes_0, x = x_cast_fp16)[name = string("op_839_cast_fp16")];
            string var_839_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_839_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
            tensor<fp32, [1]> duration = cast(dtype = var_839_cast_fp16_to_fp32_dtype_0, x = var_839_cast_fp16)[name = string("cast_20")];
        } -> (duration);
}