program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.1"}, {"coremltools-component-milinternal", ""}, {"coremltools-version", "9.0"}})]
{
    func main<ios18>(tensor<fp32, [1, 144, ?]> latent) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"latent", [1, 144, 64]}}), ("RangeDims", {{"latent", [[1, 1], [144, 144], [1, 1024]]}})))] {
            fp32 _inversed_real_div_0_y_0 = const()[name = string("_inversed_real_div_0_y_0"), val = fp32(0x1p+2)];
            tensor<fp32, [1, 144, ?]> _inversed_real_div_0 = mul(x = latent, y = _inversed_real_div_0_y_0)[name = string("_inversed_real_div_0")];
            tensor<int32, [3]> shape_0 = shape(x = _inversed_real_div_0)[name = string("shape_0")];
            tensor<int32, [1]> slice_by_index_0_begin_0 = const()[name = string("slice_by_index_0_begin_0"), val = tensor<int32, [1]>([-1])];
            tensor<int32, [1]> slice_by_index_0_end_0 = const()[name = string("slice_by_index_0_end_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> slice_by_index_0_stride_0 = const()[name = string("slice_by_index_0_stride_0"), val = tensor<int32, [1]>([1])];
            tensor<bool, [1]> slice_by_index_0_begin_mask_0 = const()[name = string("slice_by_index_0_begin_mask_0"), val = tensor<bool, [1]>([false])];
            tensor<bool, [1]> slice_by_index_0_end_mask_0 = const()[name = string("slice_by_index_0_end_mask_0"), val = tensor<bool, [1]>([true])];
            tensor<int32, [1]> slice_by_index_0 = slice_by_index(begin = slice_by_index_0_begin_0, begin_mask = slice_by_index_0_begin_mask_0, end = slice_by_index_0_end_0, end_mask = slice_by_index_0_end_mask_0, stride = slice_by_index_0_stride_0, x = shape_0)[name = string("slice_by_index_0")];
            tensor<int32, [1]> expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> const_2 = const()[name = string("const_2"), val = tensor<int32, [1]>([-1])];
            tensor<int32, [1]> const_3 = const()[name = string("const_3"), val = tensor<int32, [1]>([6])];
            int32 concat_0_axis_0 = const()[name = string("concat_0_axis_0"), val = int32(0)];
            bool concat_0_interleave_0 = const()[name = string("concat_0_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (expand_dims_0, const_2, const_3, slice_by_index_0))[name = string("concat_0")];
            tensor<fp32, [1, 24, 6, ?]> reshape_0 = reshape(shape = concat_0, x = _inversed_real_div_0)[name = string("reshape_0")];
            tensor<int32, [4]> transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [3]> concat_1 = const()[name = string("concat_1"), val = tensor<int32, [3]>([1, 24, -1])];
            tensor<fp32, [1, 24, ?, 6]> transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_43")];
            tensor<fp32, [1, 24, ?]> reshape_1 = reshape(shape = concat_1, x = transpose_0)[name = string("reshape_1")];
            tensor<fp32, [1, 24, 1]> const_7 = const()[name = string("const_7"), val = tensor<fp32, [1, 24, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
            tensor<fp32, [1, 24, ?]> mul_0 = mul(x = reshape_1, y = const_7)[name = string("mul_0")];
            tensor<fp32, [1, 24, 1]> const_8 = const()[name = string("const_8"), val = tensor<fp32, [1, 24, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256)))];
            tensor<fp32, [1, 24, ?]> add_0 = add(x = mul_0, y = const_8)[name = string("add_0")];
            tensor<int32, [3]> slice_by_index_1_begin_0 = const()[name = string("slice_by_index_1_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> slice_by_index_1_end_0 = const()[name = string("slice_by_index_1_end_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<bool, [3]> slice_by_index_1_begin_mask_0 = const()[name = string("slice_by_index_1_begin_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<bool, [3]> slice_by_index_1_end_mask_0 = const()[name = string("slice_by_index_1_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 24, ?]> slice_by_index_1 = slice_by_index(begin = slice_by_index_1_begin_0, begin_mask = slice_by_index_1_begin_mask_0, end = slice_by_index_1_end_0, end_mask = slice_by_index_1_end_mask_0, x = add_0)[name = string("slice_by_index_1")];
            tensor<int32, [3]> tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor<int32, [3]>([1, 1, 6])];
            tensor<fp32, [1, 24, ?]> tile_0 = tile(reps = tile_0_reps_0, x = slice_by_index_1)[name = string("tile_0")];
            int32 concat_2_axis_0 = const()[name = string("concat_2_axis_0"), val = int32(2)];
            bool concat_2_interleave_0 = const()[name = string("concat_2_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 24, ?]> concat_2 = concat(axis = concat_2_axis_0, interleave = concat_2_interleave_0, values = (tile_0, add_0))[name = string("concat_2")];
            tensor<fp32, [512, 24, 7]> const_9 = const()[name = string("const_9"), val = tensor<fp32, [512, 24, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(448)))];
            tensor<fp32, [512]> const_10 = const()[name = string("const_10"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(344576)))];
            tensor<int32, [1]> conv_0_strides_0 = const()[name = string("conv_0_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_0_pad_type_0 = const()[name = string("conv_0_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_0_pad_0 = const()[name = string("conv_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_0_dilations_0 = const()[name = string("conv_0_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_0_groups_0 = const()[name = string("conv_0_groups_0"), val = int32(1)];
            tensor<fp32, [1, 512, ?]> conv_0 = conv(bias = const_10, dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = conv_0_strides_0, weight = const_9, x = concat_2)[name = string("conv_0")];
            tensor<int32, [3]> slice_by_index_2_begin_0 = const()[name = string("slice_by_index_2_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> slice_by_index_2_end_0 = const()[name = string("slice_by_index_2_end_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<bool, [3]> slice_by_index_2_begin_mask_0 = const()[name = string("slice_by_index_2_begin_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<bool, [3]> slice_by_index_2_end_mask_0 = const()[name = string("slice_by_index_2_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 512, ?]> slice_by_index_2 = slice_by_index(begin = slice_by_index_2_begin_0, begin_mask = slice_by_index_2_begin_mask_0, end = slice_by_index_2_end_0, end_mask = slice_by_index_2_end_mask_0, x = conv_0)[name = string("slice_by_index_2")];
            tensor<int32, [3]> tile_1_reps_0 = const()[name = string("tile_1_reps_0"), val = tensor<int32, [3]>([1, 1, 6])];
            tensor<fp32, [1, 512, ?]> tile_1 = tile(reps = tile_1_reps_0, x = slice_by_index_2)[name = string("tile_1")];
            int32 concat_3_axis_0 = const()[name = string("concat_3_axis_0"), val = int32(2)];
            bool concat_3_interleave_0 = const()[name = string("concat_3_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 512, ?]> concat_3 = concat(axis = concat_3_axis_0, interleave = concat_3_interleave_0, values = (tile_1, conv_0))[name = string("concat_3")];
            tensor<fp32, [512, 1, 7]> const_11 = const()[name = string("const_11"), val = tensor<fp32, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346688)))];
            tensor<fp32, [512]> const_12 = const()[name = string("const_12"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(361088)))];
            tensor<int32, [1]> conv_1_strides_0 = const()[name = string("conv_1_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_1_pad_type_0 = const()[name = string("conv_1_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_1_pad_0 = const()[name = string("conv_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_1_dilations_0 = const()[name = string("conv_1_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_1_groups_0 = const()[name = string("conv_1_groups_0"), val = int32(512)];
            tensor<fp32, [1, 512, ?]> conv_1 = conv(bias = const_12, dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = conv_1_strides_0, weight = const_11, x = concat_3)[name = string("conv_1")];
            tensor<int32, [3]> transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [512]> const_13 = const()[name = string("const_13"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363200)))];
            tensor<fp32, [512]> const_14 = const()[name = string("const_14"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(365312)))];
            tensor<int32, [1]> layer_norm_0_axes_0 = const()[name = string("layer_norm_0_axes_0"), val = tensor<int32, [1]>([2])];
            fp32 layer_norm_0_epsilon_0 = const()[name = string("layer_norm_0_epsilon_0"), val = fp32(0x1.0c6f7ap-20)];
            tensor<fp32, [1, ?, 512]> transpose_1 = transpose(perm = transpose_1_perm_0, x = conv_1)[name = string("transpose_42")];
            tensor<fp32, [1, ?, 512]> layer_norm_0 = layer_norm(axes = layer_norm_0_axes_0, beta = const_14, epsilon = layer_norm_0_epsilon_0, gamma = const_13, x = transpose_1)[name = string("layer_norm_0")];
            tensor<int32, [3]> transpose_2_perm_0 = const()[name = string("transpose_2_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [2048, 512, 1]> const_15 = const()[name = string("const_15"), val = tensor<fp32, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(367424)))];
            tensor<fp32, [2048]> const_16 = const()[name = string("const_16"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4561792)))];
            tensor<int32, [1]> conv_2_strides_0 = const()[name = string("conv_2_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_2_pad_type_0 = const()[name = string("conv_2_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_2_pad_0 = const()[name = string("conv_2_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_2_dilations_0 = const()[name = string("conv_2_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_2_groups_0 = const()[name = string("conv_2_groups_0"), val = int32(1)];
            tensor<fp32, [1, 512, ?]> transpose_2 = transpose(perm = transpose_2_perm_0, x = layer_norm_0)[name = string("transpose_41")];
            tensor<fp32, [1, 2048, ?]> conv_2 = conv(bias = const_16, dilations = conv_2_dilations_0, groups = conv_2_groups_0, pad = conv_2_pad_0, pad_type = conv_2_pad_type_0, strides = conv_2_strides_0, weight = const_15, x = transpose_2)[name = string("conv_2")];
            string mul_2_mode_0 = const()[name = string("mul_2_mode_0"), val = string("EXACT")];
            tensor<fp32, [1, 2048, ?]> mul_2 = gelu(mode = mul_2_mode_0, x = conv_2)[name = string("mul_2")];
            tensor<int32, [1]> conv_3_strides_0 = const()[name = string("conv_3_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_3_pad_type_0 = const()[name = string("conv_3_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_3_pad_0 = const()[name = string("conv_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_3_dilations_0 = const()[name = string("conv_3_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_3_groups_0 = const()[name = string("conv_3_groups_0"), val = int32(1)];
            tensor<fp32, [512, 2048, 1]> mul_3_weight_0 = const()[name = string("mul_3_weight_0"), val = tensor<fp32, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4570048)))];
            tensor<fp32, [512]> mul_3_bias_0 = const()[name = string("mul_3_bias_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8764416)))];
            tensor<fp32, [1, 512, ?]> mul_3 = conv(bias = mul_3_bias_0, dilations = conv_3_dilations_0, groups = conv_3_groups_0, pad = conv_3_pad_0, pad_type = conv_3_pad_type_0, strides = conv_3_strides_0, weight = mul_3_weight_0, x = mul_2)[name = string("mul_3")];
            tensor<fp32, [1, 512, ?]> add_2 = add(x = conv_0, y = mul_3)[name = string("add_2")];
            tensor<int32, [3]> slice_by_index_3_begin_0 = const()[name = string("slice_by_index_3_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> slice_by_index_3_end_0 = const()[name = string("slice_by_index_3_end_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<bool, [3]> slice_by_index_3_begin_mask_0 = const()[name = string("slice_by_index_3_begin_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<bool, [3]> slice_by_index_3_end_mask_0 = const()[name = string("slice_by_index_3_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 512, ?]> slice_by_index_3 = slice_by_index(begin = slice_by_index_3_begin_0, begin_mask = slice_by_index_3_begin_mask_0, end = slice_by_index_3_end_0, end_mask = slice_by_index_3_end_mask_0, x = add_2)[name = string("slice_by_index_3")];
            tensor<int32, [3]> tile_2_reps_0 = const()[name = string("tile_2_reps_0"), val = tensor<int32, [3]>([1, 1, 12])];
            tensor<fp32, [1, 512, ?]> tile_2 = tile(reps = tile_2_reps_0, x = slice_by_index_3)[name = string("tile_2")];
            int32 concat_4_axis_0 = const()[name = string("concat_4_axis_0"), val = int32(2)];
            bool concat_4_interleave_0 = const()[name = string("concat_4_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 512, ?]> concat_4 = concat(axis = concat_4_axis_0, interleave = concat_4_interleave_0, values = (tile_2, add_2))[name = string("concat_4")];
            tensor<fp32, [512, 1, 7]> const_23 = const()[name = string("const_23"), val = tensor<fp32, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8766528)))];
            tensor<fp32, [512]> const_24 = const()[name = string("const_24"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8780928)))];
            tensor<int32, [1]> conv_4_strides_0 = const()[name = string("conv_4_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_4_pad_type_0 = const()[name = string("conv_4_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_4_pad_0 = const()[name = string("conv_4_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_4_dilations_0 = const()[name = string("conv_4_dilations_0"), val = tensor<int32, [1]>([2])];
            int32 conv_4_groups_0 = const()[name = string("conv_4_groups_0"), val = int32(512)];
            tensor<fp32, [1, 512, ?]> conv_4 = conv(bias = const_24, dilations = conv_4_dilations_0, groups = conv_4_groups_0, pad = conv_4_pad_0, pad_type = conv_4_pad_type_0, strides = conv_4_strides_0, weight = const_23, x = concat_4)[name = string("conv_4")];
            tensor<int32, [3]> transpose_3_perm_0 = const()[name = string("transpose_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [512]> const_25 = const()[name = string("const_25"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8783040)))];
            tensor<fp32, [512]> const_26 = const()[name = string("const_26"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8785152)))];
            tensor<int32, [1]> layer_norm_1_axes_0 = const()[name = string("layer_norm_1_axes_0"), val = tensor<int32, [1]>([2])];
            fp32 layer_norm_1_epsilon_0 = const()[name = string("layer_norm_1_epsilon_0"), val = fp32(0x1.0c6f7ap-20)];
            tensor<fp32, [1, ?, 512]> transpose_3 = transpose(perm = transpose_3_perm_0, x = conv_4)[name = string("transpose_40")];
            tensor<fp32, [1, ?, 512]> layer_norm_1 = layer_norm(axes = layer_norm_1_axes_0, beta = const_26, epsilon = layer_norm_1_epsilon_0, gamma = const_25, x = transpose_3)[name = string("layer_norm_1")];
            tensor<int32, [3]> transpose_4_perm_0 = const()[name = string("transpose_4_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [2048, 512, 1]> const_27 = const()[name = string("const_27"), val = tensor<fp32, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8787264)))];
            tensor<fp32, [2048]> const_28 = const()[name = string("const_28"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12981632)))];
            tensor<int32, [1]> conv_5_strides_0 = const()[name = string("conv_5_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_5_pad_type_0 = const()[name = string("conv_5_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_5_pad_0 = const()[name = string("conv_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_5_dilations_0 = const()[name = string("conv_5_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_5_groups_0 = const()[name = string("conv_5_groups_0"), val = int32(1)];
            tensor<fp32, [1, 512, ?]> transpose_4 = transpose(perm = transpose_4_perm_0, x = layer_norm_1)[name = string("transpose_39")];
            tensor<fp32, [1, 2048, ?]> conv_5 = conv(bias = const_28, dilations = conv_5_dilations_0, groups = conv_5_groups_0, pad = conv_5_pad_0, pad_type = conv_5_pad_type_0, strides = conv_5_strides_0, weight = const_27, x = transpose_4)[name = string("conv_5")];
            string mul_5_mode_0 = const()[name = string("mul_5_mode_0"), val = string("EXACT")];
            tensor<fp32, [1, 2048, ?]> mul_5 = gelu(mode = mul_5_mode_0, x = conv_5)[name = string("mul_5")];
            tensor<int32, [1]> conv_6_strides_0 = const()[name = string("conv_6_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_6_pad_type_0 = const()[name = string("conv_6_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_6_pad_0 = const()[name = string("conv_6_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_6_dilations_0 = const()[name = string("conv_6_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_6_groups_0 = const()[name = string("conv_6_groups_0"), val = int32(1)];
            tensor<fp32, [512, 2048, 1]> mul_6_weight_0 = const()[name = string("mul_6_weight_0"), val = tensor<fp32, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12989888)))];
            tensor<fp32, [512]> mul_6_bias_0 = const()[name = string("mul_6_bias_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17184256)))];
            tensor<fp32, [1, 512, ?]> mul_6 = conv(bias = mul_6_bias_0, dilations = conv_6_dilations_0, groups = conv_6_groups_0, pad = conv_6_pad_0, pad_type = conv_6_pad_type_0, strides = conv_6_strides_0, weight = mul_6_weight_0, x = mul_5)[name = string("mul_6")];
            tensor<fp32, [1, 512, ?]> add_4 = add(x = add_2, y = mul_6)[name = string("add_4")];
            tensor<int32, [3]> slice_by_index_4_begin_0 = const()[name = string("slice_by_index_4_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> slice_by_index_4_end_0 = const()[name = string("slice_by_index_4_end_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<bool, [3]> slice_by_index_4_begin_mask_0 = const()[name = string("slice_by_index_4_begin_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<bool, [3]> slice_by_index_4_end_mask_0 = const()[name = string("slice_by_index_4_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 512, ?]> slice_by_index_4 = slice_by_index(begin = slice_by_index_4_begin_0, begin_mask = slice_by_index_4_begin_mask_0, end = slice_by_index_4_end_0, end_mask = slice_by_index_4_end_mask_0, x = add_4)[name = string("slice_by_index_4")];
            tensor<int32, [3]> tile_3_reps_0 = const()[name = string("tile_3_reps_0"), val = tensor<int32, [3]>([1, 1, 24])];
            tensor<fp32, [1, 512, ?]> tile_3 = tile(reps = tile_3_reps_0, x = slice_by_index_4)[name = string("tile_3")];
            int32 concat_5_axis_0 = const()[name = string("concat_5_axis_0"), val = int32(2)];
            bool concat_5_interleave_0 = const()[name = string("concat_5_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 512, ?]> concat_5 = concat(axis = concat_5_axis_0, interleave = concat_5_interleave_0, values = (tile_3, add_4))[name = string("concat_5")];
            tensor<fp32, [512, 1, 7]> const_35 = const()[name = string("const_35"), val = tensor<fp32, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17186368)))];
            tensor<fp32, [512]> const_36 = const()[name = string("const_36"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17200768)))];
            tensor<int32, [1]> conv_7_strides_0 = const()[name = string("conv_7_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_7_pad_type_0 = const()[name = string("conv_7_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_7_pad_0 = const()[name = string("conv_7_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_7_dilations_0 = const()[name = string("conv_7_dilations_0"), val = tensor<int32, [1]>([4])];
            int32 conv_7_groups_0 = const()[name = string("conv_7_groups_0"), val = int32(512)];
            tensor<fp32, [1, 512, ?]> conv_7 = conv(bias = const_36, dilations = conv_7_dilations_0, groups = conv_7_groups_0, pad = conv_7_pad_0, pad_type = conv_7_pad_type_0, strides = conv_7_strides_0, weight = const_35, x = concat_5)[name = string("conv_7")];
            tensor<int32, [3]> transpose_5_perm_0 = const()[name = string("transpose_5_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [512]> const_37 = const()[name = string("const_37"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17202880)))];
            tensor<fp32, [512]> const_38 = const()[name = string("const_38"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17204992)))];
            tensor<int32, [1]> layer_norm_2_axes_0 = const()[name = string("layer_norm_2_axes_0"), val = tensor<int32, [1]>([2])];
            fp32 layer_norm_2_epsilon_0 = const()[name = string("layer_norm_2_epsilon_0"), val = fp32(0x1.0c6f7ap-20)];
            tensor<fp32, [1, ?, 512]> transpose_5 = transpose(perm = transpose_5_perm_0, x = conv_7)[name = string("transpose_38")];
            tensor<fp32, [1, ?, 512]> layer_norm_2 = layer_norm(axes = layer_norm_2_axes_0, beta = const_38, epsilon = layer_norm_2_epsilon_0, gamma = const_37, x = transpose_5)[name = string("layer_norm_2")];
            tensor<int32, [3]> transpose_6_perm_0 = const()[name = string("transpose_6_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [2048, 512, 1]> const_39 = const()[name = string("const_39"), val = tensor<fp32, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17207104)))];
            tensor<fp32, [2048]> const_40 = const()[name = string("const_40"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21401472)))];
            tensor<int32, [1]> conv_8_strides_0 = const()[name = string("conv_8_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_8_pad_type_0 = const()[name = string("conv_8_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_8_pad_0 = const()[name = string("conv_8_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_8_dilations_0 = const()[name = string("conv_8_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_8_groups_0 = const()[name = string("conv_8_groups_0"), val = int32(1)];
            tensor<fp32, [1, 512, ?]> transpose_6 = transpose(perm = transpose_6_perm_0, x = layer_norm_2)[name = string("transpose_37")];
            tensor<fp32, [1, 2048, ?]> conv_8 = conv(bias = const_40, dilations = conv_8_dilations_0, groups = conv_8_groups_0, pad = conv_8_pad_0, pad_type = conv_8_pad_type_0, strides = conv_8_strides_0, weight = const_39, x = transpose_6)[name = string("conv_8")];
            string mul_8_mode_0 = const()[name = string("mul_8_mode_0"), val = string("EXACT")];
            tensor<fp32, [1, 2048, ?]> mul_8 = gelu(mode = mul_8_mode_0, x = conv_8)[name = string("mul_8")];
            tensor<int32, [1]> conv_9_strides_0 = const()[name = string("conv_9_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_9_pad_type_0 = const()[name = string("conv_9_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_9_pad_0 = const()[name = string("conv_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_9_dilations_0 = const()[name = string("conv_9_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_9_groups_0 = const()[name = string("conv_9_groups_0"), val = int32(1)];
            tensor<fp32, [512, 2048, 1]> mul_9_weight_0 = const()[name = string("mul_9_weight_0"), val = tensor<fp32, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21409728)))];
            tensor<fp32, [512]> mul_9_bias_0 = const()[name = string("mul_9_bias_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25604096)))];
            tensor<fp32, [1, 512, ?]> mul_9 = conv(bias = mul_9_bias_0, dilations = conv_9_dilations_0, groups = conv_9_groups_0, pad = conv_9_pad_0, pad_type = conv_9_pad_type_0, strides = conv_9_strides_0, weight = mul_9_weight_0, x = mul_8)[name = string("mul_9")];
            tensor<fp32, [1, 512, ?]> add_6 = add(x = add_4, y = mul_9)[name = string("add_6")];
            tensor<int32, [3]> slice_by_index_5_begin_0 = const()[name = string("slice_by_index_5_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> slice_by_index_5_end_0 = const()[name = string("slice_by_index_5_end_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<bool, [3]> slice_by_index_5_begin_mask_0 = const()[name = string("slice_by_index_5_begin_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<bool, [3]> slice_by_index_5_end_mask_0 = const()[name = string("slice_by_index_5_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 512, ?]> slice_by_index_5 = slice_by_index(begin = slice_by_index_5_begin_0, begin_mask = slice_by_index_5_begin_mask_0, end = slice_by_index_5_end_0, end_mask = slice_by_index_5_end_mask_0, x = add_6)[name = string("slice_by_index_5")];
            tensor<int32, [3]> tile_4_reps_0 = const()[name = string("tile_4_reps_0"), val = tensor<int32, [3]>([1, 1, 6])];
            tensor<fp32, [1, 512, ?]> tile_4 = tile(reps = tile_4_reps_0, x = slice_by_index_5)[name = string("tile_4")];
            int32 concat_6_axis_0 = const()[name = string("concat_6_axis_0"), val = int32(2)];
            bool concat_6_interleave_0 = const()[name = string("concat_6_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 512, ?]> concat_6 = concat(axis = concat_6_axis_0, interleave = concat_6_interleave_0, values = (tile_4, add_6))[name = string("concat_6")];
            tensor<fp32, [512, 1, 7]> const_47 = const()[name = string("const_47"), val = tensor<fp32, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25606208)))];
            tensor<fp32, [512]> const_48 = const()[name = string("const_48"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25620608)))];
            tensor<int32, [1]> conv_10_strides_0 = const()[name = string("conv_10_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_10_pad_type_0 = const()[name = string("conv_10_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_10_pad_0 = const()[name = string("conv_10_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_10_dilations_0 = const()[name = string("conv_10_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_10_groups_0 = const()[name = string("conv_10_groups_0"), val = int32(512)];
            tensor<fp32, [1, 512, ?]> conv_10 = conv(bias = const_48, dilations = conv_10_dilations_0, groups = conv_10_groups_0, pad = conv_10_pad_0, pad_type = conv_10_pad_type_0, strides = conv_10_strides_0, weight = const_47, x = concat_6)[name = string("conv_10")];
            tensor<int32, [3]> transpose_7_perm_0 = const()[name = string("transpose_7_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [512]> const_49 = const()[name = string("const_49"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25622720)))];
            tensor<fp32, [512]> const_50 = const()[name = string("const_50"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25624832)))];
            tensor<int32, [1]> layer_norm_3_axes_0 = const()[name = string("layer_norm_3_axes_0"), val = tensor<int32, [1]>([2])];
            fp32 layer_norm_3_epsilon_0 = const()[name = string("layer_norm_3_epsilon_0"), val = fp32(0x1.0c6f7ap-20)];
            tensor<fp32, [1, ?, 512]> transpose_7 = transpose(perm = transpose_7_perm_0, x = conv_10)[name = string("transpose_36")];
            tensor<fp32, [1, ?, 512]> layer_norm_3 = layer_norm(axes = layer_norm_3_axes_0, beta = const_50, epsilon = layer_norm_3_epsilon_0, gamma = const_49, x = transpose_7)[name = string("layer_norm_3")];
            tensor<int32, [3]> transpose_8_perm_0 = const()[name = string("transpose_8_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [2048, 512, 1]> const_51 = const()[name = string("const_51"), val = tensor<fp32, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25626944)))];
            tensor<fp32, [2048]> const_52 = const()[name = string("const_52"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29821312)))];
            tensor<int32, [1]> conv_11_strides_0 = const()[name = string("conv_11_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_11_pad_type_0 = const()[name = string("conv_11_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_11_pad_0 = const()[name = string("conv_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_11_dilations_0 = const()[name = string("conv_11_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_11_groups_0 = const()[name = string("conv_11_groups_0"), val = int32(1)];
            tensor<fp32, [1, 512, ?]> transpose_8 = transpose(perm = transpose_8_perm_0, x = layer_norm_3)[name = string("transpose_35")];
            tensor<fp32, [1, 2048, ?]> conv_11 = conv(bias = const_52, dilations = conv_11_dilations_0, groups = conv_11_groups_0, pad = conv_11_pad_0, pad_type = conv_11_pad_type_0, strides = conv_11_strides_0, weight = const_51, x = transpose_8)[name = string("conv_11")];
            string mul_11_mode_0 = const()[name = string("mul_11_mode_0"), val = string("EXACT")];
            tensor<fp32, [1, 2048, ?]> mul_11 = gelu(mode = mul_11_mode_0, x = conv_11)[name = string("mul_11")];
            tensor<int32, [1]> conv_12_strides_0 = const()[name = string("conv_12_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_12_pad_type_0 = const()[name = string("conv_12_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_12_pad_0 = const()[name = string("conv_12_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_12_dilations_0 = const()[name = string("conv_12_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_12_groups_0 = const()[name = string("conv_12_groups_0"), val = int32(1)];
            tensor<fp32, [512, 2048, 1]> mul_12_weight_0 = const()[name = string("mul_12_weight_0"), val = tensor<fp32, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29829568)))];
            tensor<fp32, [512]> mul_12_bias_0 = const()[name = string("mul_12_bias_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34023936)))];
            tensor<fp32, [1, 512, ?]> mul_12 = conv(bias = mul_12_bias_0, dilations = conv_12_dilations_0, groups = conv_12_groups_0, pad = conv_12_pad_0, pad_type = conv_12_pad_type_0, strides = conv_12_strides_0, weight = mul_12_weight_0, x = mul_11)[name = string("mul_12")];
            tensor<fp32, [1, 512, ?]> add_8 = add(x = add_6, y = mul_12)[name = string("add_8")];
            tensor<int32, [3]> slice_by_index_6_begin_0 = const()[name = string("slice_by_index_6_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> slice_by_index_6_end_0 = const()[name = string("slice_by_index_6_end_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<bool, [3]> slice_by_index_6_begin_mask_0 = const()[name = string("slice_by_index_6_begin_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<bool, [3]> slice_by_index_6_end_mask_0 = const()[name = string("slice_by_index_6_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 512, ?]> slice_by_index_6 = slice_by_index(begin = slice_by_index_6_begin_0, begin_mask = slice_by_index_6_begin_mask_0, end = slice_by_index_6_end_0, end_mask = slice_by_index_6_end_mask_0, x = add_8)[name = string("slice_by_index_6")];
            tensor<int32, [3]> tile_5_reps_0 = const()[name = string("tile_5_reps_0"), val = tensor<int32, [3]>([1, 1, 12])];
            tensor<fp32, [1, 512, ?]> tile_5 = tile(reps = tile_5_reps_0, x = slice_by_index_6)[name = string("tile_5")];
            int32 concat_7_axis_0 = const()[name = string("concat_7_axis_0"), val = int32(2)];
            bool concat_7_interleave_0 = const()[name = string("concat_7_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 512, ?]> concat_7 = concat(axis = concat_7_axis_0, interleave = concat_7_interleave_0, values = (tile_5, add_8))[name = string("concat_7")];
            tensor<fp32, [512, 1, 7]> const_59 = const()[name = string("const_59"), val = tensor<fp32, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34026048)))];
            tensor<fp32, [512]> const_60 = const()[name = string("const_60"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34040448)))];
            tensor<int32, [1]> conv_13_strides_0 = const()[name = string("conv_13_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_13_pad_type_0 = const()[name = string("conv_13_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_13_pad_0 = const()[name = string("conv_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_13_dilations_0 = const()[name = string("conv_13_dilations_0"), val = tensor<int32, [1]>([2])];
            int32 conv_13_groups_0 = const()[name = string("conv_13_groups_0"), val = int32(512)];
            tensor<fp32, [1, 512, ?]> conv_13 = conv(bias = const_60, dilations = conv_13_dilations_0, groups = conv_13_groups_0, pad = conv_13_pad_0, pad_type = conv_13_pad_type_0, strides = conv_13_strides_0, weight = const_59, x = concat_7)[name = string("conv_13")];
            tensor<int32, [3]> transpose_9_perm_0 = const()[name = string("transpose_9_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [512]> const_61 = const()[name = string("const_61"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34042560)))];
            tensor<fp32, [512]> const_62 = const()[name = string("const_62"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34044672)))];
            tensor<int32, [1]> layer_norm_4_axes_0 = const()[name = string("layer_norm_4_axes_0"), val = tensor<int32, [1]>([2])];
            fp32 layer_norm_4_epsilon_0 = const()[name = string("layer_norm_4_epsilon_0"), val = fp32(0x1.0c6f7ap-20)];
            tensor<fp32, [1, ?, 512]> transpose_9 = transpose(perm = transpose_9_perm_0, x = conv_13)[name = string("transpose_34")];
            tensor<fp32, [1, ?, 512]> layer_norm_4 = layer_norm(axes = layer_norm_4_axes_0, beta = const_62, epsilon = layer_norm_4_epsilon_0, gamma = const_61, x = transpose_9)[name = string("layer_norm_4")];
            tensor<int32, [3]> transpose_10_perm_0 = const()[name = string("transpose_10_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [2048, 512, 1]> const_63 = const()[name = string("const_63"), val = tensor<fp32, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34046784)))];
            tensor<fp32, [2048]> const_64 = const()[name = string("const_64"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38241152)))];
            tensor<int32, [1]> conv_14_strides_0 = const()[name = string("conv_14_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_14_pad_type_0 = const()[name = string("conv_14_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_14_pad_0 = const()[name = string("conv_14_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_14_dilations_0 = const()[name = string("conv_14_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_14_groups_0 = const()[name = string("conv_14_groups_0"), val = int32(1)];
            tensor<fp32, [1, 512, ?]> transpose_10 = transpose(perm = transpose_10_perm_0, x = layer_norm_4)[name = string("transpose_33")];
            tensor<fp32, [1, 2048, ?]> conv_14 = conv(bias = const_64, dilations = conv_14_dilations_0, groups = conv_14_groups_0, pad = conv_14_pad_0, pad_type = conv_14_pad_type_0, strides = conv_14_strides_0, weight = const_63, x = transpose_10)[name = string("conv_14")];
            string mul_14_mode_0 = const()[name = string("mul_14_mode_0"), val = string("EXACT")];
            tensor<fp32, [1, 2048, ?]> mul_14 = gelu(mode = mul_14_mode_0, x = conv_14)[name = string("mul_14")];
            tensor<int32, [1]> conv_15_strides_0 = const()[name = string("conv_15_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_15_pad_type_0 = const()[name = string("conv_15_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_15_pad_0 = const()[name = string("conv_15_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_15_dilations_0 = const()[name = string("conv_15_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_15_groups_0 = const()[name = string("conv_15_groups_0"), val = int32(1)];
            tensor<fp32, [512, 2048, 1]> mul_15_weight_0 = const()[name = string("mul_15_weight_0"), val = tensor<fp32, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38249408)))];
            tensor<fp32, [512]> mul_15_bias_0 = const()[name = string("mul_15_bias_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42443776)))];
            tensor<fp32, [1, 512, ?]> mul_15 = conv(bias = mul_15_bias_0, dilations = conv_15_dilations_0, groups = conv_15_groups_0, pad = conv_15_pad_0, pad_type = conv_15_pad_type_0, strides = conv_15_strides_0, weight = mul_15_weight_0, x = mul_14)[name = string("mul_15")];
            tensor<fp32, [1, 512, ?]> add_10 = add(x = add_8, y = mul_15)[name = string("add_10")];
            tensor<int32, [3]> slice_by_index_7_begin_0 = const()[name = string("slice_by_index_7_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> slice_by_index_7_end_0 = const()[name = string("slice_by_index_7_end_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<bool, [3]> slice_by_index_7_begin_mask_0 = const()[name = string("slice_by_index_7_begin_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<bool, [3]> slice_by_index_7_end_mask_0 = const()[name = string("slice_by_index_7_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 512, ?]> slice_by_index_7 = slice_by_index(begin = slice_by_index_7_begin_0, begin_mask = slice_by_index_7_begin_mask_0, end = slice_by_index_7_end_0, end_mask = slice_by_index_7_end_mask_0, x = add_10)[name = string("slice_by_index_7")];
            tensor<int32, [3]> tile_6_reps_0 = const()[name = string("tile_6_reps_0"), val = tensor<int32, [3]>([1, 1, 24])];
            tensor<fp32, [1, 512, ?]> tile_6 = tile(reps = tile_6_reps_0, x = slice_by_index_7)[name = string("tile_6")];
            int32 concat_8_axis_0 = const()[name = string("concat_8_axis_0"), val = int32(2)];
            bool concat_8_interleave_0 = const()[name = string("concat_8_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 512, ?]> concat_8 = concat(axis = concat_8_axis_0, interleave = concat_8_interleave_0, values = (tile_6, add_10))[name = string("concat_8")];
            tensor<fp32, [512, 1, 7]> const_71 = const()[name = string("const_71"), val = tensor<fp32, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42445888)))];
            tensor<fp32, [512]> const_72 = const()[name = string("const_72"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42460288)))];
            tensor<int32, [1]> conv_16_strides_0 = const()[name = string("conv_16_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_16_pad_type_0 = const()[name = string("conv_16_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_16_pad_0 = const()[name = string("conv_16_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_16_dilations_0 = const()[name = string("conv_16_dilations_0"), val = tensor<int32, [1]>([4])];
            int32 conv_16_groups_0 = const()[name = string("conv_16_groups_0"), val = int32(512)];
            tensor<fp32, [1, 512, ?]> conv_16 = conv(bias = const_72, dilations = conv_16_dilations_0, groups = conv_16_groups_0, pad = conv_16_pad_0, pad_type = conv_16_pad_type_0, strides = conv_16_strides_0, weight = const_71, x = concat_8)[name = string("conv_16")];
            tensor<int32, [3]> transpose_11_perm_0 = const()[name = string("transpose_11_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [512]> const_73 = const()[name = string("const_73"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42462400)))];
            tensor<fp32, [512]> const_74 = const()[name = string("const_74"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42464512)))];
            tensor<int32, [1]> layer_norm_5_axes_0 = const()[name = string("layer_norm_5_axes_0"), val = tensor<int32, [1]>([2])];
            fp32 layer_norm_5_epsilon_0 = const()[name = string("layer_norm_5_epsilon_0"), val = fp32(0x1.0c6f7ap-20)];
            tensor<fp32, [1, ?, 512]> transpose_11 = transpose(perm = transpose_11_perm_0, x = conv_16)[name = string("transpose_32")];
            tensor<fp32, [1, ?, 512]> layer_norm_5 = layer_norm(axes = layer_norm_5_axes_0, beta = const_74, epsilon = layer_norm_5_epsilon_0, gamma = const_73, x = transpose_11)[name = string("layer_norm_5")];
            tensor<int32, [3]> transpose_12_perm_0 = const()[name = string("transpose_12_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [2048, 512, 1]> const_75 = const()[name = string("const_75"), val = tensor<fp32, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42466624)))];
            tensor<fp32, [2048]> const_76 = const()[name = string("const_76"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46660992)))];
            tensor<int32, [1]> conv_17_strides_0 = const()[name = string("conv_17_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_17_pad_type_0 = const()[name = string("conv_17_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_17_pad_0 = const()[name = string("conv_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_17_dilations_0 = const()[name = string("conv_17_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_17_groups_0 = const()[name = string("conv_17_groups_0"), val = int32(1)];
            tensor<fp32, [1, 512, ?]> transpose_12 = transpose(perm = transpose_12_perm_0, x = layer_norm_5)[name = string("transpose_31")];
            tensor<fp32, [1, 2048, ?]> conv_17 = conv(bias = const_76, dilations = conv_17_dilations_0, groups = conv_17_groups_0, pad = conv_17_pad_0, pad_type = conv_17_pad_type_0, strides = conv_17_strides_0, weight = const_75, x = transpose_12)[name = string("conv_17")];
            string mul_17_mode_0 = const()[name = string("mul_17_mode_0"), val = string("EXACT")];
            tensor<fp32, [1, 2048, ?]> mul_17 = gelu(mode = mul_17_mode_0, x = conv_17)[name = string("mul_17")];
            tensor<int32, [1]> conv_18_strides_0 = const()[name = string("conv_18_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_18_pad_type_0 = const()[name = string("conv_18_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_18_pad_0 = const()[name = string("conv_18_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_18_dilations_0 = const()[name = string("conv_18_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_18_groups_0 = const()[name = string("conv_18_groups_0"), val = int32(1)];
            tensor<fp32, [512, 2048, 1]> mul_18_weight_0 = const()[name = string("mul_18_weight_0"), val = tensor<fp32, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46669248)))];
            tensor<fp32, [512]> mul_18_bias_0 = const()[name = string("mul_18_bias_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50863616)))];
            tensor<fp32, [1, 512, ?]> mul_18 = conv(bias = mul_18_bias_0, dilations = conv_18_dilations_0, groups = conv_18_groups_0, pad = conv_18_pad_0, pad_type = conv_18_pad_type_0, strides = conv_18_strides_0, weight = mul_18_weight_0, x = mul_17)[name = string("mul_18")];
            tensor<fp32, [1, 512, ?]> add_12 = add(x = add_10, y = mul_18)[name = string("add_12")];
            tensor<int32, [3]> slice_by_index_8_begin_0 = const()[name = string("slice_by_index_8_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> slice_by_index_8_end_0 = const()[name = string("slice_by_index_8_end_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<bool, [3]> slice_by_index_8_begin_mask_0 = const()[name = string("slice_by_index_8_begin_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<bool, [3]> slice_by_index_8_end_mask_0 = const()[name = string("slice_by_index_8_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 512, ?]> slice_by_index_8 = slice_by_index(begin = slice_by_index_8_begin_0, begin_mask = slice_by_index_8_begin_mask_0, end = slice_by_index_8_end_0, end_mask = slice_by_index_8_end_mask_0, x = add_12)[name = string("slice_by_index_8")];
            tensor<int32, [3]> tile_7_reps_0 = const()[name = string("tile_7_reps_0"), val = tensor<int32, [3]>([1, 1, 6])];
            tensor<fp32, [1, 512, ?]> tile_7 = tile(reps = tile_7_reps_0, x = slice_by_index_8)[name = string("tile_7")];
            int32 concat_9_axis_0 = const()[name = string("concat_9_axis_0"), val = int32(2)];
            bool concat_9_interleave_0 = const()[name = string("concat_9_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 512, ?]> concat_9 = concat(axis = concat_9_axis_0, interleave = concat_9_interleave_0, values = (tile_7, add_12))[name = string("concat_9")];
            tensor<fp32, [512, 1, 7]> const_83 = const()[name = string("const_83"), val = tensor<fp32, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50865728)))];
            tensor<fp32, [512]> const_84 = const()[name = string("const_84"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50880128)))];
            tensor<int32, [1]> conv_19_strides_0 = const()[name = string("conv_19_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_19_pad_type_0 = const()[name = string("conv_19_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_19_pad_0 = const()[name = string("conv_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_19_dilations_0 = const()[name = string("conv_19_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_19_groups_0 = const()[name = string("conv_19_groups_0"), val = int32(512)];
            tensor<fp32, [1, 512, ?]> conv_19 = conv(bias = const_84, dilations = conv_19_dilations_0, groups = conv_19_groups_0, pad = conv_19_pad_0, pad_type = conv_19_pad_type_0, strides = conv_19_strides_0, weight = const_83, x = concat_9)[name = string("conv_19")];
            tensor<int32, [3]> transpose_13_perm_0 = const()[name = string("transpose_13_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [512]> const_85 = const()[name = string("const_85"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50882240)))];
            tensor<fp32, [512]> const_86 = const()[name = string("const_86"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50884352)))];
            tensor<int32, [1]> layer_norm_6_axes_0 = const()[name = string("layer_norm_6_axes_0"), val = tensor<int32, [1]>([2])];
            fp32 layer_norm_6_epsilon_0 = const()[name = string("layer_norm_6_epsilon_0"), val = fp32(0x1.0c6f7ap-20)];
            tensor<fp32, [1, ?, 512]> transpose_13 = transpose(perm = transpose_13_perm_0, x = conv_19)[name = string("transpose_30")];
            tensor<fp32, [1, ?, 512]> layer_norm_6 = layer_norm(axes = layer_norm_6_axes_0, beta = const_86, epsilon = layer_norm_6_epsilon_0, gamma = const_85, x = transpose_13)[name = string("layer_norm_6")];
            tensor<int32, [3]> transpose_14_perm_0 = const()[name = string("transpose_14_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [2048, 512, 1]> const_87 = const()[name = string("const_87"), val = tensor<fp32, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50886464)))];
            tensor<fp32, [2048]> const_88 = const()[name = string("const_88"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55080832)))];
            tensor<int32, [1]> conv_20_strides_0 = const()[name = string("conv_20_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_20_pad_type_0 = const()[name = string("conv_20_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_20_pad_0 = const()[name = string("conv_20_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_20_dilations_0 = const()[name = string("conv_20_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_20_groups_0 = const()[name = string("conv_20_groups_0"), val = int32(1)];
            tensor<fp32, [1, 512, ?]> transpose_14 = transpose(perm = transpose_14_perm_0, x = layer_norm_6)[name = string("transpose_29")];
            tensor<fp32, [1, 2048, ?]> conv_20 = conv(bias = const_88, dilations = conv_20_dilations_0, groups = conv_20_groups_0, pad = conv_20_pad_0, pad_type = conv_20_pad_type_0, strides = conv_20_strides_0, weight = const_87, x = transpose_14)[name = string("conv_20")];
            string mul_20_mode_0 = const()[name = string("mul_20_mode_0"), val = string("EXACT")];
            tensor<fp32, [1, 2048, ?]> mul_20 = gelu(mode = mul_20_mode_0, x = conv_20)[name = string("mul_20")];
            tensor<int32, [1]> conv_21_strides_0 = const()[name = string("conv_21_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_21_pad_type_0 = const()[name = string("conv_21_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_21_pad_0 = const()[name = string("conv_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_21_dilations_0 = const()[name = string("conv_21_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_21_groups_0 = const()[name = string("conv_21_groups_0"), val = int32(1)];
            tensor<fp32, [512, 2048, 1]> mul_21_weight_0 = const()[name = string("mul_21_weight_0"), val = tensor<fp32, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55089088)))];
            tensor<fp32, [512]> mul_21_bias_0 = const()[name = string("mul_21_bias_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59283456)))];
            tensor<fp32, [1, 512, ?]> mul_21 = conv(bias = mul_21_bias_0, dilations = conv_21_dilations_0, groups = conv_21_groups_0, pad = conv_21_pad_0, pad_type = conv_21_pad_type_0, strides = conv_21_strides_0, weight = mul_21_weight_0, x = mul_20)[name = string("mul_21")];
            tensor<fp32, [1, 512, ?]> add_14 = add(x = add_12, y = mul_21)[name = string("add_14")];
            tensor<int32, [3]> slice_by_index_9_begin_0 = const()[name = string("slice_by_index_9_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> slice_by_index_9_end_0 = const()[name = string("slice_by_index_9_end_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<bool, [3]> slice_by_index_9_begin_mask_0 = const()[name = string("slice_by_index_9_begin_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<bool, [3]> slice_by_index_9_end_mask_0 = const()[name = string("slice_by_index_9_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 512, ?]> slice_by_index_9 = slice_by_index(begin = slice_by_index_9_begin_0, begin_mask = slice_by_index_9_begin_mask_0, end = slice_by_index_9_end_0, end_mask = slice_by_index_9_end_mask_0, x = add_14)[name = string("slice_by_index_9")];
            tensor<int32, [3]> tile_8_reps_0 = const()[name = string("tile_8_reps_0"), val = tensor<int32, [3]>([1, 1, 6])];
            tensor<fp32, [1, 512, ?]> tile_8 = tile(reps = tile_8_reps_0, x = slice_by_index_9)[name = string("tile_8")];
            int32 concat_10_axis_0 = const()[name = string("concat_10_axis_0"), val = int32(2)];
            bool concat_10_interleave_0 = const()[name = string("concat_10_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 512, ?]> concat_10 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (tile_8, add_14))[name = string("concat_10")];
            tensor<fp32, [512, 1, 7]> const_95 = const()[name = string("const_95"), val = tensor<fp32, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59285568)))];
            tensor<fp32, [512]> const_96 = const()[name = string("const_96"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59299968)))];
            tensor<int32, [1]> conv_22_strides_0 = const()[name = string("conv_22_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_22_pad_type_0 = const()[name = string("conv_22_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_22_pad_0 = const()[name = string("conv_22_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_22_dilations_0 = const()[name = string("conv_22_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_22_groups_0 = const()[name = string("conv_22_groups_0"), val = int32(512)];
            tensor<fp32, [1, 512, ?]> conv_22 = conv(bias = const_96, dilations = conv_22_dilations_0, groups = conv_22_groups_0, pad = conv_22_pad_0, pad_type = conv_22_pad_type_0, strides = conv_22_strides_0, weight = const_95, x = concat_10)[name = string("conv_22")];
            tensor<int32, [3]> transpose_15_perm_0 = const()[name = string("transpose_15_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [512]> const_97 = const()[name = string("const_97"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59302080)))];
            tensor<fp32, [512]> const_98 = const()[name = string("const_98"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59304192)))];
            tensor<int32, [1]> layer_norm_7_axes_0 = const()[name = string("layer_norm_7_axes_0"), val = tensor<int32, [1]>([2])];
            fp32 layer_norm_7_epsilon_0 = const()[name = string("layer_norm_7_epsilon_0"), val = fp32(0x1.0c6f7ap-20)];
            tensor<fp32, [1, ?, 512]> transpose_15 = transpose(perm = transpose_15_perm_0, x = conv_22)[name = string("transpose_28")];
            tensor<fp32, [1, ?, 512]> layer_norm_7 = layer_norm(axes = layer_norm_7_axes_0, beta = const_98, epsilon = layer_norm_7_epsilon_0, gamma = const_97, x = transpose_15)[name = string("layer_norm_7")];
            tensor<int32, [3]> transpose_16_perm_0 = const()[name = string("transpose_16_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [2048, 512, 1]> const_99 = const()[name = string("const_99"), val = tensor<fp32, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59306304)))];
            tensor<fp32, [2048]> const_100 = const()[name = string("const_100"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63500672)))];
            tensor<int32, [1]> conv_23_strides_0 = const()[name = string("conv_23_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_23_pad_type_0 = const()[name = string("conv_23_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_23_pad_0 = const()[name = string("conv_23_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_23_dilations_0 = const()[name = string("conv_23_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_23_groups_0 = const()[name = string("conv_23_groups_0"), val = int32(1)];
            tensor<fp32, [1, 512, ?]> transpose_16 = transpose(perm = transpose_16_perm_0, x = layer_norm_7)[name = string("transpose_27")];
            tensor<fp32, [1, 2048, ?]> conv_23 = conv(bias = const_100, dilations = conv_23_dilations_0, groups = conv_23_groups_0, pad = conv_23_pad_0, pad_type = conv_23_pad_type_0, strides = conv_23_strides_0, weight = const_99, x = transpose_16)[name = string("conv_23")];
            string mul_23_mode_0 = const()[name = string("mul_23_mode_0"), val = string("EXACT")];
            tensor<fp32, [1, 2048, ?]> mul_23 = gelu(mode = mul_23_mode_0, x = conv_23)[name = string("mul_23")];
            tensor<int32, [1]> conv_24_strides_0 = const()[name = string("conv_24_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_24_pad_type_0 = const()[name = string("conv_24_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_24_pad_0 = const()[name = string("conv_24_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_24_dilations_0 = const()[name = string("conv_24_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_24_groups_0 = const()[name = string("conv_24_groups_0"), val = int32(1)];
            tensor<fp32, [512, 2048, 1]> mul_24_weight_0 = const()[name = string("mul_24_weight_0"), val = tensor<fp32, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63508928)))];
            tensor<fp32, [512]> mul_24_bias_0 = const()[name = string("mul_24_bias_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67703296)))];
            tensor<fp32, [1, 512, ?]> mul_24 = conv(bias = mul_24_bias_0, dilations = conv_24_dilations_0, groups = conv_24_groups_0, pad = conv_24_pad_0, pad_type = conv_24_pad_type_0, strides = conv_24_strides_0, weight = mul_24_weight_0, x = mul_23)[name = string("mul_24")];
            tensor<fp32, [1, 512, ?]> add_16 = add(x = add_14, y = mul_24)[name = string("add_16")];
            tensor<int32, [3]> slice_by_index_10_begin_0 = const()[name = string("slice_by_index_10_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> slice_by_index_10_end_0 = const()[name = string("slice_by_index_10_end_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<bool, [3]> slice_by_index_10_begin_mask_0 = const()[name = string("slice_by_index_10_begin_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<bool, [3]> slice_by_index_10_end_mask_0 = const()[name = string("slice_by_index_10_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 512, ?]> slice_by_index_10 = slice_by_index(begin = slice_by_index_10_begin_0, begin_mask = slice_by_index_10_begin_mask_0, end = slice_by_index_10_end_0, end_mask = slice_by_index_10_end_mask_0, x = add_16)[name = string("slice_by_index_10")];
            tensor<int32, [3]> tile_9_reps_0 = const()[name = string("tile_9_reps_0"), val = tensor<int32, [3]>([1, 1, 6])];
            tensor<fp32, [1, 512, ?]> tile_9 = tile(reps = tile_9_reps_0, x = slice_by_index_10)[name = string("tile_9")];
            int32 concat_11_axis_0 = const()[name = string("concat_11_axis_0"), val = int32(2)];
            bool concat_11_interleave_0 = const()[name = string("concat_11_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 512, ?]> concat_11 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (tile_9, add_16))[name = string("concat_11")];
            tensor<fp32, [512, 1, 7]> const_107 = const()[name = string("const_107"), val = tensor<fp32, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67705408)))];
            tensor<fp32, [512]> const_108 = const()[name = string("const_108"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67719808)))];
            tensor<int32, [1]> conv_25_strides_0 = const()[name = string("conv_25_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_25_pad_type_0 = const()[name = string("conv_25_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_25_pad_0 = const()[name = string("conv_25_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_25_dilations_0 = const()[name = string("conv_25_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_25_groups_0 = const()[name = string("conv_25_groups_0"), val = int32(512)];
            tensor<fp32, [1, 512, ?]> conv_25 = conv(bias = const_108, dilations = conv_25_dilations_0, groups = conv_25_groups_0, pad = conv_25_pad_0, pad_type = conv_25_pad_type_0, strides = conv_25_strides_0, weight = const_107, x = concat_11)[name = string("conv_25")];
            tensor<int32, [3]> transpose_17_perm_0 = const()[name = string("transpose_17_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [512]> const_109 = const()[name = string("const_109"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67721920)))];
            tensor<fp32, [512]> const_110 = const()[name = string("const_110"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67724032)))];
            tensor<int32, [1]> layer_norm_8_axes_0 = const()[name = string("layer_norm_8_axes_0"), val = tensor<int32, [1]>([2])];
            fp32 layer_norm_8_epsilon_0 = const()[name = string("layer_norm_8_epsilon_0"), val = fp32(0x1.0c6f7ap-20)];
            tensor<fp32, [1, ?, 512]> transpose_17 = transpose(perm = transpose_17_perm_0, x = conv_25)[name = string("transpose_26")];
            tensor<fp32, [1, ?, 512]> layer_norm_8 = layer_norm(axes = layer_norm_8_axes_0, beta = const_110, epsilon = layer_norm_8_epsilon_0, gamma = const_109, x = transpose_17)[name = string("layer_norm_8")];
            tensor<int32, [3]> transpose_18_perm_0 = const()[name = string("transpose_18_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [2048, 512, 1]> const_111 = const()[name = string("const_111"), val = tensor<fp32, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67726144)))];
            tensor<fp32, [2048]> const_112 = const()[name = string("const_112"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71920512)))];
            tensor<int32, [1]> conv_26_strides_0 = const()[name = string("conv_26_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_26_pad_type_0 = const()[name = string("conv_26_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_26_pad_0 = const()[name = string("conv_26_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_26_dilations_0 = const()[name = string("conv_26_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_26_groups_0 = const()[name = string("conv_26_groups_0"), val = int32(1)];
            tensor<fp32, [1, 512, ?]> transpose_18 = transpose(perm = transpose_18_perm_0, x = layer_norm_8)[name = string("transpose_25")];
            tensor<fp32, [1, 2048, ?]> conv_26 = conv(bias = const_112, dilations = conv_26_dilations_0, groups = conv_26_groups_0, pad = conv_26_pad_0, pad_type = conv_26_pad_type_0, strides = conv_26_strides_0, weight = const_111, x = transpose_18)[name = string("conv_26")];
            string mul_26_mode_0 = const()[name = string("mul_26_mode_0"), val = string("EXACT")];
            tensor<fp32, [1, 2048, ?]> mul_26 = gelu(mode = mul_26_mode_0, x = conv_26)[name = string("mul_26")];
            tensor<int32, [1]> conv_27_strides_0 = const()[name = string("conv_27_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_27_pad_type_0 = const()[name = string("conv_27_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_27_pad_0 = const()[name = string("conv_27_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_27_dilations_0 = const()[name = string("conv_27_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_27_groups_0 = const()[name = string("conv_27_groups_0"), val = int32(1)];
            tensor<fp32, [512, 2048, 1]> mul_27_weight_0 = const()[name = string("mul_27_weight_0"), val = tensor<fp32, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71928768)))];
            tensor<fp32, [512]> mul_27_bias_0 = const()[name = string("mul_27_bias_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76123136)))];
            tensor<fp32, [1, 512, ?]> mul_27 = conv(bias = mul_27_bias_0, dilations = conv_27_dilations_0, groups = conv_27_groups_0, pad = conv_27_pad_0, pad_type = conv_27_pad_type_0, strides = conv_27_strides_0, weight = mul_27_weight_0, x = mul_26)[name = string("mul_27")];
            tensor<fp32, [1, 512, ?]> add_18 = add(x = add_16, y = mul_27)[name = string("add_18")];
            tensor<int32, [3]> slice_by_index_11_begin_0 = const()[name = string("slice_by_index_11_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> slice_by_index_11_end_0 = const()[name = string("slice_by_index_11_end_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<bool, [3]> slice_by_index_11_begin_mask_0 = const()[name = string("slice_by_index_11_begin_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<bool, [3]> slice_by_index_11_end_mask_0 = const()[name = string("slice_by_index_11_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 512, ?]> slice_by_index_11 = slice_by_index(begin = slice_by_index_11_begin_0, begin_mask = slice_by_index_11_begin_mask_0, end = slice_by_index_11_end_0, end_mask = slice_by_index_11_end_mask_0, x = add_18)[name = string("slice_by_index_11")];
            tensor<int32, [3]> tile_10_reps_0 = const()[name = string("tile_10_reps_0"), val = tensor<int32, [3]>([1, 1, 6])];
            tensor<fp32, [1, 512, ?]> tile_10 = tile(reps = tile_10_reps_0, x = slice_by_index_11)[name = string("tile_10")];
            int32 concat_12_axis_0 = const()[name = string("concat_12_axis_0"), val = int32(2)];
            bool concat_12_interleave_0 = const()[name = string("concat_12_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 512, ?]> concat_12 = concat(axis = concat_12_axis_0, interleave = concat_12_interleave_0, values = (tile_10, add_18))[name = string("concat_12")];
            tensor<fp32, [512, 1, 7]> const_119 = const()[name = string("const_119"), val = tensor<fp32, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76125248)))];
            tensor<fp32, [512]> const_120 = const()[name = string("const_120"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76139648)))];
            tensor<int32, [1]> conv_28_strides_0 = const()[name = string("conv_28_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_28_pad_type_0 = const()[name = string("conv_28_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_28_pad_0 = const()[name = string("conv_28_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_28_dilations_0 = const()[name = string("conv_28_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_28_groups_0 = const()[name = string("conv_28_groups_0"), val = int32(512)];
            tensor<fp32, [1, 512, ?]> conv_28 = conv(bias = const_120, dilations = conv_28_dilations_0, groups = conv_28_groups_0, pad = conv_28_pad_0, pad_type = conv_28_pad_type_0, strides = conv_28_strides_0, weight = const_119, x = concat_12)[name = string("conv_28")];
            tensor<int32, [3]> transpose_19_perm_0 = const()[name = string("transpose_19_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [512]> const_121 = const()[name = string("const_121"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76141760)))];
            tensor<fp32, [512]> const_122 = const()[name = string("const_122"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76143872)))];
            tensor<int32, [1]> layer_norm_9_axes_0 = const()[name = string("layer_norm_9_axes_0"), val = tensor<int32, [1]>([2])];
            fp32 layer_norm_9_epsilon_0 = const()[name = string("layer_norm_9_epsilon_0"), val = fp32(0x1.0c6f7ap-20)];
            tensor<fp32, [1, ?, 512]> transpose_19 = transpose(perm = transpose_19_perm_0, x = conv_28)[name = string("transpose_24")];
            tensor<fp32, [1, ?, 512]> layer_norm_9 = layer_norm(axes = layer_norm_9_axes_0, beta = const_122, epsilon = layer_norm_9_epsilon_0, gamma = const_121, x = transpose_19)[name = string("layer_norm_9")];
            tensor<int32, [3]> transpose_20_perm_0 = const()[name = string("transpose_20_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<fp32, [2048, 512, 1]> const_123 = const()[name = string("const_123"), val = tensor<fp32, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76145984)))];
            tensor<fp32, [2048]> const_124 = const()[name = string("const_124"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80340352)))];
            tensor<int32, [1]> conv_29_strides_0 = const()[name = string("conv_29_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_29_pad_type_0 = const()[name = string("conv_29_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_29_pad_0 = const()[name = string("conv_29_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_29_dilations_0 = const()[name = string("conv_29_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_29_groups_0 = const()[name = string("conv_29_groups_0"), val = int32(1)];
            tensor<fp32, [1, 512, ?]> transpose_20 = transpose(perm = transpose_20_perm_0, x = layer_norm_9)[name = string("transpose_23")];
            tensor<fp32, [1, 2048, ?]> conv_29 = conv(bias = const_124, dilations = conv_29_dilations_0, groups = conv_29_groups_0, pad = conv_29_pad_0, pad_type = conv_29_pad_type_0, strides = conv_29_strides_0, weight = const_123, x = transpose_20)[name = string("conv_29")];
            string mul_29_mode_0 = const()[name = string("mul_29_mode_0"), val = string("EXACT")];
            tensor<fp32, [1, 2048, ?]> mul_29 = gelu(mode = mul_29_mode_0, x = conv_29)[name = string("mul_29")];
            tensor<int32, [1]> conv_30_strides_0 = const()[name = string("conv_30_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_30_pad_type_0 = const()[name = string("conv_30_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_30_pad_0 = const()[name = string("conv_30_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_30_dilations_0 = const()[name = string("conv_30_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_30_groups_0 = const()[name = string("conv_30_groups_0"), val = int32(1)];
            tensor<fp32, [512, 2048, 1]> mul_30_weight_0 = const()[name = string("mul_30_weight_0"), val = tensor<fp32, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80348608)))];
            tensor<fp32, [512]> mul_30_bias_0 = const()[name = string("mul_30_bias_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84542976)))];
            tensor<fp32, [1, 512, ?]> mul_30 = conv(bias = mul_30_bias_0, dilations = conv_30_dilations_0, groups = conv_30_groups_0, pad = conv_30_pad_0, pad_type = conv_30_pad_type_0, strides = conv_30_strides_0, weight = mul_30_weight_0, x = mul_29)[name = string("mul_30")];
            tensor<fp32, [1, 512, ?]> add_20 = add(x = add_18, y = mul_30)[name = string("add_20")];
            tensor<fp32, [512]> const_131 = const()[name = string("const_131"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84545088)))];
            tensor<fp32, [512]> const_132 = const()[name = string("const_132"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84547200)))];
            tensor<fp32, [512]> const_133 = const()[name = string("const_133"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84549312)))];
            tensor<fp32, [512]> const_134 = const()[name = string("const_134"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84551424)))];
            fp32 batch_norm_0_epsilon_0 = const()[name = string("batch_norm_0_epsilon_0"), val = fp32(0x1.4f8b58p-17)];
            tensor<fp32, [1, 512, ?]> batch_norm_0 = batch_norm(beta = const_134, epsilon = batch_norm_0_epsilon_0, gamma = const_133, mean = const_131, variance = const_132, x = add_20)[name = string("batch_norm_0")];
            tensor<int32, [3]> slice_by_index_12_begin_0 = const()[name = string("slice_by_index_12_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> slice_by_index_12_end_0 = const()[name = string("slice_by_index_12_end_0"), val = tensor<int32, [3]>([0, 0, 1])];
            tensor<bool, [3]> slice_by_index_12_begin_mask_0 = const()[name = string("slice_by_index_12_begin_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<bool, [3]> slice_by_index_12_end_mask_0 = const()[name = string("slice_by_index_12_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp32, [1, 512, ?]> slice_by_index_12 = slice_by_index(begin = slice_by_index_12_begin_0, begin_mask = slice_by_index_12_begin_mask_0, end = slice_by_index_12_end_0, end_mask = slice_by_index_12_end_mask_0, x = batch_norm_0)[name = string("slice_by_index_12")];
            tensor<int32, [3]> tile_11_reps_0 = const()[name = string("tile_11_reps_0"), val = tensor<int32, [3]>([1, 1, 2])];
            tensor<fp32, [1, 512, ?]> tile_11 = tile(reps = tile_11_reps_0, x = slice_by_index_12)[name = string("tile_11")];
            int32 concat_13_axis_0 = const()[name = string("concat_13_axis_0"), val = int32(2)];
            bool concat_13_interleave_0 = const()[name = string("concat_13_interleave_0"), val = bool(false)];
            tensor<fp32, [1, 512, ?]> concat_13 = concat(axis = concat_13_axis_0, interleave = concat_13_interleave_0, values = (tile_11, batch_norm_0))[name = string("concat_13")];
            tensor<fp32, [2048, 512, 3]> const_135 = const()[name = string("const_135"), val = tensor<fp32, [2048, 512, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84553536)))];
            tensor<fp32, [2048]> const_136 = const()[name = string("const_136"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97136512)))];
            tensor<int32, [1]> conv_31_strides_0 = const()[name = string("conv_31_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_31_pad_type_0 = const()[name = string("conv_31_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_31_pad_0 = const()[name = string("conv_31_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_31_dilations_0 = const()[name = string("conv_31_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_31_groups_0 = const()[name = string("conv_31_groups_0"), val = int32(1)];
            tensor<fp32, [1, 2048, ?]> conv_31 = conv(bias = const_136, dilations = conv_31_dilations_0, groups = conv_31_groups_0, pad = conv_31_pad_0, pad_type = conv_31_pad_type_0, strides = conv_31_strides_0, weight = const_135, x = concat_13)[name = string("conv_31")];
            tensor<fp32, [1, 2048, ?]> relu_0 = relu(x = conv_31)[name = string("relu_0")];
            fp32 minimum_0_y_0 = const()[name = string("minimum_0_y_0"), val = fp32(0x0p+0)];
            tensor<fp32, [1, 2048, ?]> minimum_0 = minimum(x = conv_31, y = minimum_0_y_0)[name = string("minimum_0")];
            tensor<fp32, [1, 1]> const_137 = const()[name = string("const_137"), val = tensor<fp32, [1, 1]>([[0x1.9814fap-13]])];
            tensor<fp32, [1, 2048, ?]> mul_31 = mul(x = minimum_0, y = const_137)[name = string("mul_31")];
            tensor<fp32, [1, 2048, ?]> add_21 = add(x = relu_0, y = mul_31)[name = string("add_21")];
            tensor<fp32, [512, 2048, 1]> const_138 = const()[name = string("const_138"), val = tensor<fp32, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(97144768)))];
            tensor<int32, [1]> conv_32_strides_0 = const()[name = string("conv_32_strides_0"), val = tensor<int32, [1]>([1])];
            string conv_32_pad_type_0 = const()[name = string("conv_32_pad_type_0"), val = string("custom")];
            tensor<int32, [2]> conv_32_pad_0 = const()[name = string("conv_32_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> conv_32_dilations_0 = const()[name = string("conv_32_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 conv_32_groups_0 = const()[name = string("conv_32_groups_0"), val = int32(1)];
            tensor<fp32, [1, 512, ?]> conv_32 = conv(dilations = conv_32_dilations_0, groups = conv_32_groups_0, pad = conv_32_pad_0, pad_type = conv_32_pad_type_0, strides = conv_32_strides_0, weight = const_138, x = add_21)[name = string("conv_32")];
            tensor<int32, [3]> transpose_21_perm_0 = const()[name = string("transpose_21_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<int32, [2]> concat_14 = const()[name = string("concat_14"), val = tensor<int32, [2]>([1, -1])];
            tensor<fp32, [1, ?, 512]> transpose_21 = transpose(perm = transpose_21_perm_0, x = conv_32)[name = string("transpose_22")];
            tensor<fp32, [1, ?]> reshape_2 = reshape(shape = concat_14, x = transpose_21)[name = string("reshape_2")];
            tensor<fp32, [1, ?]> wav = identity(x = reshape_2)[name = string("wav")];
        } -> (wav);
}