program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3405.2.1"}, {"coremlc-version", "3405.2.1"}, {"coremltools-component-milinternal", ""}, {"coremltools-version", "9.0"}})]
{
    func main<ios18>(tensor<fp16, [1, 64, 6]> cache_0, tensor<fp16, [1, 2048, 63]> cache_1, tensor<fp16, [1, 1024, 27]> cache_2, tensor<fp16, [1, 512, 27]> cache_3, tensor<fp16, [1, 256, 25]> cache_4, tensor<fp16, [1, 128, 21]> cache_5, tensor<fp16, [1, 64, 21]> cache_6, tensor<fp16, [1, 32, 24]> cache_7, tensor<fp16, [1, 64, 12]> input_x) {
            tensor<int32, [9]> var_730 = const()[name = string("op_730"), val = tensor<int32, [9]>([6, 6, 6, 6, 6, 6, 6, 6, 15])];
            int32 var_732_axis_0 = const()[name = string("op_732_axis_0"), val = int32(-1)];
            tensor<fp16, [1, 2048, 6]> var_732_cast_fp16_0, tensor<fp16, [1, 2048, 6]> var_732_cast_fp16_1, tensor<fp16, [1, 2048, 6]> var_732_cast_fp16_2, tensor<fp16, [1, 2048, 6]> var_732_cast_fp16_3, tensor<fp16, [1, 2048, 6]> var_732_cast_fp16_4, tensor<fp16, [1, 2048, 6]> var_732_cast_fp16_5, tensor<fp16, [1, 2048, 6]> var_732_cast_fp16_6, tensor<fp16, [1, 2048, 6]> var_732_cast_fp16_7, tensor<fp16, [1, 2048, 15]> var_732_cast_fp16_8 = split(axis = var_732_axis_0, split_sizes = var_730, x = cache_1)[name = string("op_732_cast_fp16")];
            tensor<int32, [4]> var_746 = const()[name = string("op_746"), val = tensor<int32, [4]>([6, 6, 6, 9])];
            int32 var_748_axis_0 = const()[name = string("op_748_axis_0"), val = int32(-1)];
            tensor<fp16, [1, 1024, 6]> var_748_cast_fp16_0, tensor<fp16, [1, 1024, 6]> var_748_cast_fp16_1, tensor<fp16, [1, 1024, 6]> var_748_cast_fp16_2, tensor<fp16, [1, 1024, 9]> var_748_cast_fp16_3 = split(axis = var_748_axis_0, split_sizes = var_746, x = cache_2)[name = string("op_748_cast_fp16")];
            tensor<int32, [4]> var_757 = const()[name = string("op_757"), val = tensor<int32, [4]>([6, 6, 6, 9])];
            int32 var_759_axis_0 = const()[name = string("op_759_axis_0"), val = int32(-1)];
            tensor<fp16, [1, 512, 6]> var_759_cast_fp16_0, tensor<fp16, [1, 512, 6]> var_759_cast_fp16_1, tensor<fp16, [1, 512, 6]> var_759_cast_fp16_2, tensor<fp16, [1, 512, 9]> var_759_cast_fp16_3 = split(axis = var_759_axis_0, split_sizes = var_757, x = cache_3)[name = string("op_759_cast_fp16")];
            tensor<int32, [4]> var_768 = const()[name = string("op_768"), val = tensor<int32, [4]>([6, 6, 6, 7])];
            int32 var_770_axis_0 = const()[name = string("op_770_axis_0"), val = int32(-1)];
            tensor<fp16, [1, 256, 6]> var_770_cast_fp16_0, tensor<fp16, [1, 256, 6]> var_770_cast_fp16_1, tensor<fp16, [1, 256, 6]> var_770_cast_fp16_2, tensor<fp16, [1, 256, 7]> var_770_cast_fp16_3 = split(axis = var_770_axis_0, split_sizes = var_768, x = cache_4)[name = string("op_770_cast_fp16")];
            tensor<int32, [4]> var_779 = const()[name = string("op_779"), val = tensor<int32, [4]>([6, 6, 6, 3])];
            int32 var_781_axis_0 = const()[name = string("op_781_axis_0"), val = int32(-1)];
            tensor<fp16, [1, 128, 6]> var_781_cast_fp16_0, tensor<fp16, [1, 128, 6]> var_781_cast_fp16_1, tensor<fp16, [1, 128, 6]> var_781_cast_fp16_2, tensor<fp16, [1, 128, 3]> var_781_cast_fp16_3 = split(axis = var_781_axis_0, split_sizes = var_779, x = cache_5)[name = string("op_781_cast_fp16")];
            tensor<int32, [4]> var_790 = const()[name = string("op_790"), val = tensor<int32, [4]>([6, 6, 6, 3])];
            int32 var_792_axis_0 = const()[name = string("op_792_axis_0"), val = int32(-1)];
            tensor<fp16, [1, 64, 6]> var_792_cast_fp16_0, tensor<fp16, [1, 64, 6]> var_792_cast_fp16_1, tensor<fp16, [1, 64, 6]> var_792_cast_fp16_2, tensor<fp16, [1, 64, 3]> var_792_cast_fp16_3 = split(axis = var_792_axis_0, split_sizes = var_790, x = cache_6)[name = string("op_792_cast_fp16")];
            tensor<int32, [4]> var_801 = const()[name = string("op_801"), val = tensor<int32, [4]>([6, 6, 6, 6])];
            int32 var_803_axis_0 = const()[name = string("op_803_axis_0"), val = int32(-1)];
            tensor<fp16, [1, 32, 6]> var_803_cast_fp16_0, tensor<fp16, [1, 32, 6]> var_803_cast_fp16_1, tensor<fp16, [1, 32, 6]> var_803_cast_fp16_2, tensor<fp16, [1, 32, 6]> var_803_cast_fp16_3 = split(axis = var_803_axis_0, split_sizes = var_801, x = cache_7)[name = string("op_803_cast_fp16")];
            int32 var_809 = const()[name = string("op_809"), val = int32(-1)];
            bool input_1_interleave_0 = const()[name = string("input_1_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 64, 18]> input_1_cast_fp16 = concat(axis = var_809, interleave = input_1_interleave_0, values = (cache_0, input_x))[name = string("input_1_cast_fp16")];
            string x_3_pad_type_0 = const()[name = string("x_3_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_3_strides_0 = const()[name = string("x_3_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_3_pad_0 = const()[name = string("x_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_3_dilations_0 = const()[name = string("x_3_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_3_groups_0 = const()[name = string("x_3_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 64, 7]> upsample_layers_0_0_conv_conv_weight_to_fp16 = const()[name = string("upsample_layers_0_0_conv_conv_weight_to_fp16"), val = tensor<fp16, [2048, 64, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
            tensor<fp16, [2048]> upsample_layers_0_0_conv_conv_bias_to_fp16 = const()[name = string("upsample_layers_0_0_conv_conv_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1835136)))];
            tensor<fp16, [1, 2048, 12]> x_3_cast_fp16 = conv(bias = upsample_layers_0_0_conv_conv_bias_to_fp16, dilations = x_3_dilations_0, groups = x_3_groups_0, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = x_3_strides_0, weight = upsample_layers_0_0_conv_conv_weight_to_fp16, x = input_1_cast_fp16)[name = string("x_3_cast_fp16")];
            tensor<int32, [3]> var_831_begin_0 = const()[name = string("op_831_begin_0"), val = tensor<int32, [3]>([0, 0, 12])];
            tensor<int32, [3]> var_831_end_0 = const()[name = string("op_831_end_0"), val = tensor<int32, [3]>([1, 64, 18])];
            tensor<bool, [3]> var_831_end_mask_0 = const()[name = string("op_831_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 64, 6]> var_831_cast_fp16 = slice_by_index(begin = var_831_begin_0, end = var_831_end_0, end_mask = var_831_end_mask_0, x = input_1_cast_fp16)[name = string("op_831_cast_fp16")];
            int32 var_836 = const()[name = string("op_836"), val = int32(1)];
            tensor<int32, [1]> x_5_axes_0 = const()[name = string("x_5_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_5_cast_fp16 = expand_dims(axes = x_5_axes_0, x = x_3_cast_fp16)[name = string("x_5_cast_fp16")];
            fp16 const_2_promoted_to_fp16 = const()[name = string("const_2_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_841_cast_fp16 = mul(x = x_5_cast_fp16, y = const_2_promoted_to_fp16)[name = string("op_841_cast_fp16")];
            bool x_7_interleave_0 = const()[name = string("x_7_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_7_cast_fp16 = concat(axis = var_836, interleave = x_7_interleave_0, values = (x_5_cast_fp16, var_841_cast_fp16))[name = string("x_7_cast_fp16")];
            tensor<int32, [1]> out_1_axes_0 = const()[name = string("out_1_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_851_to_fp16 = const()[name = string("op_851_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_1_cast_fp16 = layer_norm(axes = out_1_axes_0, epsilon = var_851_to_fp16, x = x_7_cast_fp16)[name = string("out_1_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_0_norm_weight_to_fp16 = const()[name = string("stages_0_0_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1839296)))];
            tensor<fp16, [1, 4096, 1, 12]> out_3_cast_fp16 = mul(x = out_1_cast_fp16, y = stages_0_0_norm_weight_to_fp16)[name = string("out_3_cast_fp16")];
            tensor<int32, [2]> var_857_split_sizes_0 = const()[name = string("op_857_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_857_axis_0 = const()[name = string("op_857_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_857_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_857_cast_fp16_1 = split(axis = var_857_axis_0, split_sizes = var_857_split_sizes_0, x = out_3_cast_fp16)[name = string("op_857_cast_fp16")];
            tensor<int32, [1]> x_11_axes_0 = const()[name = string("x_11_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_11_cast_fp16 = squeeze(axes = x_11_axes_0, x = var_857_cast_fp16_0)[name = string("x_11_cast_fp16")];
            int32 var_862 = const()[name = string("op_862"), val = int32(-1)];
            bool input_3_interleave_0 = const()[name = string("input_3_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 18]> input_3_cast_fp16 = concat(axis = var_862, interleave = input_3_interleave_0, values = (var_732_cast_fp16_0, x_11_cast_fp16))[name = string("input_3_cast_fp16")];
            string x_13_pad_type_0 = const()[name = string("x_13_pad_type_0"), val = string("valid")];
            int32 x_13_groups_0 = const()[name = string("x_13_groups_0"), val = int32(2048)];
            tensor<int32, [1]> x_13_strides_0 = const()[name = string("x_13_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_13_pad_0 = const()[name = string("x_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_13_dilations_0 = const()[name = string("x_13_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2048, 1, 7]> x_15_weight_0_to_fp16 = const()[name = string("x_15_weight_0_to_fp16"), val = tensor<fp16, [2048, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1847552)))];
            tensor<fp16, [2048]> x_15_bias_0_to_fp16 = const()[name = string("x_15_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1876288)))];
            tensor<fp16, [1, 2048, 12]> x_15_cast_fp16 = conv(bias = x_15_bias_0_to_fp16, dilations = x_13_dilations_0, groups = x_13_groups_0, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = x_13_strides_0, weight = x_15_weight_0_to_fp16, x = input_3_cast_fp16)[name = string("x_15_cast_fp16")];
            tensor<int32, [3]> var_885_begin_0 = const()[name = string("op_885_begin_0"), val = tensor<int32, [3]>([0, 0, 12])];
            tensor<int32, [3]> var_885_end_0 = const()[name = string("op_885_end_0"), val = tensor<int32, [3]>([1, 2048, 18])];
            tensor<bool, [3]> var_885_end_mask_0 = const()[name = string("op_885_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 2048, 6]> var_885_cast_fp16 = slice_by_index(begin = var_885_begin_0, end = var_885_end_0, end_mask = var_885_end_mask_0, x = input_3_cast_fp16)[name = string("op_885_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_17_cast_fp16 = add(x = x_3_cast_fp16, y = x_15_cast_fp16)[name = string("x_17_cast_fp16")];
            int32 var_895 = const()[name = string("op_895"), val = int32(1)];
            tensor<int32, [1]> x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_19_cast_fp16 = expand_dims(axes = x_19_axes_0, x = x_17_cast_fp16)[name = string("x_19_cast_fp16")];
            fp16 const_5_promoted_to_fp16 = const()[name = string("const_5_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_900_cast_fp16 = mul(x = x_19_cast_fp16, y = const_5_promoted_to_fp16)[name = string("op_900_cast_fp16")];
            bool x_21_interleave_0 = const()[name = string("x_21_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_21_cast_fp16 = concat(axis = var_895, interleave = x_21_interleave_0, values = (x_19_cast_fp16, var_900_cast_fp16))[name = string("x_21_cast_fp16")];
            tensor<int32, [1]> out_9_axes_0 = const()[name = string("out_9_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_910_to_fp16 = const()[name = string("op_910_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_9_cast_fp16 = layer_norm(axes = out_9_axes_0, epsilon = var_910_to_fp16, x = x_21_cast_fp16)[name = string("out_9_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_0_ffn_norm_weight_to_fp16 = const()[name = string("stages_0_0_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1880448)))];
            tensor<fp16, [1, 4096, 1, 12]> out_11_cast_fp16 = mul(x = out_9_cast_fp16, y = stages_0_0_ffn_norm_weight_to_fp16)[name = string("out_11_cast_fp16")];
            tensor<int32, [2]> var_916_split_sizes_0 = const()[name = string("op_916_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_916_axis_0 = const()[name = string("op_916_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_916_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_916_cast_fp16_1 = split(axis = var_916_axis_0, split_sizes = var_916_split_sizes_0, x = out_11_cast_fp16)[name = string("op_916_cast_fp16")];
            tensor<int32, [1]> x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_25_cast_fp16 = squeeze(axes = x_25_axes_0, x = var_916_cast_fp16_0)[name = string("x_25_cast_fp16")];
            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)];
            tensor<fp16, [8192, 2048, 1]> var_922_to_fp16 = const()[name = string("op_922_to_fp16"), val = tensor<fp16, [8192, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1888704)))];
            tensor<fp16, [8192]> stages_0_0_ffn_linear1_bias_to_fp16 = const()[name = string("stages_0_0_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [8192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35443200)))];
            tensor<fp16, [1, 8192, 12]> input_5_cast_fp16 = conv(bias = stages_0_0_ffn_linear1_bias_to_fp16, dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = var_922_to_fp16, x = x_25_cast_fp16)[name = string("input_5_cast_fp16")];
            string x_27_mode_0 = const()[name = string("x_27_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 8192, 12]> x_27_cast_fp16 = gelu(mode = x_27_mode_0, x = input_5_cast_fp16)[name = string("x_27_cast_fp16")];
            string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 8192, 1]> x_31_weight_0_to_fp16 = const()[name = string("x_31_weight_0_to_fp16"), val = tensor<fp16, [2048, 8192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35459648)))];
            tensor<fp16, [2048]> x_31_bias_0_to_fp16 = const()[name = string("x_31_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69014144)))];
            tensor<fp16, [1, 2048, 12]> x_31_cast_fp16 = conv(bias = x_31_bias_0_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = x_31_weight_0_to_fp16, x = x_27_cast_fp16)[name = string("x_31_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_33_cast_fp16 = add(x = x_17_cast_fp16, y = x_31_cast_fp16)[name = string("x_33_cast_fp16")];
            int32 var_951 = const()[name = string("op_951"), val = int32(1)];
            tensor<int32, [1]> x_35_axes_0 = const()[name = string("x_35_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_35_cast_fp16 = expand_dims(axes = x_35_axes_0, x = x_33_cast_fp16)[name = string("x_35_cast_fp16")];
            fp16 const_6_promoted_to_fp16 = const()[name = string("const_6_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_956_cast_fp16 = mul(x = x_35_cast_fp16, y = const_6_promoted_to_fp16)[name = string("op_956_cast_fp16")];
            bool x_37_interleave_0 = const()[name = string("x_37_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_37_cast_fp16 = concat(axis = var_951, interleave = x_37_interleave_0, values = (x_35_cast_fp16, var_956_cast_fp16))[name = string("x_37_cast_fp16")];
            tensor<int32, [1]> out_17_axes_0 = const()[name = string("out_17_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_966_to_fp16 = const()[name = string("op_966_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_17_cast_fp16 = layer_norm(axes = out_17_axes_0, epsilon = var_966_to_fp16, x = x_37_cast_fp16)[name = string("out_17_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_1_norm_weight_to_fp16 = const()[name = string("stages_0_1_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69018304)))];
            tensor<fp16, [1, 4096, 1, 12]> out_19_cast_fp16 = mul(x = out_17_cast_fp16, y = stages_0_1_norm_weight_to_fp16)[name = string("out_19_cast_fp16")];
            tensor<int32, [2]> var_972_split_sizes_0 = const()[name = string("op_972_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_972_axis_0 = const()[name = string("op_972_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_972_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_972_cast_fp16_1 = split(axis = var_972_axis_0, split_sizes = var_972_split_sizes_0, x = out_19_cast_fp16)[name = string("op_972_cast_fp16")];
            tensor<int32, [1]> x_41_axes_0 = const()[name = string("x_41_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_41_cast_fp16 = squeeze(axes = x_41_axes_0, x = var_972_cast_fp16_0)[name = string("x_41_cast_fp16")];
            int32 var_977 = const()[name = string("op_977"), val = int32(-1)];
            bool input_7_interleave_0 = const()[name = string("input_7_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 18]> input_7_cast_fp16 = concat(axis = var_977, interleave = input_7_interleave_0, values = (var_732_cast_fp16_1, x_41_cast_fp16))[name = string("input_7_cast_fp16")];
            string x_43_pad_type_0 = const()[name = string("x_43_pad_type_0"), val = string("valid")];
            int32 x_43_groups_0 = const()[name = string("x_43_groups_0"), val = int32(2048)];
            tensor<int32, [1]> x_43_strides_0 = const()[name = string("x_43_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_43_pad_0 = const()[name = string("x_43_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_43_dilations_0 = const()[name = string("x_43_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2048, 1, 7]> x_45_weight_0_to_fp16 = const()[name = string("x_45_weight_0_to_fp16"), val = tensor<fp16, [2048, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69026560)))];
            tensor<fp16, [2048]> x_45_bias_0_to_fp16 = const()[name = string("x_45_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69055296)))];
            tensor<fp16, [1, 2048, 12]> x_45_cast_fp16 = conv(bias = x_45_bias_0_to_fp16, dilations = x_43_dilations_0, groups = x_43_groups_0, pad = x_43_pad_0, pad_type = x_43_pad_type_0, strides = x_43_strides_0, weight = x_45_weight_0_to_fp16, x = input_7_cast_fp16)[name = string("x_45_cast_fp16")];
            tensor<int32, [3]> var_1000_begin_0 = const()[name = string("op_1000_begin_0"), val = tensor<int32, [3]>([0, 0, 12])];
            tensor<int32, [3]> var_1000_end_0 = const()[name = string("op_1000_end_0"), val = tensor<int32, [3]>([1, 2048, 18])];
            tensor<bool, [3]> var_1000_end_mask_0 = const()[name = string("op_1000_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 2048, 6]> var_1000_cast_fp16 = slice_by_index(begin = var_1000_begin_0, end = var_1000_end_0, end_mask = var_1000_end_mask_0, x = input_7_cast_fp16)[name = string("op_1000_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_47_cast_fp16 = add(x = x_33_cast_fp16, y = x_45_cast_fp16)[name = string("x_47_cast_fp16")];
            int32 var_1010 = const()[name = string("op_1010"), val = int32(1)];
            tensor<int32, [1]> x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_49_cast_fp16 = expand_dims(axes = x_49_axes_0, x = x_47_cast_fp16)[name = string("x_49_cast_fp16")];
            fp16 const_9_promoted_to_fp16 = const()[name = string("const_9_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_1015_cast_fp16 = mul(x = x_49_cast_fp16, y = const_9_promoted_to_fp16)[name = string("op_1015_cast_fp16")];
            bool x_51_interleave_0 = const()[name = string("x_51_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_51_cast_fp16 = concat(axis = var_1010, interleave = x_51_interleave_0, values = (x_49_cast_fp16, var_1015_cast_fp16))[name = string("x_51_cast_fp16")];
            tensor<int32, [1]> out_25_axes_0 = const()[name = string("out_25_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1025_to_fp16 = const()[name = string("op_1025_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_25_cast_fp16 = layer_norm(axes = out_25_axes_0, epsilon = var_1025_to_fp16, x = x_51_cast_fp16)[name = string("out_25_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_1_ffn_norm_weight_to_fp16 = const()[name = string("stages_0_1_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69059456)))];
            tensor<fp16, [1, 4096, 1, 12]> out_27_cast_fp16 = mul(x = out_25_cast_fp16, y = stages_0_1_ffn_norm_weight_to_fp16)[name = string("out_27_cast_fp16")];
            tensor<int32, [2]> var_1031_split_sizes_0 = const()[name = string("op_1031_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1031_axis_0 = const()[name = string("op_1031_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_1031_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_1031_cast_fp16_1 = split(axis = var_1031_axis_0, split_sizes = var_1031_split_sizes_0, x = out_27_cast_fp16)[name = string("op_1031_cast_fp16")];
            tensor<int32, [1]> x_55_axes_0 = const()[name = string("x_55_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_55_cast_fp16 = squeeze(axes = x_55_axes_0, x = var_1031_cast_fp16_0)[name = string("x_55_cast_fp16")];
            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
            tensor<fp16, [8192, 2048, 1]> var_1037_to_fp16 = const()[name = string("op_1037_to_fp16"), val = tensor<fp16, [8192, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69067712)))];
            tensor<fp16, [8192]> stages_0_1_ffn_linear1_bias_to_fp16 = const()[name = string("stages_0_1_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [8192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102622208)))];
            tensor<fp16, [1, 8192, 12]> input_9_cast_fp16 = conv(bias = stages_0_1_ffn_linear1_bias_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = var_1037_to_fp16, x = x_55_cast_fp16)[name = string("input_9_cast_fp16")];
            string x_57_mode_0 = const()[name = string("x_57_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 8192, 12]> x_57_cast_fp16 = gelu(mode = x_57_mode_0, x = input_9_cast_fp16)[name = string("x_57_cast_fp16")];
            string x_59_pad_type_0 = const()[name = string("x_59_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_59_strides_0 = const()[name = string("x_59_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_59_pad_0 = const()[name = string("x_59_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_59_dilations_0 = const()[name = string("x_59_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_59_groups_0 = const()[name = string("x_59_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 8192, 1]> x_61_weight_0_to_fp16 = const()[name = string("x_61_weight_0_to_fp16"), val = tensor<fp16, [2048, 8192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(102638656)))];
            tensor<fp16, [2048]> x_61_bias_0_to_fp16 = const()[name = string("x_61_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136193152)))];
            tensor<fp16, [1, 2048, 12]> x_61_cast_fp16 = conv(bias = x_61_bias_0_to_fp16, dilations = x_59_dilations_0, groups = x_59_groups_0, pad = x_59_pad_0, pad_type = x_59_pad_type_0, strides = x_59_strides_0, weight = x_61_weight_0_to_fp16, x = x_57_cast_fp16)[name = string("x_61_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_63_cast_fp16 = add(x = x_47_cast_fp16, y = x_61_cast_fp16)[name = string("x_63_cast_fp16")];
            int32 var_1066 = const()[name = string("op_1066"), val = int32(1)];
            tensor<int32, [1]> x_65_axes_0 = const()[name = string("x_65_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_65_cast_fp16 = expand_dims(axes = x_65_axes_0, x = x_63_cast_fp16)[name = string("x_65_cast_fp16")];
            fp16 const_10_promoted_to_fp16 = const()[name = string("const_10_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_1071_cast_fp16 = mul(x = x_65_cast_fp16, y = const_10_promoted_to_fp16)[name = string("op_1071_cast_fp16")];
            bool x_67_interleave_0 = const()[name = string("x_67_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_67_cast_fp16 = concat(axis = var_1066, interleave = x_67_interleave_0, values = (x_65_cast_fp16, var_1071_cast_fp16))[name = string("x_67_cast_fp16")];
            tensor<int32, [1]> out_33_axes_0 = const()[name = string("out_33_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1081_to_fp16 = const()[name = string("op_1081_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_33_cast_fp16 = layer_norm(axes = out_33_axes_0, epsilon = var_1081_to_fp16, x = x_67_cast_fp16)[name = string("out_33_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_2_norm_weight_to_fp16 = const()[name = string("stages_0_2_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136197312)))];
            tensor<fp16, [1, 4096, 1, 12]> out_35_cast_fp16 = mul(x = out_33_cast_fp16, y = stages_0_2_norm_weight_to_fp16)[name = string("out_35_cast_fp16")];
            tensor<int32, [2]> var_1087_split_sizes_0 = const()[name = string("op_1087_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1087_axis_0 = const()[name = string("op_1087_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_1087_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_1087_cast_fp16_1 = split(axis = var_1087_axis_0, split_sizes = var_1087_split_sizes_0, x = out_35_cast_fp16)[name = string("op_1087_cast_fp16")];
            tensor<int32, [1]> x_71_axes_0 = const()[name = string("x_71_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_71_cast_fp16 = squeeze(axes = x_71_axes_0, x = var_1087_cast_fp16_0)[name = string("x_71_cast_fp16")];
            int32 var_1092 = const()[name = string("op_1092"), val = int32(-1)];
            bool input_11_interleave_0 = const()[name = string("input_11_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 18]> input_11_cast_fp16 = concat(axis = var_1092, interleave = input_11_interleave_0, values = (var_732_cast_fp16_2, x_71_cast_fp16))[name = string("input_11_cast_fp16")];
            string x_73_pad_type_0 = const()[name = string("x_73_pad_type_0"), val = string("valid")];
            int32 x_73_groups_0 = const()[name = string("x_73_groups_0"), val = int32(2048)];
            tensor<int32, [1]> x_73_strides_0 = const()[name = string("x_73_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_73_pad_0 = const()[name = string("x_73_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_73_dilations_0 = const()[name = string("x_73_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2048, 1, 7]> x_75_weight_0_to_fp16 = const()[name = string("x_75_weight_0_to_fp16"), val = tensor<fp16, [2048, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136205568)))];
            tensor<fp16, [2048]> x_75_bias_0_to_fp16 = const()[name = string("x_75_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136234304)))];
            tensor<fp16, [1, 2048, 12]> x_75_cast_fp16 = conv(bias = x_75_bias_0_to_fp16, dilations = x_73_dilations_0, groups = x_73_groups_0, pad = x_73_pad_0, pad_type = x_73_pad_type_0, strides = x_73_strides_0, weight = x_75_weight_0_to_fp16, x = input_11_cast_fp16)[name = string("x_75_cast_fp16")];
            tensor<int32, [3]> var_1115_begin_0 = const()[name = string("op_1115_begin_0"), val = tensor<int32, [3]>([0, 0, 12])];
            tensor<int32, [3]> var_1115_end_0 = const()[name = string("op_1115_end_0"), val = tensor<int32, [3]>([1, 2048, 18])];
            tensor<bool, [3]> var_1115_end_mask_0 = const()[name = string("op_1115_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 2048, 6]> var_1115_cast_fp16 = slice_by_index(begin = var_1115_begin_0, end = var_1115_end_0, end_mask = var_1115_end_mask_0, x = input_11_cast_fp16)[name = string("op_1115_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_77_cast_fp16 = add(x = x_63_cast_fp16, y = x_75_cast_fp16)[name = string("x_77_cast_fp16")];
            int32 var_1125 = const()[name = string("op_1125"), val = int32(1)];
            tensor<int32, [1]> x_79_axes_0 = const()[name = string("x_79_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_79_cast_fp16 = expand_dims(axes = x_79_axes_0, x = x_77_cast_fp16)[name = string("x_79_cast_fp16")];
            fp16 const_13_promoted_to_fp16 = const()[name = string("const_13_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_1130_cast_fp16 = mul(x = x_79_cast_fp16, y = const_13_promoted_to_fp16)[name = string("op_1130_cast_fp16")];
            bool x_81_interleave_0 = const()[name = string("x_81_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_81_cast_fp16 = concat(axis = var_1125, interleave = x_81_interleave_0, values = (x_79_cast_fp16, var_1130_cast_fp16))[name = string("x_81_cast_fp16")];
            tensor<int32, [1]> out_41_axes_0 = const()[name = string("out_41_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1140_to_fp16 = const()[name = string("op_1140_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_41_cast_fp16 = layer_norm(axes = out_41_axes_0, epsilon = var_1140_to_fp16, x = x_81_cast_fp16)[name = string("out_41_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_2_ffn_norm_weight_to_fp16 = const()[name = string("stages_0_2_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136238464)))];
            tensor<fp16, [1, 4096, 1, 12]> out_43_cast_fp16 = mul(x = out_41_cast_fp16, y = stages_0_2_ffn_norm_weight_to_fp16)[name = string("out_43_cast_fp16")];
            tensor<int32, [2]> var_1146_split_sizes_0 = const()[name = string("op_1146_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1146_axis_0 = const()[name = string("op_1146_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_1146_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_1146_cast_fp16_1 = split(axis = var_1146_axis_0, split_sizes = var_1146_split_sizes_0, x = out_43_cast_fp16)[name = string("op_1146_cast_fp16")];
            tensor<int32, [1]> x_85_axes_0 = const()[name = string("x_85_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_85_cast_fp16 = squeeze(axes = x_85_axes_0, x = var_1146_cast_fp16_0)[name = string("x_85_cast_fp16")];
            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
            tensor<fp16, [8192, 2048, 1]> var_1152_to_fp16 = const()[name = string("op_1152_to_fp16"), val = tensor<fp16, [8192, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136246720)))];
            tensor<fp16, [8192]> stages_0_2_ffn_linear1_bias_to_fp16 = const()[name = string("stages_0_2_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [8192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169801216)))];
            tensor<fp16, [1, 8192, 12]> input_13_cast_fp16 = conv(bias = stages_0_2_ffn_linear1_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = var_1152_to_fp16, x = x_85_cast_fp16)[name = string("input_13_cast_fp16")];
            string x_87_mode_0 = const()[name = string("x_87_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 8192, 12]> x_87_cast_fp16 = gelu(mode = x_87_mode_0, x = input_13_cast_fp16)[name = string("x_87_cast_fp16")];
            string x_89_pad_type_0 = const()[name = string("x_89_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_89_strides_0 = const()[name = string("x_89_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_89_pad_0 = const()[name = string("x_89_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_89_dilations_0 = const()[name = string("x_89_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_89_groups_0 = const()[name = string("x_89_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 8192, 1]> x_91_weight_0_to_fp16 = const()[name = string("x_91_weight_0_to_fp16"), val = tensor<fp16, [2048, 8192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(169817664)))];
            tensor<fp16, [2048]> x_91_bias_0_to_fp16 = const()[name = string("x_91_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203372160)))];
            tensor<fp16, [1, 2048, 12]> x_91_cast_fp16 = conv(bias = x_91_bias_0_to_fp16, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = x_91_weight_0_to_fp16, x = x_87_cast_fp16)[name = string("x_91_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_93_cast_fp16 = add(x = x_77_cast_fp16, y = x_91_cast_fp16)[name = string("x_93_cast_fp16")];
            int32 var_1181 = const()[name = string("op_1181"), val = int32(1)];
            tensor<int32, [1]> x_95_axes_0 = const()[name = string("x_95_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_95_cast_fp16 = expand_dims(axes = x_95_axes_0, x = x_93_cast_fp16)[name = string("x_95_cast_fp16")];
            fp16 const_14_promoted_to_fp16 = const()[name = string("const_14_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_1186_cast_fp16 = mul(x = x_95_cast_fp16, y = const_14_promoted_to_fp16)[name = string("op_1186_cast_fp16")];
            bool x_97_interleave_0 = const()[name = string("x_97_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_97_cast_fp16 = concat(axis = var_1181, interleave = x_97_interleave_0, values = (x_95_cast_fp16, var_1186_cast_fp16))[name = string("x_97_cast_fp16")];
            tensor<int32, [1]> out_49_axes_0 = const()[name = string("out_49_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1196_to_fp16 = const()[name = string("op_1196_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_49_cast_fp16 = layer_norm(axes = out_49_axes_0, epsilon = var_1196_to_fp16, x = x_97_cast_fp16)[name = string("out_49_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_3_norm_weight_to_fp16 = const()[name = string("stages_0_3_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203376320)))];
            tensor<fp16, [1, 4096, 1, 12]> out_51_cast_fp16 = mul(x = out_49_cast_fp16, y = stages_0_3_norm_weight_to_fp16)[name = string("out_51_cast_fp16")];
            tensor<int32, [2]> var_1202_split_sizes_0 = const()[name = string("op_1202_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1202_axis_0 = const()[name = string("op_1202_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_1202_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_1202_cast_fp16_1 = split(axis = var_1202_axis_0, split_sizes = var_1202_split_sizes_0, x = out_51_cast_fp16)[name = string("op_1202_cast_fp16")];
            tensor<int32, [1]> x_101_axes_0 = const()[name = string("x_101_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_101_cast_fp16 = squeeze(axes = x_101_axes_0, x = var_1202_cast_fp16_0)[name = string("x_101_cast_fp16")];
            int32 var_1207 = const()[name = string("op_1207"), val = int32(-1)];
            bool input_15_interleave_0 = const()[name = string("input_15_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 18]> input_15_cast_fp16 = concat(axis = var_1207, interleave = input_15_interleave_0, values = (var_732_cast_fp16_3, x_101_cast_fp16))[name = string("input_15_cast_fp16")];
            string x_103_pad_type_0 = const()[name = string("x_103_pad_type_0"), val = string("valid")];
            int32 x_103_groups_0 = const()[name = string("x_103_groups_0"), val = int32(2048)];
            tensor<int32, [1]> x_103_strides_0 = const()[name = string("x_103_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_103_pad_0 = const()[name = string("x_103_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_103_dilations_0 = const()[name = string("x_103_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2048, 1, 7]> x_105_weight_0_to_fp16 = const()[name = string("x_105_weight_0_to_fp16"), val = tensor<fp16, [2048, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203384576)))];
            tensor<fp16, [2048]> x_105_bias_0_to_fp16 = const()[name = string("x_105_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203413312)))];
            tensor<fp16, [1, 2048, 12]> x_105_cast_fp16 = conv(bias = x_105_bias_0_to_fp16, dilations = x_103_dilations_0, groups = x_103_groups_0, pad = x_103_pad_0, pad_type = x_103_pad_type_0, strides = x_103_strides_0, weight = x_105_weight_0_to_fp16, x = input_15_cast_fp16)[name = string("x_105_cast_fp16")];
            tensor<int32, [3]> var_1230_begin_0 = const()[name = string("op_1230_begin_0"), val = tensor<int32, [3]>([0, 0, 12])];
            tensor<int32, [3]> var_1230_end_0 = const()[name = string("op_1230_end_0"), val = tensor<int32, [3]>([1, 2048, 18])];
            tensor<bool, [3]> var_1230_end_mask_0 = const()[name = string("op_1230_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 2048, 6]> var_1230_cast_fp16 = slice_by_index(begin = var_1230_begin_0, end = var_1230_end_0, end_mask = var_1230_end_mask_0, x = input_15_cast_fp16)[name = string("op_1230_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_107_cast_fp16 = add(x = x_93_cast_fp16, y = x_105_cast_fp16)[name = string("x_107_cast_fp16")];
            int32 var_1240 = const()[name = string("op_1240"), val = int32(1)];
            tensor<int32, [1]> x_109_axes_0 = const()[name = string("x_109_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_109_cast_fp16 = expand_dims(axes = x_109_axes_0, x = x_107_cast_fp16)[name = string("x_109_cast_fp16")];
            fp16 const_17_promoted_to_fp16 = const()[name = string("const_17_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_1245_cast_fp16 = mul(x = x_109_cast_fp16, y = const_17_promoted_to_fp16)[name = string("op_1245_cast_fp16")];
            bool x_111_interleave_0 = const()[name = string("x_111_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_111_cast_fp16 = concat(axis = var_1240, interleave = x_111_interleave_0, values = (x_109_cast_fp16, var_1245_cast_fp16))[name = string("x_111_cast_fp16")];
            tensor<int32, [1]> out_57_axes_0 = const()[name = string("out_57_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1255_to_fp16 = const()[name = string("op_1255_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_57_cast_fp16 = layer_norm(axes = out_57_axes_0, epsilon = var_1255_to_fp16, x = x_111_cast_fp16)[name = string("out_57_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_3_ffn_norm_weight_to_fp16 = const()[name = string("stages_0_3_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203417472)))];
            tensor<fp16, [1, 4096, 1, 12]> out_59_cast_fp16 = mul(x = out_57_cast_fp16, y = stages_0_3_ffn_norm_weight_to_fp16)[name = string("out_59_cast_fp16")];
            tensor<int32, [2]> var_1261_split_sizes_0 = const()[name = string("op_1261_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1261_axis_0 = const()[name = string("op_1261_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_1261_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_1261_cast_fp16_1 = split(axis = var_1261_axis_0, split_sizes = var_1261_split_sizes_0, x = out_59_cast_fp16)[name = string("op_1261_cast_fp16")];
            tensor<int32, [1]> x_115_axes_0 = const()[name = string("x_115_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_115_cast_fp16 = squeeze(axes = x_115_axes_0, x = var_1261_cast_fp16_0)[name = string("x_115_cast_fp16")];
            string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(1)];
            tensor<fp16, [8192, 2048, 1]> var_1267_to_fp16 = const()[name = string("op_1267_to_fp16"), val = tensor<fp16, [8192, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(203425728)))];
            tensor<fp16, [8192]> stages_0_3_ffn_linear1_bias_to_fp16 = const()[name = string("stages_0_3_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [8192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236980224)))];
            tensor<fp16, [1, 8192, 12]> input_17_cast_fp16 = conv(bias = stages_0_3_ffn_linear1_bias_to_fp16, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = var_1267_to_fp16, x = x_115_cast_fp16)[name = string("input_17_cast_fp16")];
            string x_117_mode_0 = const()[name = string("x_117_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 8192, 12]> x_117_cast_fp16 = gelu(mode = x_117_mode_0, x = input_17_cast_fp16)[name = string("x_117_cast_fp16")];
            string x_119_pad_type_0 = const()[name = string("x_119_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_119_strides_0 = const()[name = string("x_119_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_119_pad_0 = const()[name = string("x_119_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_119_dilations_0 = const()[name = string("x_119_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_119_groups_0 = const()[name = string("x_119_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 8192, 1]> x_121_weight_0_to_fp16 = const()[name = string("x_121_weight_0_to_fp16"), val = tensor<fp16, [2048, 8192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(236996672)))];
            tensor<fp16, [2048]> x_121_bias_0_to_fp16 = const()[name = string("x_121_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270551168)))];
            tensor<fp16, [1, 2048, 12]> x_121_cast_fp16 = conv(bias = x_121_bias_0_to_fp16, dilations = x_119_dilations_0, groups = x_119_groups_0, pad = x_119_pad_0, pad_type = x_119_pad_type_0, strides = x_119_strides_0, weight = x_121_weight_0_to_fp16, x = x_117_cast_fp16)[name = string("x_121_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_123_cast_fp16 = add(x = x_107_cast_fp16, y = x_121_cast_fp16)[name = string("x_123_cast_fp16")];
            int32 var_1296 = const()[name = string("op_1296"), val = int32(1)];
            tensor<int32, [1]> x_125_axes_0 = const()[name = string("x_125_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_125_cast_fp16 = expand_dims(axes = x_125_axes_0, x = x_123_cast_fp16)[name = string("x_125_cast_fp16")];
            fp16 const_18_promoted_to_fp16 = const()[name = string("const_18_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_1301_cast_fp16 = mul(x = x_125_cast_fp16, y = const_18_promoted_to_fp16)[name = string("op_1301_cast_fp16")];
            bool x_127_interleave_0 = const()[name = string("x_127_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_127_cast_fp16 = concat(axis = var_1296, interleave = x_127_interleave_0, values = (x_125_cast_fp16, var_1301_cast_fp16))[name = string("x_127_cast_fp16")];
            tensor<int32, [1]> out_65_axes_0 = const()[name = string("out_65_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1311_to_fp16 = const()[name = string("op_1311_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_65_cast_fp16 = layer_norm(axes = out_65_axes_0, epsilon = var_1311_to_fp16, x = x_127_cast_fp16)[name = string("out_65_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_4_norm_weight_to_fp16 = const()[name = string("stages_0_4_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270555328)))];
            tensor<fp16, [1, 4096, 1, 12]> out_67_cast_fp16 = mul(x = out_65_cast_fp16, y = stages_0_4_norm_weight_to_fp16)[name = string("out_67_cast_fp16")];
            tensor<int32, [2]> var_1317_split_sizes_0 = const()[name = string("op_1317_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1317_axis_0 = const()[name = string("op_1317_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_1317_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_1317_cast_fp16_1 = split(axis = var_1317_axis_0, split_sizes = var_1317_split_sizes_0, x = out_67_cast_fp16)[name = string("op_1317_cast_fp16")];
            tensor<int32, [1]> x_131_axes_0 = const()[name = string("x_131_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_131_cast_fp16 = squeeze(axes = x_131_axes_0, x = var_1317_cast_fp16_0)[name = string("x_131_cast_fp16")];
            int32 var_1322 = const()[name = string("op_1322"), val = int32(-1)];
            bool input_19_interleave_0 = const()[name = string("input_19_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 18]> input_19_cast_fp16 = concat(axis = var_1322, interleave = input_19_interleave_0, values = (var_732_cast_fp16_4, x_131_cast_fp16))[name = string("input_19_cast_fp16")];
            string x_133_pad_type_0 = const()[name = string("x_133_pad_type_0"), val = string("valid")];
            int32 x_133_groups_0 = const()[name = string("x_133_groups_0"), val = int32(2048)];
            tensor<int32, [1]> x_133_strides_0 = const()[name = string("x_133_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_133_pad_0 = const()[name = string("x_133_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_133_dilations_0 = const()[name = string("x_133_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2048, 1, 7]> x_135_weight_0_to_fp16 = const()[name = string("x_135_weight_0_to_fp16"), val = tensor<fp16, [2048, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270563584)))];
            tensor<fp16, [2048]> x_135_bias_0_to_fp16 = const()[name = string("x_135_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270592320)))];
            tensor<fp16, [1, 2048, 12]> x_135_cast_fp16 = conv(bias = x_135_bias_0_to_fp16, dilations = x_133_dilations_0, groups = x_133_groups_0, pad = x_133_pad_0, pad_type = x_133_pad_type_0, strides = x_133_strides_0, weight = x_135_weight_0_to_fp16, x = input_19_cast_fp16)[name = string("x_135_cast_fp16")];
            tensor<int32, [3]> var_1345_begin_0 = const()[name = string("op_1345_begin_0"), val = tensor<int32, [3]>([0, 0, 12])];
            tensor<int32, [3]> var_1345_end_0 = const()[name = string("op_1345_end_0"), val = tensor<int32, [3]>([1, 2048, 18])];
            tensor<bool, [3]> var_1345_end_mask_0 = const()[name = string("op_1345_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 2048, 6]> var_1345_cast_fp16 = slice_by_index(begin = var_1345_begin_0, end = var_1345_end_0, end_mask = var_1345_end_mask_0, x = input_19_cast_fp16)[name = string("op_1345_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_137_cast_fp16 = add(x = x_123_cast_fp16, y = x_135_cast_fp16)[name = string("x_137_cast_fp16")];
            int32 var_1355 = const()[name = string("op_1355"), val = int32(1)];
            tensor<int32, [1]> x_139_axes_0 = const()[name = string("x_139_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_139_cast_fp16 = expand_dims(axes = x_139_axes_0, x = x_137_cast_fp16)[name = string("x_139_cast_fp16")];
            fp16 const_21_promoted_to_fp16 = const()[name = string("const_21_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_1360_cast_fp16 = mul(x = x_139_cast_fp16, y = const_21_promoted_to_fp16)[name = string("op_1360_cast_fp16")];
            bool x_141_interleave_0 = const()[name = string("x_141_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_141_cast_fp16 = concat(axis = var_1355, interleave = x_141_interleave_0, values = (x_139_cast_fp16, var_1360_cast_fp16))[name = string("x_141_cast_fp16")];
            tensor<int32, [1]> out_73_axes_0 = const()[name = string("out_73_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1370_to_fp16 = const()[name = string("op_1370_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_73_cast_fp16 = layer_norm(axes = out_73_axes_0, epsilon = var_1370_to_fp16, x = x_141_cast_fp16)[name = string("out_73_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_4_ffn_norm_weight_to_fp16 = const()[name = string("stages_0_4_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270596480)))];
            tensor<fp16, [1, 4096, 1, 12]> out_75_cast_fp16 = mul(x = out_73_cast_fp16, y = stages_0_4_ffn_norm_weight_to_fp16)[name = string("out_75_cast_fp16")];
            tensor<int32, [2]> var_1376_split_sizes_0 = const()[name = string("op_1376_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1376_axis_0 = const()[name = string("op_1376_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_1376_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_1376_cast_fp16_1 = split(axis = var_1376_axis_0, split_sizes = var_1376_split_sizes_0, x = out_75_cast_fp16)[name = string("op_1376_cast_fp16")];
            tensor<int32, [1]> x_145_axes_0 = const()[name = string("x_145_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_145_cast_fp16 = squeeze(axes = x_145_axes_0, x = var_1376_cast_fp16_0)[name = string("x_145_cast_fp16")];
            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)];
            tensor<fp16, [8192, 2048, 1]> var_1382_to_fp16 = const()[name = string("op_1382_to_fp16"), val = tensor<fp16, [8192, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270604736)))];
            tensor<fp16, [8192]> stages_0_4_ffn_linear1_bias_to_fp16 = const()[name = string("stages_0_4_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [8192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304159232)))];
            tensor<fp16, [1, 8192, 12]> input_21_cast_fp16 = conv(bias = stages_0_4_ffn_linear1_bias_to_fp16, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = var_1382_to_fp16, x = x_145_cast_fp16)[name = string("input_21_cast_fp16")];
            string x_147_mode_0 = const()[name = string("x_147_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 8192, 12]> x_147_cast_fp16 = gelu(mode = x_147_mode_0, x = input_21_cast_fp16)[name = string("x_147_cast_fp16")];
            string x_149_pad_type_0 = const()[name = string("x_149_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_149_strides_0 = const()[name = string("x_149_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_149_pad_0 = const()[name = string("x_149_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_149_dilations_0 = const()[name = string("x_149_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_149_groups_0 = const()[name = string("x_149_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 8192, 1]> x_151_weight_0_to_fp16 = const()[name = string("x_151_weight_0_to_fp16"), val = tensor<fp16, [2048, 8192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304175680)))];
            tensor<fp16, [2048]> x_151_bias_0_to_fp16 = const()[name = string("x_151_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337730176)))];
            tensor<fp16, [1, 2048, 12]> x_151_cast_fp16 = conv(bias = x_151_bias_0_to_fp16, dilations = x_149_dilations_0, groups = x_149_groups_0, pad = x_149_pad_0, pad_type = x_149_pad_type_0, strides = x_149_strides_0, weight = x_151_weight_0_to_fp16, x = x_147_cast_fp16)[name = string("x_151_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_153_cast_fp16 = add(x = x_137_cast_fp16, y = x_151_cast_fp16)[name = string("x_153_cast_fp16")];
            int32 var_1411 = const()[name = string("op_1411"), val = int32(1)];
            tensor<int32, [1]> x_155_axes_0 = const()[name = string("x_155_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_155_cast_fp16 = expand_dims(axes = x_155_axes_0, x = x_153_cast_fp16)[name = string("x_155_cast_fp16")];
            fp16 const_22_promoted_to_fp16 = const()[name = string("const_22_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_1416_cast_fp16 = mul(x = x_155_cast_fp16, y = const_22_promoted_to_fp16)[name = string("op_1416_cast_fp16")];
            bool x_157_interleave_0 = const()[name = string("x_157_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_157_cast_fp16 = concat(axis = var_1411, interleave = x_157_interleave_0, values = (x_155_cast_fp16, var_1416_cast_fp16))[name = string("x_157_cast_fp16")];
            tensor<int32, [1]> out_81_axes_0 = const()[name = string("out_81_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1426_to_fp16 = const()[name = string("op_1426_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_81_cast_fp16 = layer_norm(axes = out_81_axes_0, epsilon = var_1426_to_fp16, x = x_157_cast_fp16)[name = string("out_81_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_5_norm_weight_to_fp16 = const()[name = string("stages_0_5_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337734336)))];
            tensor<fp16, [1, 4096, 1, 12]> out_83_cast_fp16 = mul(x = out_81_cast_fp16, y = stages_0_5_norm_weight_to_fp16)[name = string("out_83_cast_fp16")];
            tensor<int32, [2]> var_1432_split_sizes_0 = const()[name = string("op_1432_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1432_axis_0 = const()[name = string("op_1432_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_1432_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_1432_cast_fp16_1 = split(axis = var_1432_axis_0, split_sizes = var_1432_split_sizes_0, x = out_83_cast_fp16)[name = string("op_1432_cast_fp16")];
            tensor<int32, [1]> x_161_axes_0 = const()[name = string("x_161_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_161_cast_fp16 = squeeze(axes = x_161_axes_0, x = var_1432_cast_fp16_0)[name = string("x_161_cast_fp16")];
            int32 var_1437 = const()[name = string("op_1437"), val = int32(-1)];
            bool input_23_interleave_0 = const()[name = string("input_23_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 18]> input_23_cast_fp16 = concat(axis = var_1437, interleave = input_23_interleave_0, values = (var_732_cast_fp16_5, x_161_cast_fp16))[name = string("input_23_cast_fp16")];
            string x_163_pad_type_0 = const()[name = string("x_163_pad_type_0"), val = string("valid")];
            int32 x_163_groups_0 = const()[name = string("x_163_groups_0"), val = int32(2048)];
            tensor<int32, [1]> x_163_strides_0 = const()[name = string("x_163_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_163_pad_0 = const()[name = string("x_163_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_163_dilations_0 = const()[name = string("x_163_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2048, 1, 7]> x_165_weight_0_to_fp16 = const()[name = string("x_165_weight_0_to_fp16"), val = tensor<fp16, [2048, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337742592)))];
            tensor<fp16, [2048]> x_165_bias_0_to_fp16 = const()[name = string("x_165_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337771328)))];
            tensor<fp16, [1, 2048, 12]> x_165_cast_fp16 = conv(bias = x_165_bias_0_to_fp16, dilations = x_163_dilations_0, groups = x_163_groups_0, pad = x_163_pad_0, pad_type = x_163_pad_type_0, strides = x_163_strides_0, weight = x_165_weight_0_to_fp16, x = input_23_cast_fp16)[name = string("x_165_cast_fp16")];
            tensor<int32, [3]> var_1460_begin_0 = const()[name = string("op_1460_begin_0"), val = tensor<int32, [3]>([0, 0, 12])];
            tensor<int32, [3]> var_1460_end_0 = const()[name = string("op_1460_end_0"), val = tensor<int32, [3]>([1, 2048, 18])];
            tensor<bool, [3]> var_1460_end_mask_0 = const()[name = string("op_1460_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 2048, 6]> var_1460_cast_fp16 = slice_by_index(begin = var_1460_begin_0, end = var_1460_end_0, end_mask = var_1460_end_mask_0, x = input_23_cast_fp16)[name = string("op_1460_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_167_cast_fp16 = add(x = x_153_cast_fp16, y = x_165_cast_fp16)[name = string("x_167_cast_fp16")];
            int32 var_1470 = const()[name = string("op_1470"), val = int32(1)];
            tensor<int32, [1]> x_169_axes_0 = const()[name = string("x_169_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_169_cast_fp16 = expand_dims(axes = x_169_axes_0, x = x_167_cast_fp16)[name = string("x_169_cast_fp16")];
            fp16 const_25_promoted_to_fp16 = const()[name = string("const_25_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_1475_cast_fp16 = mul(x = x_169_cast_fp16, y = const_25_promoted_to_fp16)[name = string("op_1475_cast_fp16")];
            bool x_171_interleave_0 = const()[name = string("x_171_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_171_cast_fp16 = concat(axis = var_1470, interleave = x_171_interleave_0, values = (x_169_cast_fp16, var_1475_cast_fp16))[name = string("x_171_cast_fp16")];
            tensor<int32, [1]> out_89_axes_0 = const()[name = string("out_89_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1485_to_fp16 = const()[name = string("op_1485_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_89_cast_fp16 = layer_norm(axes = out_89_axes_0, epsilon = var_1485_to_fp16, x = x_171_cast_fp16)[name = string("out_89_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_5_ffn_norm_weight_to_fp16 = const()[name = string("stages_0_5_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337775488)))];
            tensor<fp16, [1, 4096, 1, 12]> out_91_cast_fp16 = mul(x = out_89_cast_fp16, y = stages_0_5_ffn_norm_weight_to_fp16)[name = string("out_91_cast_fp16")];
            tensor<int32, [2]> var_1491_split_sizes_0 = const()[name = string("op_1491_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1491_axis_0 = const()[name = string("op_1491_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_1491_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_1491_cast_fp16_1 = split(axis = var_1491_axis_0, split_sizes = var_1491_split_sizes_0, x = out_91_cast_fp16)[name = string("op_1491_cast_fp16")];
            tensor<int32, [1]> x_175_axes_0 = const()[name = string("x_175_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_175_cast_fp16 = squeeze(axes = x_175_axes_0, x = var_1491_cast_fp16_0)[name = string("x_175_cast_fp16")];
            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
            tensor<fp16, [8192, 2048, 1]> var_1497_to_fp16 = const()[name = string("op_1497_to_fp16"), val = tensor<fp16, [8192, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(337783744)))];
            tensor<fp16, [8192]> stages_0_5_ffn_linear1_bias_to_fp16 = const()[name = string("stages_0_5_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [8192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371338240)))];
            tensor<fp16, [1, 8192, 12]> input_25_cast_fp16 = conv(bias = stages_0_5_ffn_linear1_bias_to_fp16, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = var_1497_to_fp16, x = x_175_cast_fp16)[name = string("input_25_cast_fp16")];
            string x_177_mode_0 = const()[name = string("x_177_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 8192, 12]> x_177_cast_fp16 = gelu(mode = x_177_mode_0, x = input_25_cast_fp16)[name = string("x_177_cast_fp16")];
            string x_179_pad_type_0 = const()[name = string("x_179_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_179_strides_0 = const()[name = string("x_179_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_179_pad_0 = const()[name = string("x_179_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_179_dilations_0 = const()[name = string("x_179_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_179_groups_0 = const()[name = string("x_179_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 8192, 1]> x_181_weight_0_to_fp16 = const()[name = string("x_181_weight_0_to_fp16"), val = tensor<fp16, [2048, 8192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(371354688)))];
            tensor<fp16, [2048]> x_181_bias_0_to_fp16 = const()[name = string("x_181_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(404909184)))];
            tensor<fp16, [1, 2048, 12]> x_181_cast_fp16 = conv(bias = x_181_bias_0_to_fp16, dilations = x_179_dilations_0, groups = x_179_groups_0, pad = x_179_pad_0, pad_type = x_179_pad_type_0, strides = x_179_strides_0, weight = x_181_weight_0_to_fp16, x = x_177_cast_fp16)[name = string("x_181_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_183_cast_fp16 = add(x = x_167_cast_fp16, y = x_181_cast_fp16)[name = string("x_183_cast_fp16")];
            int32 var_1526 = const()[name = string("op_1526"), val = int32(1)];
            tensor<int32, [1]> x_185_axes_0 = const()[name = string("x_185_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_185_cast_fp16 = expand_dims(axes = x_185_axes_0, x = x_183_cast_fp16)[name = string("x_185_cast_fp16")];
            fp16 const_26_promoted_to_fp16 = const()[name = string("const_26_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_1531_cast_fp16 = mul(x = x_185_cast_fp16, y = const_26_promoted_to_fp16)[name = string("op_1531_cast_fp16")];
            bool x_187_interleave_0 = const()[name = string("x_187_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_187_cast_fp16 = concat(axis = var_1526, interleave = x_187_interleave_0, values = (x_185_cast_fp16, var_1531_cast_fp16))[name = string("x_187_cast_fp16")];
            tensor<int32, [1]> out_97_axes_0 = const()[name = string("out_97_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1541_to_fp16 = const()[name = string("op_1541_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_97_cast_fp16 = layer_norm(axes = out_97_axes_0, epsilon = var_1541_to_fp16, x = x_187_cast_fp16)[name = string("out_97_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_6_norm_weight_to_fp16 = const()[name = string("stages_0_6_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(404913344)))];
            tensor<fp16, [1, 4096, 1, 12]> out_99_cast_fp16 = mul(x = out_97_cast_fp16, y = stages_0_6_norm_weight_to_fp16)[name = string("out_99_cast_fp16")];
            tensor<int32, [2]> var_1547_split_sizes_0 = const()[name = string("op_1547_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1547_axis_0 = const()[name = string("op_1547_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_1547_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_1547_cast_fp16_1 = split(axis = var_1547_axis_0, split_sizes = var_1547_split_sizes_0, x = out_99_cast_fp16)[name = string("op_1547_cast_fp16")];
            tensor<int32, [1]> x_191_axes_0 = const()[name = string("x_191_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_191_cast_fp16 = squeeze(axes = x_191_axes_0, x = var_1547_cast_fp16_0)[name = string("x_191_cast_fp16")];
            int32 var_1552 = const()[name = string("op_1552"), val = int32(-1)];
            bool input_27_interleave_0 = const()[name = string("input_27_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 18]> input_27_cast_fp16 = concat(axis = var_1552, interleave = input_27_interleave_0, values = (var_732_cast_fp16_6, x_191_cast_fp16))[name = string("input_27_cast_fp16")];
            string x_193_pad_type_0 = const()[name = string("x_193_pad_type_0"), val = string("valid")];
            int32 x_193_groups_0 = const()[name = string("x_193_groups_0"), val = int32(2048)];
            tensor<int32, [1]> x_193_strides_0 = const()[name = string("x_193_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_193_pad_0 = const()[name = string("x_193_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_193_dilations_0 = const()[name = string("x_193_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2048, 1, 7]> x_195_weight_0_to_fp16 = const()[name = string("x_195_weight_0_to_fp16"), val = tensor<fp16, [2048, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(404921600)))];
            tensor<fp16, [2048]> x_195_bias_0_to_fp16 = const()[name = string("x_195_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(404950336)))];
            tensor<fp16, [1, 2048, 12]> x_195_cast_fp16 = conv(bias = x_195_bias_0_to_fp16, dilations = x_193_dilations_0, groups = x_193_groups_0, pad = x_193_pad_0, pad_type = x_193_pad_type_0, strides = x_193_strides_0, weight = x_195_weight_0_to_fp16, x = input_27_cast_fp16)[name = string("x_195_cast_fp16")];
            tensor<int32, [3]> var_1575_begin_0 = const()[name = string("op_1575_begin_0"), val = tensor<int32, [3]>([0, 0, 12])];
            tensor<int32, [3]> var_1575_end_0 = const()[name = string("op_1575_end_0"), val = tensor<int32, [3]>([1, 2048, 18])];
            tensor<bool, [3]> var_1575_end_mask_0 = const()[name = string("op_1575_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 2048, 6]> var_1575_cast_fp16 = slice_by_index(begin = var_1575_begin_0, end = var_1575_end_0, end_mask = var_1575_end_mask_0, x = input_27_cast_fp16)[name = string("op_1575_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_197_cast_fp16 = add(x = x_183_cast_fp16, y = x_195_cast_fp16)[name = string("x_197_cast_fp16")];
            int32 var_1585 = const()[name = string("op_1585"), val = int32(1)];
            tensor<int32, [1]> x_199_axes_0 = const()[name = string("x_199_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_199_cast_fp16 = expand_dims(axes = x_199_axes_0, x = x_197_cast_fp16)[name = string("x_199_cast_fp16")];
            fp16 const_29_promoted_to_fp16 = const()[name = string("const_29_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_1590_cast_fp16 = mul(x = x_199_cast_fp16, y = const_29_promoted_to_fp16)[name = string("op_1590_cast_fp16")];
            bool x_201_interleave_0 = const()[name = string("x_201_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_201_cast_fp16 = concat(axis = var_1585, interleave = x_201_interleave_0, values = (x_199_cast_fp16, var_1590_cast_fp16))[name = string("x_201_cast_fp16")];
            tensor<int32, [1]> out_105_axes_0 = const()[name = string("out_105_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1600_to_fp16 = const()[name = string("op_1600_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_105_cast_fp16 = layer_norm(axes = out_105_axes_0, epsilon = var_1600_to_fp16, x = x_201_cast_fp16)[name = string("out_105_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_6_ffn_norm_weight_to_fp16 = const()[name = string("stages_0_6_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(404954496)))];
            tensor<fp16, [1, 4096, 1, 12]> out_107_cast_fp16 = mul(x = out_105_cast_fp16, y = stages_0_6_ffn_norm_weight_to_fp16)[name = string("out_107_cast_fp16")];
            tensor<int32, [2]> var_1606_split_sizes_0 = const()[name = string("op_1606_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1606_axis_0 = const()[name = string("op_1606_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_1606_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_1606_cast_fp16_1 = split(axis = var_1606_axis_0, split_sizes = var_1606_split_sizes_0, x = out_107_cast_fp16)[name = string("op_1606_cast_fp16")];
            tensor<int32, [1]> x_205_axes_0 = const()[name = string("x_205_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_205_cast_fp16 = squeeze(axes = x_205_axes_0, x = var_1606_cast_fp16_0)[name = string("x_205_cast_fp16")];
            string input_29_pad_type_0 = const()[name = string("input_29_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_29_strides_0 = const()[name = string("input_29_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_29_pad_0 = const()[name = string("input_29_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_29_dilations_0 = const()[name = string("input_29_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_29_groups_0 = const()[name = string("input_29_groups_0"), val = int32(1)];
            tensor<fp16, [8192, 2048, 1]> var_1612_to_fp16 = const()[name = string("op_1612_to_fp16"), val = tensor<fp16, [8192, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(404962752)))];
            tensor<fp16, [8192]> stages_0_6_ffn_linear1_bias_to_fp16 = const()[name = string("stages_0_6_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [8192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(438517248)))];
            tensor<fp16, [1, 8192, 12]> input_29_cast_fp16 = conv(bias = stages_0_6_ffn_linear1_bias_to_fp16, dilations = input_29_dilations_0, groups = input_29_groups_0, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = input_29_strides_0, weight = var_1612_to_fp16, x = x_205_cast_fp16)[name = string("input_29_cast_fp16")];
            string x_207_mode_0 = const()[name = string("x_207_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 8192, 12]> x_207_cast_fp16 = gelu(mode = x_207_mode_0, x = input_29_cast_fp16)[name = string("x_207_cast_fp16")];
            string x_209_pad_type_0 = const()[name = string("x_209_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_209_strides_0 = const()[name = string("x_209_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_209_pad_0 = const()[name = string("x_209_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_209_dilations_0 = const()[name = string("x_209_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_209_groups_0 = const()[name = string("x_209_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 8192, 1]> x_211_weight_0_to_fp16 = const()[name = string("x_211_weight_0_to_fp16"), val = tensor<fp16, [2048, 8192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(438533696)))];
            tensor<fp16, [2048]> x_211_bias_0_to_fp16 = const()[name = string("x_211_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472088192)))];
            tensor<fp16, [1, 2048, 12]> x_211_cast_fp16 = conv(bias = x_211_bias_0_to_fp16, dilations = x_209_dilations_0, groups = x_209_groups_0, pad = x_209_pad_0, pad_type = x_209_pad_type_0, strides = x_209_strides_0, weight = x_211_weight_0_to_fp16, x = x_207_cast_fp16)[name = string("x_211_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_213_cast_fp16 = add(x = x_197_cast_fp16, y = x_211_cast_fp16)[name = string("x_213_cast_fp16")];
            int32 var_1641 = const()[name = string("op_1641"), val = int32(1)];
            tensor<int32, [1]> x_215_axes_0 = const()[name = string("x_215_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_215_cast_fp16 = expand_dims(axes = x_215_axes_0, x = x_213_cast_fp16)[name = string("x_215_cast_fp16")];
            fp16 const_30_promoted_to_fp16 = const()[name = string("const_30_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_1646_cast_fp16 = mul(x = x_215_cast_fp16, y = const_30_promoted_to_fp16)[name = string("op_1646_cast_fp16")];
            bool x_217_interleave_0 = const()[name = string("x_217_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_217_cast_fp16 = concat(axis = var_1641, interleave = x_217_interleave_0, values = (x_215_cast_fp16, var_1646_cast_fp16))[name = string("x_217_cast_fp16")];
            tensor<int32, [1]> out_113_axes_0 = const()[name = string("out_113_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1656_to_fp16 = const()[name = string("op_1656_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_113_cast_fp16 = layer_norm(axes = out_113_axes_0, epsilon = var_1656_to_fp16, x = x_217_cast_fp16)[name = string("out_113_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_7_norm_weight_to_fp16 = const()[name = string("stages_0_7_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472092352)))];
            tensor<fp16, [1, 4096, 1, 12]> out_115_cast_fp16 = mul(x = out_113_cast_fp16, y = stages_0_7_norm_weight_to_fp16)[name = string("out_115_cast_fp16")];
            tensor<int32, [2]> var_1662_split_sizes_0 = const()[name = string("op_1662_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1662_axis_0 = const()[name = string("op_1662_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_1662_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_1662_cast_fp16_1 = split(axis = var_1662_axis_0, split_sizes = var_1662_split_sizes_0, x = out_115_cast_fp16)[name = string("op_1662_cast_fp16")];
            tensor<int32, [1]> x_221_axes_0 = const()[name = string("x_221_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_221_cast_fp16 = squeeze(axes = x_221_axes_0, x = var_1662_cast_fp16_0)[name = string("x_221_cast_fp16")];
            int32 var_1667 = const()[name = string("op_1667"), val = int32(-1)];
            bool input_31_interleave_0 = const()[name = string("input_31_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 18]> input_31_cast_fp16 = concat(axis = var_1667, interleave = input_31_interleave_0, values = (var_732_cast_fp16_7, x_221_cast_fp16))[name = string("input_31_cast_fp16")];
            string x_223_pad_type_0 = const()[name = string("x_223_pad_type_0"), val = string("valid")];
            int32 x_223_groups_0 = const()[name = string("x_223_groups_0"), val = int32(2048)];
            tensor<int32, [1]> x_223_strides_0 = const()[name = string("x_223_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_223_pad_0 = const()[name = string("x_223_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_223_dilations_0 = const()[name = string("x_223_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [2048, 1, 7]> x_225_weight_0_to_fp16 = const()[name = string("x_225_weight_0_to_fp16"), val = tensor<fp16, [2048, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472100608)))];
            tensor<fp16, [2048]> x_225_bias_0_to_fp16 = const()[name = string("x_225_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472129344)))];
            tensor<fp16, [1, 2048, 12]> x_225_cast_fp16 = conv(bias = x_225_bias_0_to_fp16, dilations = x_223_dilations_0, groups = x_223_groups_0, pad = x_223_pad_0, pad_type = x_223_pad_type_0, strides = x_223_strides_0, weight = x_225_weight_0_to_fp16, x = input_31_cast_fp16)[name = string("x_225_cast_fp16")];
            tensor<int32, [3]> var_1690_begin_0 = const()[name = string("op_1690_begin_0"), val = tensor<int32, [3]>([0, 0, 12])];
            tensor<int32, [3]> var_1690_end_0 = const()[name = string("op_1690_end_0"), val = tensor<int32, [3]>([1, 2048, 18])];
            tensor<bool, [3]> var_1690_end_mask_0 = const()[name = string("op_1690_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 2048, 6]> var_1690_cast_fp16 = slice_by_index(begin = var_1690_begin_0, end = var_1690_end_0, end_mask = var_1690_end_mask_0, x = input_31_cast_fp16)[name = string("op_1690_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_227_cast_fp16 = add(x = x_213_cast_fp16, y = x_225_cast_fp16)[name = string("x_227_cast_fp16")];
            int32 var_1700 = const()[name = string("op_1700"), val = int32(1)];
            tensor<int32, [1]> x_229_axes_0 = const()[name = string("x_229_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 1, 12]> x_229_cast_fp16 = expand_dims(axes = x_229_axes_0, x = x_227_cast_fp16)[name = string("x_229_cast_fp16")];
            fp16 const_33_promoted_to_fp16 = const()[name = string("const_33_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 12]> var_1705_cast_fp16 = mul(x = x_229_cast_fp16, y = const_33_promoted_to_fp16)[name = string("op_1705_cast_fp16")];
            bool x_231_interleave_0 = const()[name = string("x_231_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 12]> x_231_cast_fp16 = concat(axis = var_1700, interleave = x_231_interleave_0, values = (x_229_cast_fp16, var_1705_cast_fp16))[name = string("x_231_cast_fp16")];
            tensor<int32, [1]> out_121_axes_0 = const()[name = string("out_121_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1715_to_fp16 = const()[name = string("op_1715_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 12]> out_121_cast_fp16 = layer_norm(axes = out_121_axes_0, epsilon = var_1715_to_fp16, x = x_231_cast_fp16)[name = string("out_121_cast_fp16")];
            tensor<fp16, [1, 4096, 1, 1]> stages_0_7_ffn_norm_weight_to_fp16 = const()[name = string("stages_0_7_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 4096, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472133504)))];
            tensor<fp16, [1, 4096, 1, 12]> out_123_cast_fp16 = mul(x = out_121_cast_fp16, y = stages_0_7_ffn_norm_weight_to_fp16)[name = string("out_123_cast_fp16")];
            tensor<int32, [2]> var_1721_split_sizes_0 = const()[name = string("op_1721_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1721_axis_0 = const()[name = string("op_1721_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 12]> var_1721_cast_fp16_0, tensor<fp16, [1, 2048, 1, 12]> var_1721_cast_fp16_1 = split(axis = var_1721_axis_0, split_sizes = var_1721_split_sizes_0, x = out_123_cast_fp16)[name = string("op_1721_cast_fp16")];
            tensor<int32, [1]> x_235_axes_0 = const()[name = string("x_235_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 2048, 12]> x_235_cast_fp16 = squeeze(axes = x_235_axes_0, x = var_1721_cast_fp16_0)[name = string("x_235_cast_fp16")];
            string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)];
            tensor<fp16, [8192, 2048, 1]> var_1727_to_fp16 = const()[name = string("op_1727_to_fp16"), val = tensor<fp16, [8192, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(472141760)))];
            tensor<fp16, [8192]> stages_0_7_ffn_linear1_bias_to_fp16 = const()[name = string("stages_0_7_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [8192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(505696256)))];
            tensor<fp16, [1, 8192, 12]> input_33_cast_fp16 = conv(bias = stages_0_7_ffn_linear1_bias_to_fp16, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = var_1727_to_fp16, x = x_235_cast_fp16)[name = string("input_33_cast_fp16")];
            string x_237_mode_0 = const()[name = string("x_237_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 8192, 12]> x_237_cast_fp16 = gelu(mode = x_237_mode_0, x = input_33_cast_fp16)[name = string("x_237_cast_fp16")];
            string x_239_pad_type_0 = const()[name = string("x_239_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_239_strides_0 = const()[name = string("x_239_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_239_pad_0 = const()[name = string("x_239_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_239_dilations_0 = const()[name = string("x_239_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_239_groups_0 = const()[name = string("x_239_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 8192, 1]> x_241_weight_0_to_fp16 = const()[name = string("x_241_weight_0_to_fp16"), val = tensor<fp16, [2048, 8192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(505712704)))];
            tensor<fp16, [2048]> x_241_bias_0_to_fp16 = const()[name = string("x_241_bias_0_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539267200)))];
            tensor<fp16, [1, 2048, 12]> x_241_cast_fp16 = conv(bias = x_241_bias_0_to_fp16, dilations = x_239_dilations_0, groups = x_239_groups_0, pad = x_239_pad_0, pad_type = x_239_pad_type_0, strides = x_239_strides_0, weight = x_241_weight_0_to_fp16, x = x_237_cast_fp16)[name = string("x_241_cast_fp16")];
            tensor<fp16, [1, 2048, 12]> x_243_cast_fp16 = add(x = x_227_cast_fp16, y = x_241_cast_fp16)[name = string("x_243_cast_fp16")];
            int32 var_1758 = const()[name = string("op_1758"), val = int32(-1)];
            bool input_35_interleave_0 = const()[name = string("input_35_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 27]> input_35_cast_fp16 = concat(axis = var_1758, interleave = input_35_interleave_0, values = (var_732_cast_fp16_8, x_243_cast_fp16))[name = string("input_35_cast_fp16")];
            string full_output_1_pad_type_0 = const()[name = string("full_output_1_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> full_output_1_strides_0 = const()[name = string("full_output_1_strides_0"), val = tensor<int32, [1]>([8])];
            tensor<int32, [2]> full_output_1_pad_0 = const()[name = string("full_output_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> full_output_1_dilations_0 = const()[name = string("full_output_1_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 full_output_1_groups_0 = const()[name = string("full_output_1_groups_0"), val = int32(1)];
            tensor<int32, [3]> full_output_1_has_output_shape_output_shape_0 = const()[name = string("full_output_1_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 1024, 224])];
            tensor<fp16, [2048, 1024, 16]> upsample_layers_1_0_convtr_convtr_weight_to_fp16 = const()[name = string("upsample_layers_1_0_convtr_convtr_weight_to_fp16"), val = tensor<fp16, [2048, 1024, 16]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539271360)))];
            tensor<fp16, [1024]> upsample_layers_1_0_convtr_convtr_bias_to_fp16 = const()[name = string("upsample_layers_1_0_convtr_convtr_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(606380288)))];
            tensor<fp16, [1, 1024, 224]> full_output_1_has_output_shape_cast_fp16 = conv_transpose(bias = upsample_layers_1_0_convtr_convtr_bias_to_fp16, dilations = full_output_1_dilations_0, groups = full_output_1_groups_0, output_shape = full_output_1_has_output_shape_output_shape_0, pad = full_output_1_pad_0, pad_type = full_output_1_pad_type_0, strides = full_output_1_strides_0, weight = upsample_layers_1_0_convtr_convtr_weight_to_fp16, x = input_35_cast_fp16)[name = string("full_output_1_has_output_shape_cast_fp16")];
            tensor<int32, [3]> full_output_3_begin_0 = const()[name = string("full_output_3_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> full_output_3_end_0 = const()[name = string("full_output_3_end_0"), val = tensor<int32, [3]>([1, 1024, 216])];
            tensor<bool, [3]> full_output_3_end_mask_0 = const()[name = string("full_output_3_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp16, [1, 1024, 216]> full_output_3_cast_fp16 = slice_by_index(begin = full_output_3_begin_0, end = full_output_3_end_0, end_mask = full_output_3_end_mask_0, x = full_output_1_has_output_shape_cast_fp16)[name = string("full_output_3_cast_fp16")];
            tensor<int32, [3]> x_245_begin_0 = const()[name = string("x_245_begin_0"), val = tensor<int32, [3]>([0, 0, 120])];
            tensor<int32, [3]> x_245_end_0 = const()[name = string("x_245_end_0"), val = tensor<int32, [3]>([1, 1024, 216])];
            tensor<bool, [3]> x_245_end_mask_0 = const()[name = string("x_245_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 1024, 96]> x_245_cast_fp16 = slice_by_index(begin = x_245_begin_0, end = x_245_end_0, end_mask = x_245_end_mask_0, x = full_output_3_cast_fp16)[name = string("x_245_cast_fp16")];
            tensor<int32, [3]> var_1792_begin_0 = const()[name = string("op_1792_begin_0"), val = tensor<int32, [3]>([0, 0, 12])];
            tensor<int32, [3]> var_1792_end_0 = const()[name = string("op_1792_end_0"), val = tensor<int32, [3]>([1, 2048, 27])];
            tensor<bool, [3]> var_1792_end_mask_0 = const()[name = string("op_1792_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 2048, 15]> var_1792_cast_fp16 = slice_by_index(begin = var_1792_begin_0, end = var_1792_end_0, end_mask = var_1792_end_mask_0, x = input_35_cast_fp16)[name = string("op_1792_cast_fp16")];
            int32 var_1797 = const()[name = string("op_1797"), val = int32(1)];
            tensor<int32, [1]> x_247_axes_0 = const()[name = string("x_247_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 1024, 1, 96]> x_247_cast_fp16 = expand_dims(axes = x_247_axes_0, x = x_245_cast_fp16)[name = string("x_247_cast_fp16")];
            fp16 const_38_promoted_to_fp16 = const()[name = string("const_38_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 1024, 1, 96]> var_1802_cast_fp16 = mul(x = x_247_cast_fp16, y = const_38_promoted_to_fp16)[name = string("op_1802_cast_fp16")];
            bool x_249_interleave_0 = const()[name = string("x_249_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 1, 96]> x_249_cast_fp16 = concat(axis = var_1797, interleave = x_249_interleave_0, values = (x_247_cast_fp16, var_1802_cast_fp16))[name = string("x_249_cast_fp16")];
            tensor<int32, [1]> out_129_axes_0 = const()[name = string("out_129_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1812_to_fp16 = const()[name = string("op_1812_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 2048, 1, 96]> out_129_cast_fp16 = layer_norm(axes = out_129_axes_0, epsilon = var_1812_to_fp16, x = x_249_cast_fp16)[name = string("out_129_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> stages_1_0_norm_weight_to_fp16 = const()[name = string("stages_1_0_norm_weight_to_fp16"), val = tensor<fp16, [1, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(606382400)))];
            tensor<fp16, [1, 2048, 1, 96]> out_131_cast_fp16 = mul(x = out_129_cast_fp16, y = stages_1_0_norm_weight_to_fp16)[name = string("out_131_cast_fp16")];
            tensor<int32, [2]> var_1818_split_sizes_0 = const()[name = string("op_1818_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
            int32 var_1818_axis_0 = const()[name = string("op_1818_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 1, 96]> var_1818_cast_fp16_0, tensor<fp16, [1, 1024, 1, 96]> var_1818_cast_fp16_1 = split(axis = var_1818_axis_0, split_sizes = var_1818_split_sizes_0, x = out_131_cast_fp16)[name = string("op_1818_cast_fp16")];
            tensor<int32, [1]> x_253_axes_0 = const()[name = string("x_253_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 1024, 96]> x_253_cast_fp16 = squeeze(axes = x_253_axes_0, x = var_1818_cast_fp16_0)[name = string("x_253_cast_fp16")];
            int32 var_1823 = const()[name = string("op_1823"), val = int32(-1)];
            bool input_37_interleave_0 = const()[name = string("input_37_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 1024, 102]> input_37_cast_fp16 = concat(axis = var_1823, interleave = input_37_interleave_0, values = (var_748_cast_fp16_0, x_253_cast_fp16))[name = string("input_37_cast_fp16")];
            string x_255_pad_type_0 = const()[name = string("x_255_pad_type_0"), val = string("valid")];
            int32 x_255_groups_0 = const()[name = string("x_255_groups_0"), val = int32(1024)];
            tensor<int32, [1]> x_255_strides_0 = const()[name = string("x_255_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_255_pad_0 = const()[name = string("x_255_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_255_dilations_0 = const()[name = string("x_255_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 7]> x_257_weight_0_to_fp16 = const()[name = string("x_257_weight_0_to_fp16"), val = tensor<fp16, [1024, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(606386560)))];
            tensor<fp16, [1024]> x_257_bias_0_to_fp16 = const()[name = string("x_257_bias_0_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(606400960)))];
            tensor<fp16, [1, 1024, 96]> x_257_cast_fp16 = conv(bias = x_257_bias_0_to_fp16, dilations = x_255_dilations_0, groups = x_255_groups_0, pad = x_255_pad_0, pad_type = x_255_pad_type_0, strides = x_255_strides_0, weight = x_257_weight_0_to_fp16, x = input_37_cast_fp16)[name = string("x_257_cast_fp16")];
            tensor<int32, [3]> var_1846_begin_0 = const()[name = string("op_1846_begin_0"), val = tensor<int32, [3]>([0, 0, 96])];
            tensor<int32, [3]> var_1846_end_0 = const()[name = string("op_1846_end_0"), val = tensor<int32, [3]>([1, 1024, 102])];
            tensor<bool, [3]> var_1846_end_mask_0 = const()[name = string("op_1846_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 1024, 6]> var_1846_cast_fp16 = slice_by_index(begin = var_1846_begin_0, end = var_1846_end_0, end_mask = var_1846_end_mask_0, x = input_37_cast_fp16)[name = string("op_1846_cast_fp16")];
            tensor<fp16, [1, 1024, 96]> x_259_cast_fp16 = add(x = x_245_cast_fp16, y = x_257_cast_fp16)[name = string("x_259_cast_fp16")];
            int32 var_1856 = const()[name = string("op_1856"), val = int32(1)];
            tensor<int32, [1]> x_261_axes_0 = const()[name = string("x_261_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 1024, 1, 96]> x_261_cast_fp16 = expand_dims(axes = x_261_axes_0, x = x_259_cast_fp16)[name = string("x_261_cast_fp16")];
            fp16 const_41_promoted_to_fp16 = const()[name = string("const_41_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 1024, 1, 96]> var_1861_cast_fp16 = mul(x = x_261_cast_fp16, y = const_41_promoted_to_fp16)[name = string("op_1861_cast_fp16")];
            bool x_263_interleave_0 = const()[name = string("x_263_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 1, 96]> x_263_cast_fp16 = concat(axis = var_1856, interleave = x_263_interleave_0, values = (x_261_cast_fp16, var_1861_cast_fp16))[name = string("x_263_cast_fp16")];
            tensor<int32, [1]> out_137_axes_0 = const()[name = string("out_137_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1871_to_fp16 = const()[name = string("op_1871_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 2048, 1, 96]> out_137_cast_fp16 = layer_norm(axes = out_137_axes_0, epsilon = var_1871_to_fp16, x = x_263_cast_fp16)[name = string("out_137_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> stages_1_0_ffn_norm_weight_to_fp16 = const()[name = string("stages_1_0_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(606403072)))];
            tensor<fp16, [1, 2048, 1, 96]> out_139_cast_fp16 = mul(x = out_137_cast_fp16, y = stages_1_0_ffn_norm_weight_to_fp16)[name = string("out_139_cast_fp16")];
            tensor<int32, [2]> var_1877_split_sizes_0 = const()[name = string("op_1877_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
            int32 var_1877_axis_0 = const()[name = string("op_1877_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 1, 96]> var_1877_cast_fp16_0, tensor<fp16, [1, 1024, 1, 96]> var_1877_cast_fp16_1 = split(axis = var_1877_axis_0, split_sizes = var_1877_split_sizes_0, x = out_139_cast_fp16)[name = string("op_1877_cast_fp16")];
            tensor<int32, [1]> x_267_axes_0 = const()[name = string("x_267_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 1024, 96]> x_267_cast_fp16 = squeeze(axes = x_267_axes_0, x = var_1877_cast_fp16_0)[name = string("x_267_cast_fp16")];
            string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)];
            tensor<fp16, [4096, 1024, 1]> var_1883_to_fp16 = const()[name = string("op_1883_to_fp16"), val = tensor<fp16, [4096, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(606407232)))];
            tensor<fp16, [4096]> stages_1_0_ffn_linear1_bias_to_fp16 = const()[name = string("stages_1_0_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(614795904)))];
            tensor<fp16, [1, 4096, 96]> input_39_cast_fp16 = conv(bias = stages_1_0_ffn_linear1_bias_to_fp16, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = var_1883_to_fp16, x = x_267_cast_fp16)[name = string("input_39_cast_fp16")];
            string x_269_mode_0 = const()[name = string("x_269_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 4096, 96]> x_269_cast_fp16 = gelu(mode = x_269_mode_0, x = input_39_cast_fp16)[name = string("x_269_cast_fp16")];
            string x_271_pad_type_0 = const()[name = string("x_271_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_271_strides_0 = const()[name = string("x_271_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_271_pad_0 = const()[name = string("x_271_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_271_dilations_0 = const()[name = string("x_271_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_271_groups_0 = const()[name = string("x_271_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 4096, 1]> x_273_weight_0_to_fp16 = const()[name = string("x_273_weight_0_to_fp16"), val = tensor<fp16, [1024, 4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(614804160)))];
            tensor<fp16, [1024]> x_273_bias_0_to_fp16 = const()[name = string("x_273_bias_0_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(623192832)))];
            tensor<fp16, [1, 1024, 96]> x_273_cast_fp16 = conv(bias = x_273_bias_0_to_fp16, dilations = x_271_dilations_0, groups = x_271_groups_0, pad = x_271_pad_0, pad_type = x_271_pad_type_0, strides = x_271_strides_0, weight = x_273_weight_0_to_fp16, x = x_269_cast_fp16)[name = string("x_273_cast_fp16")];
            tensor<fp16, [1, 1024, 96]> x_275_cast_fp16 = add(x = x_259_cast_fp16, y = x_273_cast_fp16)[name = string("x_275_cast_fp16")];
            int32 var_1912 = const()[name = string("op_1912"), val = int32(1)];
            tensor<int32, [1]> x_277_axes_0 = const()[name = string("x_277_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 1024, 1, 96]> x_277_cast_fp16 = expand_dims(axes = x_277_axes_0, x = x_275_cast_fp16)[name = string("x_277_cast_fp16")];
            fp16 const_42_promoted_to_fp16 = const()[name = string("const_42_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 1024, 1, 96]> var_1917_cast_fp16 = mul(x = x_277_cast_fp16, y = const_42_promoted_to_fp16)[name = string("op_1917_cast_fp16")];
            bool x_279_interleave_0 = const()[name = string("x_279_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 1, 96]> x_279_cast_fp16 = concat(axis = var_1912, interleave = x_279_interleave_0, values = (x_277_cast_fp16, var_1917_cast_fp16))[name = string("x_279_cast_fp16")];
            tensor<int32, [1]> out_145_axes_0 = const()[name = string("out_145_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1927_to_fp16 = const()[name = string("op_1927_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 2048, 1, 96]> out_145_cast_fp16 = layer_norm(axes = out_145_axes_0, epsilon = var_1927_to_fp16, x = x_279_cast_fp16)[name = string("out_145_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> stages_1_1_norm_weight_to_fp16 = const()[name = string("stages_1_1_norm_weight_to_fp16"), val = tensor<fp16, [1, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(623194944)))];
            tensor<fp16, [1, 2048, 1, 96]> out_147_cast_fp16 = mul(x = out_145_cast_fp16, y = stages_1_1_norm_weight_to_fp16)[name = string("out_147_cast_fp16")];
            tensor<int32, [2]> var_1933_split_sizes_0 = const()[name = string("op_1933_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
            int32 var_1933_axis_0 = const()[name = string("op_1933_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 1, 96]> var_1933_cast_fp16_0, tensor<fp16, [1, 1024, 1, 96]> var_1933_cast_fp16_1 = split(axis = var_1933_axis_0, split_sizes = var_1933_split_sizes_0, x = out_147_cast_fp16)[name = string("op_1933_cast_fp16")];
            tensor<int32, [1]> x_283_axes_0 = const()[name = string("x_283_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 1024, 96]> x_283_cast_fp16 = squeeze(axes = x_283_axes_0, x = var_1933_cast_fp16_0)[name = string("x_283_cast_fp16")];
            int32 var_1938 = const()[name = string("op_1938"), val = int32(-1)];
            bool input_41_interleave_0 = const()[name = string("input_41_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 1024, 102]> input_41_cast_fp16 = concat(axis = var_1938, interleave = input_41_interleave_0, values = (var_748_cast_fp16_1, x_283_cast_fp16))[name = string("input_41_cast_fp16")];
            string x_285_pad_type_0 = const()[name = string("x_285_pad_type_0"), val = string("valid")];
            int32 x_285_groups_0 = const()[name = string("x_285_groups_0"), val = int32(1024)];
            tensor<int32, [1]> x_285_strides_0 = const()[name = string("x_285_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_285_pad_0 = const()[name = string("x_285_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_285_dilations_0 = const()[name = string("x_285_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 7]> x_287_weight_0_to_fp16 = const()[name = string("x_287_weight_0_to_fp16"), val = tensor<fp16, [1024, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(623199104)))];
            tensor<fp16, [1024]> x_287_bias_0_to_fp16 = const()[name = string("x_287_bias_0_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(623213504)))];
            tensor<fp16, [1, 1024, 96]> x_287_cast_fp16 = conv(bias = x_287_bias_0_to_fp16, dilations = x_285_dilations_0, groups = x_285_groups_0, pad = x_285_pad_0, pad_type = x_285_pad_type_0, strides = x_285_strides_0, weight = x_287_weight_0_to_fp16, x = input_41_cast_fp16)[name = string("x_287_cast_fp16")];
            tensor<int32, [3]> var_1961_begin_0 = const()[name = string("op_1961_begin_0"), val = tensor<int32, [3]>([0, 0, 96])];
            tensor<int32, [3]> var_1961_end_0 = const()[name = string("op_1961_end_0"), val = tensor<int32, [3]>([1, 1024, 102])];
            tensor<bool, [3]> var_1961_end_mask_0 = const()[name = string("op_1961_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 1024, 6]> var_1961_cast_fp16 = slice_by_index(begin = var_1961_begin_0, end = var_1961_end_0, end_mask = var_1961_end_mask_0, x = input_41_cast_fp16)[name = string("op_1961_cast_fp16")];
            tensor<fp16, [1, 1024, 96]> x_289_cast_fp16 = add(x = x_275_cast_fp16, y = x_287_cast_fp16)[name = string("x_289_cast_fp16")];
            int32 var_1971 = const()[name = string("op_1971"), val = int32(1)];
            tensor<int32, [1]> x_291_axes_0 = const()[name = string("x_291_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 1024, 1, 96]> x_291_cast_fp16 = expand_dims(axes = x_291_axes_0, x = x_289_cast_fp16)[name = string("x_291_cast_fp16")];
            fp16 const_45_promoted_to_fp16 = const()[name = string("const_45_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 1024, 1, 96]> var_1976_cast_fp16 = mul(x = x_291_cast_fp16, y = const_45_promoted_to_fp16)[name = string("op_1976_cast_fp16")];
            bool x_293_interleave_0 = const()[name = string("x_293_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 1, 96]> x_293_cast_fp16 = concat(axis = var_1971, interleave = x_293_interleave_0, values = (x_291_cast_fp16, var_1976_cast_fp16))[name = string("x_293_cast_fp16")];
            tensor<int32, [1]> out_153_axes_0 = const()[name = string("out_153_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_1986_to_fp16 = const()[name = string("op_1986_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 2048, 1, 96]> out_153_cast_fp16 = layer_norm(axes = out_153_axes_0, epsilon = var_1986_to_fp16, x = x_293_cast_fp16)[name = string("out_153_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> stages_1_1_ffn_norm_weight_to_fp16 = const()[name = string("stages_1_1_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(623215616)))];
            tensor<fp16, [1, 2048, 1, 96]> out_155_cast_fp16 = mul(x = out_153_cast_fp16, y = stages_1_1_ffn_norm_weight_to_fp16)[name = string("out_155_cast_fp16")];
            tensor<int32, [2]> var_1992_split_sizes_0 = const()[name = string("op_1992_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
            int32 var_1992_axis_0 = const()[name = string("op_1992_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 1, 96]> var_1992_cast_fp16_0, tensor<fp16, [1, 1024, 1, 96]> var_1992_cast_fp16_1 = split(axis = var_1992_axis_0, split_sizes = var_1992_split_sizes_0, x = out_155_cast_fp16)[name = string("op_1992_cast_fp16")];
            tensor<int32, [1]> x_297_axes_0 = const()[name = string("x_297_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 1024, 96]> x_297_cast_fp16 = squeeze(axes = x_297_axes_0, x = var_1992_cast_fp16_0)[name = string("x_297_cast_fp16")];
            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
            tensor<fp16, [4096, 1024, 1]> var_1998_to_fp16 = const()[name = string("op_1998_to_fp16"), val = tensor<fp16, [4096, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(623219776)))];
            tensor<fp16, [4096]> stages_1_1_ffn_linear1_bias_to_fp16 = const()[name = string("stages_1_1_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(631608448)))];
            tensor<fp16, [1, 4096, 96]> input_43_cast_fp16 = conv(bias = stages_1_1_ffn_linear1_bias_to_fp16, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = var_1998_to_fp16, x = x_297_cast_fp16)[name = string("input_43_cast_fp16")];
            string x_299_mode_0 = const()[name = string("x_299_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 4096, 96]> x_299_cast_fp16 = gelu(mode = x_299_mode_0, x = input_43_cast_fp16)[name = string("x_299_cast_fp16")];
            string x_301_pad_type_0 = const()[name = string("x_301_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_301_strides_0 = const()[name = string("x_301_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_301_pad_0 = const()[name = string("x_301_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_301_dilations_0 = const()[name = string("x_301_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_301_groups_0 = const()[name = string("x_301_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 4096, 1]> x_303_weight_0_to_fp16 = const()[name = string("x_303_weight_0_to_fp16"), val = tensor<fp16, [1024, 4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(631616704)))];
            tensor<fp16, [1024]> x_303_bias_0_to_fp16 = const()[name = string("x_303_bias_0_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(640005376)))];
            tensor<fp16, [1, 1024, 96]> x_303_cast_fp16 = conv(bias = x_303_bias_0_to_fp16, dilations = x_301_dilations_0, groups = x_301_groups_0, pad = x_301_pad_0, pad_type = x_301_pad_type_0, strides = x_301_strides_0, weight = x_303_weight_0_to_fp16, x = x_299_cast_fp16)[name = string("x_303_cast_fp16")];
            tensor<fp16, [1, 1024, 96]> x_305_cast_fp16 = add(x = x_289_cast_fp16, y = x_303_cast_fp16)[name = string("x_305_cast_fp16")];
            int32 var_2027 = const()[name = string("op_2027"), val = int32(1)];
            tensor<int32, [1]> x_307_axes_0 = const()[name = string("x_307_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 1024, 1, 96]> x_307_cast_fp16 = expand_dims(axes = x_307_axes_0, x = x_305_cast_fp16)[name = string("x_307_cast_fp16")];
            fp16 const_46_promoted_to_fp16 = const()[name = string("const_46_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 1024, 1, 96]> var_2032_cast_fp16 = mul(x = x_307_cast_fp16, y = const_46_promoted_to_fp16)[name = string("op_2032_cast_fp16")];
            bool x_309_interleave_0 = const()[name = string("x_309_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 1, 96]> x_309_cast_fp16 = concat(axis = var_2027, interleave = x_309_interleave_0, values = (x_307_cast_fp16, var_2032_cast_fp16))[name = string("x_309_cast_fp16")];
            tensor<int32, [1]> out_161_axes_0 = const()[name = string("out_161_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2042_to_fp16 = const()[name = string("op_2042_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 2048, 1, 96]> out_161_cast_fp16 = layer_norm(axes = out_161_axes_0, epsilon = var_2042_to_fp16, x = x_309_cast_fp16)[name = string("out_161_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> stages_1_2_norm_weight_to_fp16 = const()[name = string("stages_1_2_norm_weight_to_fp16"), val = tensor<fp16, [1, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(640007488)))];
            tensor<fp16, [1, 2048, 1, 96]> out_163_cast_fp16 = mul(x = out_161_cast_fp16, y = stages_1_2_norm_weight_to_fp16)[name = string("out_163_cast_fp16")];
            tensor<int32, [2]> var_2048_split_sizes_0 = const()[name = string("op_2048_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
            int32 var_2048_axis_0 = const()[name = string("op_2048_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 1, 96]> var_2048_cast_fp16_0, tensor<fp16, [1, 1024, 1, 96]> var_2048_cast_fp16_1 = split(axis = var_2048_axis_0, split_sizes = var_2048_split_sizes_0, x = out_163_cast_fp16)[name = string("op_2048_cast_fp16")];
            tensor<int32, [1]> x_313_axes_0 = const()[name = string("x_313_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 1024, 96]> x_313_cast_fp16 = squeeze(axes = x_313_axes_0, x = var_2048_cast_fp16_0)[name = string("x_313_cast_fp16")];
            int32 var_2053 = const()[name = string("op_2053"), val = int32(-1)];
            bool input_45_interleave_0 = const()[name = string("input_45_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 1024, 102]> input_45_cast_fp16 = concat(axis = var_2053, interleave = input_45_interleave_0, values = (var_748_cast_fp16_2, x_313_cast_fp16))[name = string("input_45_cast_fp16")];
            string x_315_pad_type_0 = const()[name = string("x_315_pad_type_0"), val = string("valid")];
            int32 x_315_groups_0 = const()[name = string("x_315_groups_0"), val = int32(1024)];
            tensor<int32, [1]> x_315_strides_0 = const()[name = string("x_315_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_315_pad_0 = const()[name = string("x_315_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_315_dilations_0 = const()[name = string("x_315_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1024, 1, 7]> x_317_weight_0_to_fp16 = const()[name = string("x_317_weight_0_to_fp16"), val = tensor<fp16, [1024, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(640011648)))];
            tensor<fp16, [1024]> x_317_bias_0_to_fp16 = const()[name = string("x_317_bias_0_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(640026048)))];
            tensor<fp16, [1, 1024, 96]> x_317_cast_fp16 = conv(bias = x_317_bias_0_to_fp16, dilations = x_315_dilations_0, groups = x_315_groups_0, pad = x_315_pad_0, pad_type = x_315_pad_type_0, strides = x_315_strides_0, weight = x_317_weight_0_to_fp16, x = input_45_cast_fp16)[name = string("x_317_cast_fp16")];
            tensor<int32, [3]> var_2076_begin_0 = const()[name = string("op_2076_begin_0"), val = tensor<int32, [3]>([0, 0, 96])];
            tensor<int32, [3]> var_2076_end_0 = const()[name = string("op_2076_end_0"), val = tensor<int32, [3]>([1, 1024, 102])];
            tensor<bool, [3]> var_2076_end_mask_0 = const()[name = string("op_2076_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 1024, 6]> var_2076_cast_fp16 = slice_by_index(begin = var_2076_begin_0, end = var_2076_end_0, end_mask = var_2076_end_mask_0, x = input_45_cast_fp16)[name = string("op_2076_cast_fp16")];
            tensor<fp16, [1, 1024, 96]> x_319_cast_fp16 = add(x = x_305_cast_fp16, y = x_317_cast_fp16)[name = string("x_319_cast_fp16")];
            int32 var_2086 = const()[name = string("op_2086"), val = int32(1)];
            tensor<int32, [1]> x_321_axes_0 = const()[name = string("x_321_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 1024, 1, 96]> x_321_cast_fp16 = expand_dims(axes = x_321_axes_0, x = x_319_cast_fp16)[name = string("x_321_cast_fp16")];
            fp16 const_49_promoted_to_fp16 = const()[name = string("const_49_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 1024, 1, 96]> var_2091_cast_fp16 = mul(x = x_321_cast_fp16, y = const_49_promoted_to_fp16)[name = string("op_2091_cast_fp16")];
            bool x_323_interleave_0 = const()[name = string("x_323_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 1, 96]> x_323_cast_fp16 = concat(axis = var_2086, interleave = x_323_interleave_0, values = (x_321_cast_fp16, var_2091_cast_fp16))[name = string("x_323_cast_fp16")];
            tensor<int32, [1]> out_169_axes_0 = const()[name = string("out_169_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2101_to_fp16 = const()[name = string("op_2101_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 2048, 1, 96]> out_169_cast_fp16 = layer_norm(axes = out_169_axes_0, epsilon = var_2101_to_fp16, x = x_323_cast_fp16)[name = string("out_169_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> stages_1_2_ffn_norm_weight_to_fp16 = const()[name = string("stages_1_2_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(640028160)))];
            tensor<fp16, [1, 2048, 1, 96]> out_171_cast_fp16 = mul(x = out_169_cast_fp16, y = stages_1_2_ffn_norm_weight_to_fp16)[name = string("out_171_cast_fp16")];
            tensor<int32, [2]> var_2107_split_sizes_0 = const()[name = string("op_2107_split_sizes_0"), val = tensor<int32, [2]>([1024, 1024])];
            int32 var_2107_axis_0 = const()[name = string("op_2107_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1024, 1, 96]> var_2107_cast_fp16_0, tensor<fp16, [1, 1024, 1, 96]> var_2107_cast_fp16_1 = split(axis = var_2107_axis_0, split_sizes = var_2107_split_sizes_0, x = out_171_cast_fp16)[name = string("op_2107_cast_fp16")];
            tensor<int32, [1]> x_327_axes_0 = const()[name = string("x_327_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 1024, 96]> x_327_cast_fp16 = squeeze(axes = x_327_axes_0, x = var_2107_cast_fp16_0)[name = string("x_327_cast_fp16")];
            string input_47_pad_type_0 = const()[name = string("input_47_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_47_strides_0 = const()[name = string("input_47_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_47_dilations_0 = const()[name = string("input_47_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_47_groups_0 = const()[name = string("input_47_groups_0"), val = int32(1)];
            tensor<fp16, [4096, 1024, 1]> var_2113_to_fp16 = const()[name = string("op_2113_to_fp16"), val = tensor<fp16, [4096, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(640032320)))];
            tensor<fp16, [4096]> stages_1_2_ffn_linear1_bias_to_fp16 = const()[name = string("stages_1_2_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(648420992)))];
            tensor<fp16, [1, 4096, 96]> input_47_cast_fp16 = conv(bias = stages_1_2_ffn_linear1_bias_to_fp16, dilations = input_47_dilations_0, groups = input_47_groups_0, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = input_47_strides_0, weight = var_2113_to_fp16, x = x_327_cast_fp16)[name = string("input_47_cast_fp16")];
            string x_329_mode_0 = const()[name = string("x_329_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 4096, 96]> x_329_cast_fp16 = gelu(mode = x_329_mode_0, x = input_47_cast_fp16)[name = string("x_329_cast_fp16")];
            string x_331_pad_type_0 = const()[name = string("x_331_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_331_strides_0 = const()[name = string("x_331_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_331_pad_0 = const()[name = string("x_331_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_331_dilations_0 = const()[name = string("x_331_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_331_groups_0 = const()[name = string("x_331_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 4096, 1]> x_333_weight_0_to_fp16 = const()[name = string("x_333_weight_0_to_fp16"), val = tensor<fp16, [1024, 4096, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(648429248)))];
            tensor<fp16, [1024]> x_333_bias_0_to_fp16 = const()[name = string("x_333_bias_0_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(656817920)))];
            tensor<fp16, [1, 1024, 96]> x_333_cast_fp16 = conv(bias = x_333_bias_0_to_fp16, dilations = x_331_dilations_0, groups = x_331_groups_0, pad = x_331_pad_0, pad_type = x_331_pad_type_0, strides = x_331_strides_0, weight = x_333_weight_0_to_fp16, x = x_329_cast_fp16)[name = string("x_333_cast_fp16")];
            tensor<fp16, [1, 1024, 96]> x_335_cast_fp16 = add(x = x_319_cast_fp16, y = x_333_cast_fp16)[name = string("x_335_cast_fp16")];
            int32 var_2144 = const()[name = string("op_2144"), val = int32(-1)];
            bool input_49_interleave_0 = const()[name = string("input_49_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 1024, 105]> input_49_cast_fp16 = concat(axis = var_2144, interleave = input_49_interleave_0, values = (var_748_cast_fp16_3, x_335_cast_fp16))[name = string("input_49_cast_fp16")];
            string full_output_5_pad_type_0 = const()[name = string("full_output_5_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> full_output_5_strides_0 = const()[name = string("full_output_5_strides_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [2]> full_output_5_pad_0 = const()[name = string("full_output_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> full_output_5_dilations_0 = const()[name = string("full_output_5_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 full_output_5_groups_0 = const()[name = string("full_output_5_groups_0"), val = int32(1)];
            tensor<int32, [3]> full_output_5_has_output_shape_output_shape_0 = const()[name = string("full_output_5_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 512, 530])];
            tensor<fp16, [1024, 512, 10]> upsample_layers_2_0_convtr_convtr_weight_to_fp16 = const()[name = string("upsample_layers_2_0_convtr_convtr_weight_to_fp16"), val = tensor<fp16, [1024, 512, 10]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(656820032)))];
            tensor<fp16, [512]> upsample_layers_2_0_convtr_convtr_bias_to_fp16 = const()[name = string("upsample_layers_2_0_convtr_convtr_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(667305856)))];
            tensor<fp16, [1, 512, 530]> full_output_5_has_output_shape_cast_fp16 = conv_transpose(bias = upsample_layers_2_0_convtr_convtr_bias_to_fp16, dilations = full_output_5_dilations_0, groups = full_output_5_groups_0, output_shape = full_output_5_has_output_shape_output_shape_0, pad = full_output_5_pad_0, pad_type = full_output_5_pad_type_0, strides = full_output_5_strides_0, weight = upsample_layers_2_0_convtr_convtr_weight_to_fp16, x = input_49_cast_fp16)[name = string("full_output_5_has_output_shape_cast_fp16")];
            tensor<int32, [3]> full_output_7_begin_0 = const()[name = string("full_output_7_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> full_output_7_end_0 = const()[name = string("full_output_7_end_0"), val = tensor<int32, [3]>([1, 512, 525])];
            tensor<bool, [3]> full_output_7_end_mask_0 = const()[name = string("full_output_7_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp16, [1, 512, 525]> full_output_7_cast_fp16 = slice_by_index(begin = full_output_7_begin_0, end = full_output_7_end_0, end_mask = full_output_7_end_mask_0, x = full_output_5_has_output_shape_cast_fp16)[name = string("full_output_7_cast_fp16")];
            tensor<int32, [3]> x_337_begin_0 = const()[name = string("x_337_begin_0"), val = tensor<int32, [3]>([0, 0, 45])];
            tensor<int32, [3]> x_337_end_0 = const()[name = string("x_337_end_0"), val = tensor<int32, [3]>([1, 512, 525])];
            tensor<bool, [3]> x_337_end_mask_0 = const()[name = string("x_337_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 512, 480]> x_337_cast_fp16 = slice_by_index(begin = x_337_begin_0, end = x_337_end_0, end_mask = x_337_end_mask_0, x = full_output_7_cast_fp16)[name = string("x_337_cast_fp16")];
            tensor<int32, [3]> var_2178_begin_0 = const()[name = string("op_2178_begin_0"), val = tensor<int32, [3]>([0, 0, 96])];
            tensor<int32, [3]> var_2178_end_0 = const()[name = string("op_2178_end_0"), val = tensor<int32, [3]>([1, 1024, 105])];
            tensor<bool, [3]> var_2178_end_mask_0 = const()[name = string("op_2178_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 1024, 9]> var_2178_cast_fp16 = slice_by_index(begin = var_2178_begin_0, end = var_2178_end_0, end_mask = var_2178_end_mask_0, x = input_49_cast_fp16)[name = string("op_2178_cast_fp16")];
            int32 var_2183 = const()[name = string("op_2183"), val = int32(1)];
            tensor<int32, [1]> x_339_axes_0 = const()[name = string("x_339_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 512, 1, 480]> x_339_cast_fp16 = expand_dims(axes = x_339_axes_0, x = x_337_cast_fp16)[name = string("x_339_cast_fp16")];
            fp16 const_54_promoted_to_fp16 = const()[name = string("const_54_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 512, 1, 480]> var_2188_cast_fp16 = mul(x = x_339_cast_fp16, y = const_54_promoted_to_fp16)[name = string("op_2188_cast_fp16")];
            bool x_341_interleave_0 = const()[name = string("x_341_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 1024, 1, 480]> x_341_cast_fp16 = concat(axis = var_2183, interleave = x_341_interleave_0, values = (x_339_cast_fp16, var_2188_cast_fp16))[name = string("x_341_cast_fp16")];
            tensor<int32, [1]> out_177_axes_0 = const()[name = string("out_177_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2198_to_fp16 = const()[name = string("op_2198_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1024, 1, 480]> out_177_cast_fp16 = layer_norm(axes = out_177_axes_0, epsilon = var_2198_to_fp16, x = x_341_cast_fp16)[name = string("out_177_cast_fp16")];
            tensor<fp16, [1, 1024, 1, 1]> stages_2_0_norm_weight_to_fp16 = const()[name = string("stages_2_0_norm_weight_to_fp16"), val = tensor<fp16, [1, 1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(667306944)))];
            tensor<fp16, [1, 1024, 1, 480]> out_179_cast_fp16 = mul(x = out_177_cast_fp16, y = stages_2_0_norm_weight_to_fp16)[name = string("out_179_cast_fp16")];
            tensor<int32, [2]> var_2204_split_sizes_0 = const()[name = string("op_2204_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            int32 var_2204_axis_0 = const()[name = string("op_2204_axis_0"), val = int32(1)];
            tensor<fp16, [1, 512, 1, 480]> var_2204_cast_fp16_0, tensor<fp16, [1, 512, 1, 480]> var_2204_cast_fp16_1 = split(axis = var_2204_axis_0, split_sizes = var_2204_split_sizes_0, x = out_179_cast_fp16)[name = string("op_2204_cast_fp16")];
            tensor<int32, [1]> x_345_axes_0 = const()[name = string("x_345_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 512, 480]> x_345_cast_fp16 = squeeze(axes = x_345_axes_0, x = var_2204_cast_fp16_0)[name = string("x_345_cast_fp16")];
            int32 var_2209 = const()[name = string("op_2209"), val = int32(-1)];
            bool input_51_interleave_0 = const()[name = string("input_51_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 512, 486]> input_51_cast_fp16 = concat(axis = var_2209, interleave = input_51_interleave_0, values = (var_759_cast_fp16_0, x_345_cast_fp16))[name = string("input_51_cast_fp16")];
            string x_347_pad_type_0 = const()[name = string("x_347_pad_type_0"), val = string("valid")];
            int32 x_347_groups_0 = const()[name = string("x_347_groups_0"), val = int32(512)];
            tensor<int32, [1]> x_347_strides_0 = const()[name = string("x_347_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_347_pad_0 = const()[name = string("x_347_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_347_dilations_0 = const()[name = string("x_347_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [512, 1, 7]> x_349_weight_0_to_fp16 = const()[name = string("x_349_weight_0_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(667309056)))];
            tensor<fp16, [512]> x_349_bias_0_to_fp16 = const()[name = string("x_349_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(667316288)))];
            tensor<fp16, [1, 512, 480]> x_349_cast_fp16 = conv(bias = x_349_bias_0_to_fp16, dilations = x_347_dilations_0, groups = x_347_groups_0, pad = x_347_pad_0, pad_type = x_347_pad_type_0, strides = x_347_strides_0, weight = x_349_weight_0_to_fp16, x = input_51_cast_fp16)[name = string("x_349_cast_fp16")];
            tensor<int32, [3]> var_2232_begin_0 = const()[name = string("op_2232_begin_0"), val = tensor<int32, [3]>([0, 0, 480])];
            tensor<int32, [3]> var_2232_end_0 = const()[name = string("op_2232_end_0"), val = tensor<int32, [3]>([1, 512, 486])];
            tensor<bool, [3]> var_2232_end_mask_0 = const()[name = string("op_2232_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 512, 6]> var_2232_cast_fp16 = slice_by_index(begin = var_2232_begin_0, end = var_2232_end_0, end_mask = var_2232_end_mask_0, x = input_51_cast_fp16)[name = string("op_2232_cast_fp16")];
            tensor<fp16, [1, 512, 480]> x_351_cast_fp16 = add(x = x_337_cast_fp16, y = x_349_cast_fp16)[name = string("x_351_cast_fp16")];
            int32 var_2242 = const()[name = string("op_2242"), val = int32(1)];
            tensor<int32, [1]> x_353_axes_0 = const()[name = string("x_353_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 512, 1, 480]> x_353_cast_fp16 = expand_dims(axes = x_353_axes_0, x = x_351_cast_fp16)[name = string("x_353_cast_fp16")];
            fp16 const_57_promoted_to_fp16 = const()[name = string("const_57_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 512, 1, 480]> var_2247_cast_fp16 = mul(x = x_353_cast_fp16, y = const_57_promoted_to_fp16)[name = string("op_2247_cast_fp16")];
            bool x_355_interleave_0 = const()[name = string("x_355_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 1024, 1, 480]> x_355_cast_fp16 = concat(axis = var_2242, interleave = x_355_interleave_0, values = (x_353_cast_fp16, var_2247_cast_fp16))[name = string("x_355_cast_fp16")];
            tensor<int32, [1]> out_185_axes_0 = const()[name = string("out_185_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2257_to_fp16 = const()[name = string("op_2257_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1024, 1, 480]> out_185_cast_fp16 = layer_norm(axes = out_185_axes_0, epsilon = var_2257_to_fp16, x = x_355_cast_fp16)[name = string("out_185_cast_fp16")];
            tensor<fp16, [1, 1024, 1, 1]> stages_2_0_ffn_norm_weight_to_fp16 = const()[name = string("stages_2_0_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(667317376)))];
            tensor<fp16, [1, 1024, 1, 480]> out_187_cast_fp16 = mul(x = out_185_cast_fp16, y = stages_2_0_ffn_norm_weight_to_fp16)[name = string("out_187_cast_fp16")];
            tensor<int32, [2]> var_2263_split_sizes_0 = const()[name = string("op_2263_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            int32 var_2263_axis_0 = const()[name = string("op_2263_axis_0"), val = int32(1)];
            tensor<fp16, [1, 512, 1, 480]> var_2263_cast_fp16_0, tensor<fp16, [1, 512, 1, 480]> var_2263_cast_fp16_1 = split(axis = var_2263_axis_0, split_sizes = var_2263_split_sizes_0, x = out_187_cast_fp16)[name = string("op_2263_cast_fp16")];
            tensor<int32, [1]> x_359_axes_0 = const()[name = string("x_359_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 512, 480]> x_359_cast_fp16 = squeeze(axes = x_359_axes_0, x = var_2263_cast_fp16_0)[name = string("x_359_cast_fp16")];
            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 512, 1]> var_2269_to_fp16 = const()[name = string("op_2269_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(667319488)))];
            tensor<fp16, [2048]> stages_2_0_ffn_linear1_bias_to_fp16 = const()[name = string("stages_2_0_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(669416704)))];
            tensor<fp16, [1, 2048, 480]> input_53_cast_fp16 = conv(bias = stages_2_0_ffn_linear1_bias_to_fp16, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = var_2269_to_fp16, x = x_359_cast_fp16)[name = string("input_53_cast_fp16")];
            string x_361_mode_0 = const()[name = string("x_361_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 2048, 480]> x_361_cast_fp16 = gelu(mode = x_361_mode_0, x = input_53_cast_fp16)[name = string("x_361_cast_fp16")];
            string x_363_pad_type_0 = const()[name = string("x_363_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_363_strides_0 = const()[name = string("x_363_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_363_pad_0 = const()[name = string("x_363_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_363_dilations_0 = const()[name = string("x_363_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_363_groups_0 = const()[name = string("x_363_groups_0"), val = int32(1)];
            tensor<fp16, [512, 2048, 1]> x_365_weight_0_to_fp16 = const()[name = string("x_365_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(669420864)))];
            tensor<fp16, [512]> x_365_bias_0_to_fp16 = const()[name = string("x_365_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(671518080)))];
            tensor<fp16, [1, 512, 480]> x_365_cast_fp16 = conv(bias = x_365_bias_0_to_fp16, dilations = x_363_dilations_0, groups = x_363_groups_0, pad = x_363_pad_0, pad_type = x_363_pad_type_0, strides = x_363_strides_0, weight = x_365_weight_0_to_fp16, x = x_361_cast_fp16)[name = string("x_365_cast_fp16")];
            tensor<fp16, [1, 512, 480]> x_367_cast_fp16 = add(x = x_351_cast_fp16, y = x_365_cast_fp16)[name = string("x_367_cast_fp16")];
            int32 var_2298 = const()[name = string("op_2298"), val = int32(1)];
            tensor<int32, [1]> x_369_axes_0 = const()[name = string("x_369_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 512, 1, 480]> x_369_cast_fp16 = expand_dims(axes = x_369_axes_0, x = x_367_cast_fp16)[name = string("x_369_cast_fp16")];
            fp16 const_58_promoted_to_fp16 = const()[name = string("const_58_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 512, 1, 480]> var_2303_cast_fp16 = mul(x = x_369_cast_fp16, y = const_58_promoted_to_fp16)[name = string("op_2303_cast_fp16")];
            bool x_371_interleave_0 = const()[name = string("x_371_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 1024, 1, 480]> x_371_cast_fp16 = concat(axis = var_2298, interleave = x_371_interleave_0, values = (x_369_cast_fp16, var_2303_cast_fp16))[name = string("x_371_cast_fp16")];
            tensor<int32, [1]> out_193_axes_0 = const()[name = string("out_193_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2313_to_fp16 = const()[name = string("op_2313_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1024, 1, 480]> out_193_cast_fp16 = layer_norm(axes = out_193_axes_0, epsilon = var_2313_to_fp16, x = x_371_cast_fp16)[name = string("out_193_cast_fp16")];
            tensor<fp16, [1, 1024, 1, 1]> stages_2_1_norm_weight_to_fp16 = const()[name = string("stages_2_1_norm_weight_to_fp16"), val = tensor<fp16, [1, 1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(671519168)))];
            tensor<fp16, [1, 1024, 1, 480]> out_195_cast_fp16 = mul(x = out_193_cast_fp16, y = stages_2_1_norm_weight_to_fp16)[name = string("out_195_cast_fp16")];
            tensor<int32, [2]> var_2319_split_sizes_0 = const()[name = string("op_2319_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            int32 var_2319_axis_0 = const()[name = string("op_2319_axis_0"), val = int32(1)];
            tensor<fp16, [1, 512, 1, 480]> var_2319_cast_fp16_0, tensor<fp16, [1, 512, 1, 480]> var_2319_cast_fp16_1 = split(axis = var_2319_axis_0, split_sizes = var_2319_split_sizes_0, x = out_195_cast_fp16)[name = string("op_2319_cast_fp16")];
            tensor<int32, [1]> x_375_axes_0 = const()[name = string("x_375_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 512, 480]> x_375_cast_fp16 = squeeze(axes = x_375_axes_0, x = var_2319_cast_fp16_0)[name = string("x_375_cast_fp16")];
            int32 var_2324 = const()[name = string("op_2324"), val = int32(-1)];
            bool input_55_interleave_0 = const()[name = string("input_55_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 512, 486]> input_55_cast_fp16 = concat(axis = var_2324, interleave = input_55_interleave_0, values = (var_759_cast_fp16_1, x_375_cast_fp16))[name = string("input_55_cast_fp16")];
            string x_377_pad_type_0 = const()[name = string("x_377_pad_type_0"), val = string("valid")];
            int32 x_377_groups_0 = const()[name = string("x_377_groups_0"), val = int32(512)];
            tensor<int32, [1]> x_377_strides_0 = const()[name = string("x_377_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_377_pad_0 = const()[name = string("x_377_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_377_dilations_0 = const()[name = string("x_377_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [512, 1, 7]> x_379_weight_0_to_fp16 = const()[name = string("x_379_weight_0_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(671521280)))];
            tensor<fp16, [512]> x_379_bias_0_to_fp16 = const()[name = string("x_379_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(671528512)))];
            tensor<fp16, [1, 512, 480]> x_379_cast_fp16 = conv(bias = x_379_bias_0_to_fp16, dilations = x_377_dilations_0, groups = x_377_groups_0, pad = x_377_pad_0, pad_type = x_377_pad_type_0, strides = x_377_strides_0, weight = x_379_weight_0_to_fp16, x = input_55_cast_fp16)[name = string("x_379_cast_fp16")];
            tensor<int32, [3]> var_2347_begin_0 = const()[name = string("op_2347_begin_0"), val = tensor<int32, [3]>([0, 0, 480])];
            tensor<int32, [3]> var_2347_end_0 = const()[name = string("op_2347_end_0"), val = tensor<int32, [3]>([1, 512, 486])];
            tensor<bool, [3]> var_2347_end_mask_0 = const()[name = string("op_2347_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 512, 6]> var_2347_cast_fp16 = slice_by_index(begin = var_2347_begin_0, end = var_2347_end_0, end_mask = var_2347_end_mask_0, x = input_55_cast_fp16)[name = string("op_2347_cast_fp16")];
            tensor<fp16, [1, 512, 480]> x_381_cast_fp16 = add(x = x_367_cast_fp16, y = x_379_cast_fp16)[name = string("x_381_cast_fp16")];
            int32 var_2357 = const()[name = string("op_2357"), val = int32(1)];
            tensor<int32, [1]> x_383_axes_0 = const()[name = string("x_383_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 512, 1, 480]> x_383_cast_fp16 = expand_dims(axes = x_383_axes_0, x = x_381_cast_fp16)[name = string("x_383_cast_fp16")];
            fp16 const_61_promoted_to_fp16 = const()[name = string("const_61_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 512, 1, 480]> var_2362_cast_fp16 = mul(x = x_383_cast_fp16, y = const_61_promoted_to_fp16)[name = string("op_2362_cast_fp16")];
            bool x_385_interleave_0 = const()[name = string("x_385_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 1024, 1, 480]> x_385_cast_fp16 = concat(axis = var_2357, interleave = x_385_interleave_0, values = (x_383_cast_fp16, var_2362_cast_fp16))[name = string("x_385_cast_fp16")];
            tensor<int32, [1]> out_201_axes_0 = const()[name = string("out_201_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2372_to_fp16 = const()[name = string("op_2372_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1024, 1, 480]> out_201_cast_fp16 = layer_norm(axes = out_201_axes_0, epsilon = var_2372_to_fp16, x = x_385_cast_fp16)[name = string("out_201_cast_fp16")];
            tensor<fp16, [1, 1024, 1, 1]> stages_2_1_ffn_norm_weight_to_fp16 = const()[name = string("stages_2_1_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(671529600)))];
            tensor<fp16, [1, 1024, 1, 480]> out_203_cast_fp16 = mul(x = out_201_cast_fp16, y = stages_2_1_ffn_norm_weight_to_fp16)[name = string("out_203_cast_fp16")];
            tensor<int32, [2]> var_2378_split_sizes_0 = const()[name = string("op_2378_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            int32 var_2378_axis_0 = const()[name = string("op_2378_axis_0"), val = int32(1)];
            tensor<fp16, [1, 512, 1, 480]> var_2378_cast_fp16_0, tensor<fp16, [1, 512, 1, 480]> var_2378_cast_fp16_1 = split(axis = var_2378_axis_0, split_sizes = var_2378_split_sizes_0, x = out_203_cast_fp16)[name = string("op_2378_cast_fp16")];
            tensor<int32, [1]> x_389_axes_0 = const()[name = string("x_389_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 512, 480]> x_389_cast_fp16 = squeeze(axes = x_389_axes_0, x = var_2378_cast_fp16_0)[name = string("x_389_cast_fp16")];
            string input_57_pad_type_0 = const()[name = string("input_57_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_57_strides_0 = const()[name = string("input_57_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_57_dilations_0 = const()[name = string("input_57_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_57_groups_0 = const()[name = string("input_57_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 512, 1]> var_2384_to_fp16 = const()[name = string("op_2384_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(671531712)))];
            tensor<fp16, [2048]> stages_2_1_ffn_linear1_bias_to_fp16 = const()[name = string("stages_2_1_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(673628928)))];
            tensor<fp16, [1, 2048, 480]> input_57_cast_fp16 = conv(bias = stages_2_1_ffn_linear1_bias_to_fp16, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = var_2384_to_fp16, x = x_389_cast_fp16)[name = string("input_57_cast_fp16")];
            string x_391_mode_0 = const()[name = string("x_391_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 2048, 480]> x_391_cast_fp16 = gelu(mode = x_391_mode_0, x = input_57_cast_fp16)[name = string("x_391_cast_fp16")];
            string x_393_pad_type_0 = const()[name = string("x_393_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_393_strides_0 = const()[name = string("x_393_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_393_pad_0 = const()[name = string("x_393_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_393_dilations_0 = const()[name = string("x_393_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_393_groups_0 = const()[name = string("x_393_groups_0"), val = int32(1)];
            tensor<fp16, [512, 2048, 1]> x_395_weight_0_to_fp16 = const()[name = string("x_395_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(673633088)))];
            tensor<fp16, [512]> x_395_bias_0_to_fp16 = const()[name = string("x_395_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(675730304)))];
            tensor<fp16, [1, 512, 480]> x_395_cast_fp16 = conv(bias = x_395_bias_0_to_fp16, dilations = x_393_dilations_0, groups = x_393_groups_0, pad = x_393_pad_0, pad_type = x_393_pad_type_0, strides = x_393_strides_0, weight = x_395_weight_0_to_fp16, x = x_391_cast_fp16)[name = string("x_395_cast_fp16")];
            tensor<fp16, [1, 512, 480]> x_397_cast_fp16 = add(x = x_381_cast_fp16, y = x_395_cast_fp16)[name = string("x_397_cast_fp16")];
            int32 var_2413 = const()[name = string("op_2413"), val = int32(1)];
            tensor<int32, [1]> x_399_axes_0 = const()[name = string("x_399_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 512, 1, 480]> x_399_cast_fp16 = expand_dims(axes = x_399_axes_0, x = x_397_cast_fp16)[name = string("x_399_cast_fp16")];
            fp16 const_62_promoted_to_fp16 = const()[name = string("const_62_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 512, 1, 480]> var_2418_cast_fp16 = mul(x = x_399_cast_fp16, y = const_62_promoted_to_fp16)[name = string("op_2418_cast_fp16")];
            bool x_401_interleave_0 = const()[name = string("x_401_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 1024, 1, 480]> x_401_cast_fp16 = concat(axis = var_2413, interleave = x_401_interleave_0, values = (x_399_cast_fp16, var_2418_cast_fp16))[name = string("x_401_cast_fp16")];
            tensor<int32, [1]> out_209_axes_0 = const()[name = string("out_209_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2428_to_fp16 = const()[name = string("op_2428_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1024, 1, 480]> out_209_cast_fp16 = layer_norm(axes = out_209_axes_0, epsilon = var_2428_to_fp16, x = x_401_cast_fp16)[name = string("out_209_cast_fp16")];
            tensor<fp16, [1, 1024, 1, 1]> stages_2_2_norm_weight_to_fp16 = const()[name = string("stages_2_2_norm_weight_to_fp16"), val = tensor<fp16, [1, 1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(675731392)))];
            tensor<fp16, [1, 1024, 1, 480]> out_211_cast_fp16 = mul(x = out_209_cast_fp16, y = stages_2_2_norm_weight_to_fp16)[name = string("out_211_cast_fp16")];
            tensor<int32, [2]> var_2434_split_sizes_0 = const()[name = string("op_2434_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            int32 var_2434_axis_0 = const()[name = string("op_2434_axis_0"), val = int32(1)];
            tensor<fp16, [1, 512, 1, 480]> var_2434_cast_fp16_0, tensor<fp16, [1, 512, 1, 480]> var_2434_cast_fp16_1 = split(axis = var_2434_axis_0, split_sizes = var_2434_split_sizes_0, x = out_211_cast_fp16)[name = string("op_2434_cast_fp16")];
            tensor<int32, [1]> x_405_axes_0 = const()[name = string("x_405_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 512, 480]> x_405_cast_fp16 = squeeze(axes = x_405_axes_0, x = var_2434_cast_fp16_0)[name = string("x_405_cast_fp16")];
            int32 var_2439 = const()[name = string("op_2439"), val = int32(-1)];
            bool input_59_interleave_0 = const()[name = string("input_59_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 512, 486]> input_59_cast_fp16 = concat(axis = var_2439, interleave = input_59_interleave_0, values = (var_759_cast_fp16_2, x_405_cast_fp16))[name = string("input_59_cast_fp16")];
            string x_407_pad_type_0 = const()[name = string("x_407_pad_type_0"), val = string("valid")];
            int32 x_407_groups_0 = const()[name = string("x_407_groups_0"), val = int32(512)];
            tensor<int32, [1]> x_407_strides_0 = const()[name = string("x_407_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_407_pad_0 = const()[name = string("x_407_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_407_dilations_0 = const()[name = string("x_407_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [512, 1, 7]> x_409_weight_0_to_fp16 = const()[name = string("x_409_weight_0_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(675733504)))];
            tensor<fp16, [512]> x_409_bias_0_to_fp16 = const()[name = string("x_409_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(675740736)))];
            tensor<fp16, [1, 512, 480]> x_409_cast_fp16 = conv(bias = x_409_bias_0_to_fp16, dilations = x_407_dilations_0, groups = x_407_groups_0, pad = x_407_pad_0, pad_type = x_407_pad_type_0, strides = x_407_strides_0, weight = x_409_weight_0_to_fp16, x = input_59_cast_fp16)[name = string("x_409_cast_fp16")];
            tensor<int32, [3]> var_2462_begin_0 = const()[name = string("op_2462_begin_0"), val = tensor<int32, [3]>([0, 0, 480])];
            tensor<int32, [3]> var_2462_end_0 = const()[name = string("op_2462_end_0"), val = tensor<int32, [3]>([1, 512, 486])];
            tensor<bool, [3]> var_2462_end_mask_0 = const()[name = string("op_2462_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 512, 6]> var_2462_cast_fp16 = slice_by_index(begin = var_2462_begin_0, end = var_2462_end_0, end_mask = var_2462_end_mask_0, x = input_59_cast_fp16)[name = string("op_2462_cast_fp16")];
            tensor<fp16, [1, 512, 480]> x_411_cast_fp16 = add(x = x_397_cast_fp16, y = x_409_cast_fp16)[name = string("x_411_cast_fp16")];
            int32 var_2472 = const()[name = string("op_2472"), val = int32(1)];
            tensor<int32, [1]> x_413_axes_0 = const()[name = string("x_413_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 512, 1, 480]> x_413_cast_fp16 = expand_dims(axes = x_413_axes_0, x = x_411_cast_fp16)[name = string("x_413_cast_fp16")];
            fp16 const_65_promoted_to_fp16 = const()[name = string("const_65_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 512, 1, 480]> var_2477_cast_fp16 = mul(x = x_413_cast_fp16, y = const_65_promoted_to_fp16)[name = string("op_2477_cast_fp16")];
            bool x_415_interleave_0 = const()[name = string("x_415_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 1024, 1, 480]> x_415_cast_fp16 = concat(axis = var_2472, interleave = x_415_interleave_0, values = (x_413_cast_fp16, var_2477_cast_fp16))[name = string("x_415_cast_fp16")];
            tensor<int32, [1]> out_217_axes_0 = const()[name = string("out_217_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2487_to_fp16 = const()[name = string("op_2487_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 1024, 1, 480]> out_217_cast_fp16 = layer_norm(axes = out_217_axes_0, epsilon = var_2487_to_fp16, x = x_415_cast_fp16)[name = string("out_217_cast_fp16")];
            tensor<fp16, [1, 1024, 1, 1]> stages_2_2_ffn_norm_weight_to_fp16 = const()[name = string("stages_2_2_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 1024, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(675741824)))];
            tensor<fp16, [1, 1024, 1, 480]> out_219_cast_fp16 = mul(x = out_217_cast_fp16, y = stages_2_2_ffn_norm_weight_to_fp16)[name = string("out_219_cast_fp16")];
            tensor<int32, [2]> var_2493_split_sizes_0 = const()[name = string("op_2493_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
            int32 var_2493_axis_0 = const()[name = string("op_2493_axis_0"), val = int32(1)];
            tensor<fp16, [1, 512, 1, 480]> var_2493_cast_fp16_0, tensor<fp16, [1, 512, 1, 480]> var_2493_cast_fp16_1 = split(axis = var_2493_axis_0, split_sizes = var_2493_split_sizes_0, x = out_219_cast_fp16)[name = string("op_2493_cast_fp16")];
            tensor<int32, [1]> x_419_axes_0 = const()[name = string("x_419_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 512, 480]> x_419_cast_fp16 = squeeze(axes = x_419_axes_0, x = var_2493_cast_fp16_0)[name = string("x_419_cast_fp16")];
            string input_61_pad_type_0 = const()[name = string("input_61_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_61_strides_0 = const()[name = string("input_61_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_61_dilations_0 = const()[name = string("input_61_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_61_groups_0 = const()[name = string("input_61_groups_0"), val = int32(1)];
            tensor<fp16, [2048, 512, 1]> var_2499_to_fp16 = const()[name = string("op_2499_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(675743936)))];
            tensor<fp16, [2048]> stages_2_2_ffn_linear1_bias_to_fp16 = const()[name = string("stages_2_2_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(677841152)))];
            tensor<fp16, [1, 2048, 480]> input_61_cast_fp16 = conv(bias = stages_2_2_ffn_linear1_bias_to_fp16, dilations = input_61_dilations_0, groups = input_61_groups_0, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = input_61_strides_0, weight = var_2499_to_fp16, x = x_419_cast_fp16)[name = string("input_61_cast_fp16")];
            string x_421_mode_0 = const()[name = string("x_421_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 2048, 480]> x_421_cast_fp16 = gelu(mode = x_421_mode_0, x = input_61_cast_fp16)[name = string("x_421_cast_fp16")];
            string x_423_pad_type_0 = const()[name = string("x_423_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_423_strides_0 = const()[name = string("x_423_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_423_pad_0 = const()[name = string("x_423_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_423_dilations_0 = const()[name = string("x_423_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_423_groups_0 = const()[name = string("x_423_groups_0"), val = int32(1)];
            tensor<fp16, [512, 2048, 1]> x_425_weight_0_to_fp16 = const()[name = string("x_425_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(677845312)))];
            tensor<fp16, [512]> x_425_bias_0_to_fp16 = const()[name = string("x_425_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(679942528)))];
            tensor<fp16, [1, 512, 480]> x_425_cast_fp16 = conv(bias = x_425_bias_0_to_fp16, dilations = x_423_dilations_0, groups = x_423_groups_0, pad = x_423_pad_0, pad_type = x_423_pad_type_0, strides = x_423_strides_0, weight = x_425_weight_0_to_fp16, x = x_421_cast_fp16)[name = string("x_425_cast_fp16")];
            tensor<fp16, [1, 512, 480]> x_427_cast_fp16 = add(x = x_411_cast_fp16, y = x_425_cast_fp16)[name = string("x_427_cast_fp16")];
            int32 var_2530 = const()[name = string("op_2530"), val = int32(-1)];
            bool input_63_interleave_0 = const()[name = string("input_63_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 512, 489]> input_63_cast_fp16 = concat(axis = var_2530, interleave = input_63_interleave_0, values = (var_759_cast_fp16_3, x_427_cast_fp16))[name = string("input_63_cast_fp16")];
            string full_output_9_pad_type_0 = const()[name = string("full_output_9_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> full_output_9_strides_0 = const()[name = string("full_output_9_strides_0"), val = tensor<int32, [1]>([5])];
            tensor<int32, [2]> full_output_9_pad_0 = const()[name = string("full_output_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> full_output_9_dilations_0 = const()[name = string("full_output_9_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 full_output_9_groups_0 = const()[name = string("full_output_9_groups_0"), val = int32(1)];
            tensor<int32, [3]> full_output_9_has_output_shape_output_shape_0 = const()[name = string("full_output_9_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 256, 2450])];
            tensor<fp16, [512, 256, 10]> upsample_layers_3_0_convtr_convtr_weight_to_fp16 = const()[name = string("upsample_layers_3_0_convtr_convtr_weight_to_fp16"), val = tensor<fp16, [512, 256, 10]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(679943616)))];
            tensor<fp16, [256]> upsample_layers_3_0_convtr_convtr_bias_to_fp16 = const()[name = string("upsample_layers_3_0_convtr_convtr_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(682565120)))];
            tensor<fp16, [1, 256, 2450]> full_output_9_has_output_shape_cast_fp16 = conv_transpose(bias = upsample_layers_3_0_convtr_convtr_bias_to_fp16, dilations = full_output_9_dilations_0, groups = full_output_9_groups_0, output_shape = full_output_9_has_output_shape_output_shape_0, pad = full_output_9_pad_0, pad_type = full_output_9_pad_type_0, strides = full_output_9_strides_0, weight = upsample_layers_3_0_convtr_convtr_weight_to_fp16, x = input_63_cast_fp16)[name = string("full_output_9_has_output_shape_cast_fp16")];
            tensor<int32, [3]> full_output_11_begin_0 = const()[name = string("full_output_11_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> full_output_11_end_0 = const()[name = string("full_output_11_end_0"), val = tensor<int32, [3]>([1, 256, 2445])];
            tensor<bool, [3]> full_output_11_end_mask_0 = const()[name = string("full_output_11_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp16, [1, 256, 2445]> full_output_11_cast_fp16 = slice_by_index(begin = full_output_11_begin_0, end = full_output_11_end_0, end_mask = full_output_11_end_mask_0, x = full_output_9_has_output_shape_cast_fp16)[name = string("full_output_11_cast_fp16")];
            tensor<int32, [3]> x_429_begin_0 = const()[name = string("x_429_begin_0"), val = tensor<int32, [3]>([0, 0, 45])];
            tensor<int32, [3]> x_429_end_0 = const()[name = string("x_429_end_0"), val = tensor<int32, [3]>([1, 256, 2445])];
            tensor<bool, [3]> x_429_end_mask_0 = const()[name = string("x_429_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 256, 2400]> x_429_cast_fp16 = slice_by_index(begin = x_429_begin_0, end = x_429_end_0, end_mask = x_429_end_mask_0, x = full_output_11_cast_fp16)[name = string("x_429_cast_fp16")];
            tensor<int32, [3]> var_2564_begin_0 = const()[name = string("op_2564_begin_0"), val = tensor<int32, [3]>([0, 0, 480])];
            tensor<int32, [3]> var_2564_end_0 = const()[name = string("op_2564_end_0"), val = tensor<int32, [3]>([1, 512, 489])];
            tensor<bool, [3]> var_2564_end_mask_0 = const()[name = string("op_2564_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 512, 9]> var_2564_cast_fp16 = slice_by_index(begin = var_2564_begin_0, end = var_2564_end_0, end_mask = var_2564_end_mask_0, x = input_63_cast_fp16)[name = string("op_2564_cast_fp16")];
            int32 var_2569 = const()[name = string("op_2569"), val = int32(1)];
            tensor<int32, [1]> x_431_axes_0 = const()[name = string("x_431_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 256, 1, 2400]> x_431_cast_fp16 = expand_dims(axes = x_431_axes_0, x = x_429_cast_fp16)[name = string("x_431_cast_fp16")];
            fp16 const_70_promoted_to_fp16 = const()[name = string("const_70_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 256, 1, 2400]> var_2574_cast_fp16 = mul(x = x_431_cast_fp16, y = const_70_promoted_to_fp16)[name = string("op_2574_cast_fp16")];
            bool x_433_interleave_0 = const()[name = string("x_433_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 512, 1, 2400]> x_433_cast_fp16 = concat(axis = var_2569, interleave = x_433_interleave_0, values = (x_431_cast_fp16, var_2574_cast_fp16))[name = string("x_433_cast_fp16")];
            tensor<int32, [1]> out_225_axes_0 = const()[name = string("out_225_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2584_to_fp16 = const()[name = string("op_2584_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 512, 1, 2400]> out_225_cast_fp16 = layer_norm(axes = out_225_axes_0, epsilon = var_2584_to_fp16, x = x_433_cast_fp16)[name = string("out_225_cast_fp16")];
            tensor<fp16, [1, 512, 1, 1]> stages_3_0_norm_weight_to_fp16 = const()[name = string("stages_3_0_norm_weight_to_fp16"), val = tensor<fp16, [1, 512, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(682565696)))];
            tensor<fp16, [1, 512, 1, 2400]> out_227_cast_fp16 = mul(x = out_225_cast_fp16, y = stages_3_0_norm_weight_to_fp16)[name = string("out_227_cast_fp16")];
            tensor<int32, [2]> var_2590_split_sizes_0 = const()[name = string("op_2590_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            int32 var_2590_axis_0 = const()[name = string("op_2590_axis_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 2400]> var_2590_cast_fp16_0, tensor<fp16, [1, 256, 1, 2400]> var_2590_cast_fp16_1 = split(axis = var_2590_axis_0, split_sizes = var_2590_split_sizes_0, x = out_227_cast_fp16)[name = string("op_2590_cast_fp16")];
            tensor<int32, [1]> x_437_axes_0 = const()[name = string("x_437_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 256, 2400]> x_437_cast_fp16 = squeeze(axes = x_437_axes_0, x = var_2590_cast_fp16_0)[name = string("x_437_cast_fp16")];
            int32 var_2595 = const()[name = string("op_2595"), val = int32(-1)];
            bool input_65_interleave_0 = const()[name = string("input_65_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 256, 2406]> input_65_cast_fp16 = concat(axis = var_2595, interleave = input_65_interleave_0, values = (var_770_cast_fp16_0, x_437_cast_fp16))[name = string("input_65_cast_fp16")];
            string x_439_pad_type_0 = const()[name = string("x_439_pad_type_0"), val = string("valid")];
            int32 x_439_groups_0 = const()[name = string("x_439_groups_0"), val = int32(256)];
            tensor<int32, [1]> x_439_strides_0 = const()[name = string("x_439_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_439_pad_0 = const()[name = string("x_439_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_439_dilations_0 = const()[name = string("x_439_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> x_441_weight_0_to_fp16 = const()[name = string("x_441_weight_0_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(682566784)))];
            tensor<fp16, [256]> x_441_bias_0_to_fp16 = const()[name = string("x_441_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(682570432)))];
            tensor<fp16, [1, 256, 2400]> x_441_cast_fp16 = conv(bias = x_441_bias_0_to_fp16, dilations = x_439_dilations_0, groups = x_439_groups_0, pad = x_439_pad_0, pad_type = x_439_pad_type_0, strides = x_439_strides_0, weight = x_441_weight_0_to_fp16, x = input_65_cast_fp16)[name = string("x_441_cast_fp16")];
            tensor<int32, [3]> var_2618_begin_0 = const()[name = string("op_2618_begin_0"), val = tensor<int32, [3]>([0, 0, 2400])];
            tensor<int32, [3]> var_2618_end_0 = const()[name = string("op_2618_end_0"), val = tensor<int32, [3]>([1, 256, 2406])];
            tensor<bool, [3]> var_2618_end_mask_0 = const()[name = string("op_2618_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 256, 6]> var_2618_cast_fp16 = slice_by_index(begin = var_2618_begin_0, end = var_2618_end_0, end_mask = var_2618_end_mask_0, x = input_65_cast_fp16)[name = string("op_2618_cast_fp16")];
            tensor<fp16, [1, 256, 2400]> x_443_cast_fp16 = add(x = x_429_cast_fp16, y = x_441_cast_fp16)[name = string("x_443_cast_fp16")];
            int32 var_2628 = const()[name = string("op_2628"), val = int32(1)];
            tensor<int32, [1]> x_445_axes_0 = const()[name = string("x_445_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 256, 1, 2400]> x_445_cast_fp16 = expand_dims(axes = x_445_axes_0, x = x_443_cast_fp16)[name = string("x_445_cast_fp16")];
            fp16 const_73_promoted_to_fp16 = const()[name = string("const_73_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 256, 1, 2400]> var_2633_cast_fp16 = mul(x = x_445_cast_fp16, y = const_73_promoted_to_fp16)[name = string("op_2633_cast_fp16")];
            bool x_447_interleave_0 = const()[name = string("x_447_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 512, 1, 2400]> x_447_cast_fp16 = concat(axis = var_2628, interleave = x_447_interleave_0, values = (x_445_cast_fp16, var_2633_cast_fp16))[name = string("x_447_cast_fp16")];
            tensor<int32, [1]> out_233_axes_0 = const()[name = string("out_233_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2643_to_fp16 = const()[name = string("op_2643_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 512, 1, 2400]> out_233_cast_fp16 = layer_norm(axes = out_233_axes_0, epsilon = var_2643_to_fp16, x = x_447_cast_fp16)[name = string("out_233_cast_fp16")];
            tensor<fp16, [1, 512, 1, 1]> stages_3_0_ffn_norm_weight_to_fp16 = const()[name = string("stages_3_0_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 512, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(682571008)))];
            tensor<fp16, [1, 512, 1, 2400]> out_235_cast_fp16 = mul(x = out_233_cast_fp16, y = stages_3_0_ffn_norm_weight_to_fp16)[name = string("out_235_cast_fp16")];
            tensor<int32, [2]> var_2649_split_sizes_0 = const()[name = string("op_2649_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            int32 var_2649_axis_0 = const()[name = string("op_2649_axis_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 2400]> var_2649_cast_fp16_0, tensor<fp16, [1, 256, 1, 2400]> var_2649_cast_fp16_1 = split(axis = var_2649_axis_0, split_sizes = var_2649_split_sizes_0, x = out_235_cast_fp16)[name = string("op_2649_cast_fp16")];
            tensor<int32, [1]> x_451_axes_0 = const()[name = string("x_451_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 256, 2400]> x_451_cast_fp16 = squeeze(axes = x_451_axes_0, x = var_2649_cast_fp16_0)[name = string("x_451_cast_fp16")];
            string input_67_pad_type_0 = const()[name = string("input_67_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_67_strides_0 = const()[name = string("input_67_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_67_pad_0 = const()[name = string("input_67_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_67_dilations_0 = const()[name = string("input_67_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_67_groups_0 = const()[name = string("input_67_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 256, 1]> var_2655_to_fp16 = const()[name = string("op_2655_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(682572096)))];
            tensor<fp16, [1024]> stages_3_0_ffn_linear1_bias_to_fp16 = const()[name = string("stages_3_0_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(683096448)))];
            tensor<fp16, [1, 1024, 2400]> input_67_cast_fp16 = conv(bias = stages_3_0_ffn_linear1_bias_to_fp16, dilations = input_67_dilations_0, groups = input_67_groups_0, pad = input_67_pad_0, pad_type = input_67_pad_type_0, strides = input_67_strides_0, weight = var_2655_to_fp16, x = x_451_cast_fp16)[name = string("input_67_cast_fp16")];
            string x_453_mode_0 = const()[name = string("x_453_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 1024, 2400]> x_453_cast_fp16 = gelu(mode = x_453_mode_0, x = input_67_cast_fp16)[name = string("x_453_cast_fp16")];
            string x_455_pad_type_0 = const()[name = string("x_455_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_455_strides_0 = const()[name = string("x_455_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_455_pad_0 = const()[name = string("x_455_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_455_dilations_0 = const()[name = string("x_455_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_455_groups_0 = const()[name = string("x_455_groups_0"), val = int32(1)];
            tensor<fp16, [256, 1024, 1]> x_457_weight_0_to_fp16 = const()[name = string("x_457_weight_0_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(683098560)))];
            tensor<fp16, [256]> x_457_bias_0_to_fp16 = const()[name = string("x_457_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(683622912)))];
            tensor<fp16, [1, 256, 2400]> x_457_cast_fp16 = conv(bias = x_457_bias_0_to_fp16, dilations = x_455_dilations_0, groups = x_455_groups_0, pad = x_455_pad_0, pad_type = x_455_pad_type_0, strides = x_455_strides_0, weight = x_457_weight_0_to_fp16, x = x_453_cast_fp16)[name = string("x_457_cast_fp16")];
            tensor<fp16, [1, 256, 2400]> x_459_cast_fp16 = add(x = x_443_cast_fp16, y = x_457_cast_fp16)[name = string("x_459_cast_fp16")];
            int32 var_2684 = const()[name = string("op_2684"), val = int32(1)];
            tensor<int32, [1]> x_461_axes_0 = const()[name = string("x_461_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 256, 1, 2400]> x_461_cast_fp16 = expand_dims(axes = x_461_axes_0, x = x_459_cast_fp16)[name = string("x_461_cast_fp16")];
            fp16 const_74_promoted_to_fp16 = const()[name = string("const_74_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 256, 1, 2400]> var_2689_cast_fp16 = mul(x = x_461_cast_fp16, y = const_74_promoted_to_fp16)[name = string("op_2689_cast_fp16")];
            bool x_463_interleave_0 = const()[name = string("x_463_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 512, 1, 2400]> x_463_cast_fp16 = concat(axis = var_2684, interleave = x_463_interleave_0, values = (x_461_cast_fp16, var_2689_cast_fp16))[name = string("x_463_cast_fp16")];
            tensor<int32, [1]> out_241_axes_0 = const()[name = string("out_241_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2699_to_fp16 = const()[name = string("op_2699_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 512, 1, 2400]> out_241_cast_fp16 = layer_norm(axes = out_241_axes_0, epsilon = var_2699_to_fp16, x = x_463_cast_fp16)[name = string("out_241_cast_fp16")];
            tensor<fp16, [1, 512, 1, 1]> stages_3_1_norm_weight_to_fp16 = const()[name = string("stages_3_1_norm_weight_to_fp16"), val = tensor<fp16, [1, 512, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(683623488)))];
            tensor<fp16, [1, 512, 1, 2400]> out_243_cast_fp16 = mul(x = out_241_cast_fp16, y = stages_3_1_norm_weight_to_fp16)[name = string("out_243_cast_fp16")];
            tensor<int32, [2]> var_2705_split_sizes_0 = const()[name = string("op_2705_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            int32 var_2705_axis_0 = const()[name = string("op_2705_axis_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 2400]> var_2705_cast_fp16_0, tensor<fp16, [1, 256, 1, 2400]> var_2705_cast_fp16_1 = split(axis = var_2705_axis_0, split_sizes = var_2705_split_sizes_0, x = out_243_cast_fp16)[name = string("op_2705_cast_fp16")];
            tensor<int32, [1]> x_467_axes_0 = const()[name = string("x_467_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 256, 2400]> x_467_cast_fp16 = squeeze(axes = x_467_axes_0, x = var_2705_cast_fp16_0)[name = string("x_467_cast_fp16")];
            int32 var_2710 = const()[name = string("op_2710"), val = int32(-1)];
            bool input_69_interleave_0 = const()[name = string("input_69_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 256, 2406]> input_69_cast_fp16 = concat(axis = var_2710, interleave = input_69_interleave_0, values = (var_770_cast_fp16_1, x_467_cast_fp16))[name = string("input_69_cast_fp16")];
            string x_469_pad_type_0 = const()[name = string("x_469_pad_type_0"), val = string("valid")];
            int32 x_469_groups_0 = const()[name = string("x_469_groups_0"), val = int32(256)];
            tensor<int32, [1]> x_469_strides_0 = const()[name = string("x_469_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_469_pad_0 = const()[name = string("x_469_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_469_dilations_0 = const()[name = string("x_469_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> x_471_weight_0_to_fp16 = const()[name = string("x_471_weight_0_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(683624576)))];
            tensor<fp16, [256]> x_471_bias_0_to_fp16 = const()[name = string("x_471_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(683628224)))];
            tensor<fp16, [1, 256, 2400]> x_471_cast_fp16 = conv(bias = x_471_bias_0_to_fp16, dilations = x_469_dilations_0, groups = x_469_groups_0, pad = x_469_pad_0, pad_type = x_469_pad_type_0, strides = x_469_strides_0, weight = x_471_weight_0_to_fp16, x = input_69_cast_fp16)[name = string("x_471_cast_fp16")];
            tensor<int32, [3]> var_2733_begin_0 = const()[name = string("op_2733_begin_0"), val = tensor<int32, [3]>([0, 0, 2400])];
            tensor<int32, [3]> var_2733_end_0 = const()[name = string("op_2733_end_0"), val = tensor<int32, [3]>([1, 256, 2406])];
            tensor<bool, [3]> var_2733_end_mask_0 = const()[name = string("op_2733_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 256, 6]> var_2733_cast_fp16 = slice_by_index(begin = var_2733_begin_0, end = var_2733_end_0, end_mask = var_2733_end_mask_0, x = input_69_cast_fp16)[name = string("op_2733_cast_fp16")];
            tensor<fp16, [1, 256, 2400]> x_473_cast_fp16 = add(x = x_459_cast_fp16, y = x_471_cast_fp16)[name = string("x_473_cast_fp16")];
            int32 var_2743 = const()[name = string("op_2743"), val = int32(1)];
            tensor<int32, [1]> x_475_axes_0 = const()[name = string("x_475_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 256, 1, 2400]> x_475_cast_fp16 = expand_dims(axes = x_475_axes_0, x = x_473_cast_fp16)[name = string("x_475_cast_fp16")];
            fp16 const_77_promoted_to_fp16 = const()[name = string("const_77_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 256, 1, 2400]> var_2748_cast_fp16 = mul(x = x_475_cast_fp16, y = const_77_promoted_to_fp16)[name = string("op_2748_cast_fp16")];
            bool x_477_interleave_0 = const()[name = string("x_477_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 512, 1, 2400]> x_477_cast_fp16 = concat(axis = var_2743, interleave = x_477_interleave_0, values = (x_475_cast_fp16, var_2748_cast_fp16))[name = string("x_477_cast_fp16")];
            tensor<int32, [1]> out_249_axes_0 = const()[name = string("out_249_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2758_to_fp16 = const()[name = string("op_2758_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 512, 1, 2400]> out_249_cast_fp16 = layer_norm(axes = out_249_axes_0, epsilon = var_2758_to_fp16, x = x_477_cast_fp16)[name = string("out_249_cast_fp16")];
            tensor<fp16, [1, 512, 1, 1]> stages_3_1_ffn_norm_weight_to_fp16 = const()[name = string("stages_3_1_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 512, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(683628800)))];
            tensor<fp16, [1, 512, 1, 2400]> out_251_cast_fp16 = mul(x = out_249_cast_fp16, y = stages_3_1_ffn_norm_weight_to_fp16)[name = string("out_251_cast_fp16")];
            tensor<int32, [2]> var_2764_split_sizes_0 = const()[name = string("op_2764_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            int32 var_2764_axis_0 = const()[name = string("op_2764_axis_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 2400]> var_2764_cast_fp16_0, tensor<fp16, [1, 256, 1, 2400]> var_2764_cast_fp16_1 = split(axis = var_2764_axis_0, split_sizes = var_2764_split_sizes_0, x = out_251_cast_fp16)[name = string("op_2764_cast_fp16")];
            tensor<int32, [1]> x_481_axes_0 = const()[name = string("x_481_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 256, 2400]> x_481_cast_fp16 = squeeze(axes = x_481_axes_0, x = var_2764_cast_fp16_0)[name = string("x_481_cast_fp16")];
            string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_71_strides_0 = const()[name = string("input_71_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_71_dilations_0 = const()[name = string("input_71_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_71_groups_0 = const()[name = string("input_71_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 256, 1]> var_2770_to_fp16 = const()[name = string("op_2770_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(683629888)))];
            tensor<fp16, [1024]> stages_3_1_ffn_linear1_bias_to_fp16 = const()[name = string("stages_3_1_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(684154240)))];
            tensor<fp16, [1, 1024, 2400]> input_71_cast_fp16 = conv(bias = stages_3_1_ffn_linear1_bias_to_fp16, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = var_2770_to_fp16, x = x_481_cast_fp16)[name = string("input_71_cast_fp16")];
            string x_483_mode_0 = const()[name = string("x_483_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 1024, 2400]> x_483_cast_fp16 = gelu(mode = x_483_mode_0, x = input_71_cast_fp16)[name = string("x_483_cast_fp16")];
            string x_485_pad_type_0 = const()[name = string("x_485_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_485_strides_0 = const()[name = string("x_485_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_485_pad_0 = const()[name = string("x_485_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_485_dilations_0 = const()[name = string("x_485_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_485_groups_0 = const()[name = string("x_485_groups_0"), val = int32(1)];
            tensor<fp16, [256, 1024, 1]> x_487_weight_0_to_fp16 = const()[name = string("x_487_weight_0_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(684156352)))];
            tensor<fp16, [256]> x_487_bias_0_to_fp16 = const()[name = string("x_487_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(684680704)))];
            tensor<fp16, [1, 256, 2400]> x_487_cast_fp16 = conv(bias = x_487_bias_0_to_fp16, dilations = x_485_dilations_0, groups = x_485_groups_0, pad = x_485_pad_0, pad_type = x_485_pad_type_0, strides = x_485_strides_0, weight = x_487_weight_0_to_fp16, x = x_483_cast_fp16)[name = string("x_487_cast_fp16")];
            tensor<fp16, [1, 256, 2400]> x_489_cast_fp16 = add(x = x_473_cast_fp16, y = x_487_cast_fp16)[name = string("x_489_cast_fp16")];
            int32 var_2799 = const()[name = string("op_2799"), val = int32(1)];
            tensor<int32, [1]> x_491_axes_0 = const()[name = string("x_491_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 256, 1, 2400]> x_491_cast_fp16 = expand_dims(axes = x_491_axes_0, x = x_489_cast_fp16)[name = string("x_491_cast_fp16")];
            fp16 const_78_promoted_to_fp16 = const()[name = string("const_78_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 256, 1, 2400]> var_2804_cast_fp16 = mul(x = x_491_cast_fp16, y = const_78_promoted_to_fp16)[name = string("op_2804_cast_fp16")];
            bool x_493_interleave_0 = const()[name = string("x_493_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 512, 1, 2400]> x_493_cast_fp16 = concat(axis = var_2799, interleave = x_493_interleave_0, values = (x_491_cast_fp16, var_2804_cast_fp16))[name = string("x_493_cast_fp16")];
            tensor<int32, [1]> out_257_axes_0 = const()[name = string("out_257_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2814_to_fp16 = const()[name = string("op_2814_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 512, 1, 2400]> out_257_cast_fp16 = layer_norm(axes = out_257_axes_0, epsilon = var_2814_to_fp16, x = x_493_cast_fp16)[name = string("out_257_cast_fp16")];
            tensor<fp16, [1, 512, 1, 1]> stages_3_2_norm_weight_to_fp16 = const()[name = string("stages_3_2_norm_weight_to_fp16"), val = tensor<fp16, [1, 512, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(684681280)))];
            tensor<fp16, [1, 512, 1, 2400]> out_259_cast_fp16 = mul(x = out_257_cast_fp16, y = stages_3_2_norm_weight_to_fp16)[name = string("out_259_cast_fp16")];
            tensor<int32, [2]> var_2820_split_sizes_0 = const()[name = string("op_2820_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            int32 var_2820_axis_0 = const()[name = string("op_2820_axis_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 2400]> var_2820_cast_fp16_0, tensor<fp16, [1, 256, 1, 2400]> var_2820_cast_fp16_1 = split(axis = var_2820_axis_0, split_sizes = var_2820_split_sizes_0, x = out_259_cast_fp16)[name = string("op_2820_cast_fp16")];
            tensor<int32, [1]> x_497_axes_0 = const()[name = string("x_497_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 256, 2400]> x_497_cast_fp16 = squeeze(axes = x_497_axes_0, x = var_2820_cast_fp16_0)[name = string("x_497_cast_fp16")];
            int32 var_2825 = const()[name = string("op_2825"), val = int32(-1)];
            bool input_73_interleave_0 = const()[name = string("input_73_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 256, 2406]> input_73_cast_fp16 = concat(axis = var_2825, interleave = input_73_interleave_0, values = (var_770_cast_fp16_2, x_497_cast_fp16))[name = string("input_73_cast_fp16")];
            string x_499_pad_type_0 = const()[name = string("x_499_pad_type_0"), val = string("valid")];
            int32 x_499_groups_0 = const()[name = string("x_499_groups_0"), val = int32(256)];
            tensor<int32, [1]> x_499_strides_0 = const()[name = string("x_499_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_499_pad_0 = const()[name = string("x_499_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_499_dilations_0 = const()[name = string("x_499_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [256, 1, 7]> x_501_weight_0_to_fp16 = const()[name = string("x_501_weight_0_to_fp16"), val = tensor<fp16, [256, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(684682368)))];
            tensor<fp16, [256]> x_501_bias_0_to_fp16 = const()[name = string("x_501_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(684686016)))];
            tensor<fp16, [1, 256, 2400]> x_501_cast_fp16 = conv(bias = x_501_bias_0_to_fp16, dilations = x_499_dilations_0, groups = x_499_groups_0, pad = x_499_pad_0, pad_type = x_499_pad_type_0, strides = x_499_strides_0, weight = x_501_weight_0_to_fp16, x = input_73_cast_fp16)[name = string("x_501_cast_fp16")];
            tensor<int32, [3]> var_2848_begin_0 = const()[name = string("op_2848_begin_0"), val = tensor<int32, [3]>([0, 0, 2400])];
            tensor<int32, [3]> var_2848_end_0 = const()[name = string("op_2848_end_0"), val = tensor<int32, [3]>([1, 256, 2406])];
            tensor<bool, [3]> var_2848_end_mask_0 = const()[name = string("op_2848_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 256, 6]> var_2848_cast_fp16 = slice_by_index(begin = var_2848_begin_0, end = var_2848_end_0, end_mask = var_2848_end_mask_0, x = input_73_cast_fp16)[name = string("op_2848_cast_fp16")];
            tensor<fp16, [1, 256, 2400]> x_503_cast_fp16 = add(x = x_489_cast_fp16, y = x_501_cast_fp16)[name = string("x_503_cast_fp16")];
            int32 var_2858 = const()[name = string("op_2858"), val = int32(1)];
            tensor<int32, [1]> x_505_axes_0 = const()[name = string("x_505_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 256, 1, 2400]> x_505_cast_fp16 = expand_dims(axes = x_505_axes_0, x = x_503_cast_fp16)[name = string("x_505_cast_fp16")];
            fp16 const_81_promoted_to_fp16 = const()[name = string("const_81_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 256, 1, 2400]> var_2863_cast_fp16 = mul(x = x_505_cast_fp16, y = const_81_promoted_to_fp16)[name = string("op_2863_cast_fp16")];
            bool x_507_interleave_0 = const()[name = string("x_507_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 512, 1, 2400]> x_507_cast_fp16 = concat(axis = var_2858, interleave = x_507_interleave_0, values = (x_505_cast_fp16, var_2863_cast_fp16))[name = string("x_507_cast_fp16")];
            tensor<int32, [1]> out_265_axes_0 = const()[name = string("out_265_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2873_to_fp16 = const()[name = string("op_2873_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 512, 1, 2400]> out_265_cast_fp16 = layer_norm(axes = out_265_axes_0, epsilon = var_2873_to_fp16, x = x_507_cast_fp16)[name = string("out_265_cast_fp16")];
            tensor<fp16, [1, 512, 1, 1]> stages_3_2_ffn_norm_weight_to_fp16 = const()[name = string("stages_3_2_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 512, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(684686592)))];
            tensor<fp16, [1, 512, 1, 2400]> out_267_cast_fp16 = mul(x = out_265_cast_fp16, y = stages_3_2_ffn_norm_weight_to_fp16)[name = string("out_267_cast_fp16")];
            tensor<int32, [2]> var_2879_split_sizes_0 = const()[name = string("op_2879_split_sizes_0"), val = tensor<int32, [2]>([256, 256])];
            int32 var_2879_axis_0 = const()[name = string("op_2879_axis_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 2400]> var_2879_cast_fp16_0, tensor<fp16, [1, 256, 1, 2400]> var_2879_cast_fp16_1 = split(axis = var_2879_axis_0, split_sizes = var_2879_split_sizes_0, x = out_267_cast_fp16)[name = string("op_2879_cast_fp16")];
            tensor<int32, [1]> x_511_axes_0 = const()[name = string("x_511_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 256, 2400]> x_511_cast_fp16 = squeeze(axes = x_511_axes_0, x = var_2879_cast_fp16_0)[name = string("x_511_cast_fp16")];
            string input_75_pad_type_0 = const()[name = string("input_75_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_75_strides_0 = const()[name = string("input_75_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_75_pad_0 = const()[name = string("input_75_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_75_dilations_0 = const()[name = string("input_75_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_75_groups_0 = const()[name = string("input_75_groups_0"), val = int32(1)];
            tensor<fp16, [1024, 256, 1]> var_2885_to_fp16 = const()[name = string("op_2885_to_fp16"), val = tensor<fp16, [1024, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(684687680)))];
            tensor<fp16, [1024]> stages_3_2_ffn_linear1_bias_to_fp16 = const()[name = string("stages_3_2_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685212032)))];
            tensor<fp16, [1, 1024, 2400]> input_75_cast_fp16 = conv(bias = stages_3_2_ffn_linear1_bias_to_fp16, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = var_2885_to_fp16, x = x_511_cast_fp16)[name = string("input_75_cast_fp16")];
            string x_513_mode_0 = const()[name = string("x_513_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 1024, 2400]> x_513_cast_fp16 = gelu(mode = x_513_mode_0, x = input_75_cast_fp16)[name = string("x_513_cast_fp16")];
            string x_515_pad_type_0 = const()[name = string("x_515_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_515_strides_0 = const()[name = string("x_515_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_515_pad_0 = const()[name = string("x_515_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_515_dilations_0 = const()[name = string("x_515_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_515_groups_0 = const()[name = string("x_515_groups_0"), val = int32(1)];
            tensor<fp16, [256, 1024, 1]> x_517_weight_0_to_fp16 = const()[name = string("x_517_weight_0_to_fp16"), val = tensor<fp16, [256, 1024, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685214144)))];
            tensor<fp16, [256]> x_517_bias_0_to_fp16 = const()[name = string("x_517_bias_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685738496)))];
            tensor<fp16, [1, 256, 2400]> x_517_cast_fp16 = conv(bias = x_517_bias_0_to_fp16, dilations = x_515_dilations_0, groups = x_515_groups_0, pad = x_515_pad_0, pad_type = x_515_pad_type_0, strides = x_515_strides_0, weight = x_517_weight_0_to_fp16, x = x_513_cast_fp16)[name = string("x_517_cast_fp16")];
            tensor<fp16, [1, 256, 2400]> x_519_cast_fp16 = add(x = x_503_cast_fp16, y = x_517_cast_fp16)[name = string("x_519_cast_fp16")];
            int32 var_2916 = const()[name = string("op_2916"), val = int32(-1)];
            bool input_77_interleave_0 = const()[name = string("input_77_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 256, 2407]> input_77_cast_fp16 = concat(axis = var_2916, interleave = input_77_interleave_0, values = (var_770_cast_fp16_3, x_519_cast_fp16))[name = string("input_77_cast_fp16")];
            string full_output_13_pad_type_0 = const()[name = string("full_output_13_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> full_output_13_strides_0 = const()[name = string("full_output_13_strides_0"), val = tensor<int32, [1]>([4])];
            tensor<int32, [2]> full_output_13_pad_0 = const()[name = string("full_output_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> full_output_13_dilations_0 = const()[name = string("full_output_13_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 full_output_13_groups_0 = const()[name = string("full_output_13_groups_0"), val = int32(1)];
            tensor<int32, [3]> full_output_13_has_output_shape_output_shape_0 = const()[name = string("full_output_13_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 128, 9632])];
            tensor<fp16, [256, 128, 8]> upsample_layers_4_0_convtr_convtr_weight_to_fp16 = const()[name = string("upsample_layers_4_0_convtr_convtr_weight_to_fp16"), val = tensor<fp16, [256, 128, 8]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685739072)))];
            tensor<fp16, [128]> upsample_layers_4_0_convtr_convtr_bias_to_fp16 = const()[name = string("upsample_layers_4_0_convtr_convtr_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686263424)))];
            tensor<fp16, [1, 128, 9632]> full_output_13_has_output_shape_cast_fp16 = conv_transpose(bias = upsample_layers_4_0_convtr_convtr_bias_to_fp16, dilations = full_output_13_dilations_0, groups = full_output_13_groups_0, output_shape = full_output_13_has_output_shape_output_shape_0, pad = full_output_13_pad_0, pad_type = full_output_13_pad_type_0, strides = full_output_13_strides_0, weight = upsample_layers_4_0_convtr_convtr_weight_to_fp16, x = input_77_cast_fp16)[name = string("full_output_13_has_output_shape_cast_fp16")];
            tensor<int32, [3]> full_output_15_begin_0 = const()[name = string("full_output_15_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> full_output_15_end_0 = const()[name = string("full_output_15_end_0"), val = tensor<int32, [3]>([1, 128, 9628])];
            tensor<bool, [3]> full_output_15_end_mask_0 = const()[name = string("full_output_15_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp16, [1, 128, 9628]> full_output_15_cast_fp16 = slice_by_index(begin = full_output_15_begin_0, end = full_output_15_end_0, end_mask = full_output_15_end_mask_0, x = full_output_13_has_output_shape_cast_fp16)[name = string("full_output_15_cast_fp16")];
            tensor<int32, [3]> x_521_begin_0 = const()[name = string("x_521_begin_0"), val = tensor<int32, [3]>([0, 0, 28])];
            tensor<int32, [3]> x_521_end_0 = const()[name = string("x_521_end_0"), val = tensor<int32, [3]>([1, 128, 9628])];
            tensor<bool, [3]> x_521_end_mask_0 = const()[name = string("x_521_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 128, 9600]> x_521_cast_fp16 = slice_by_index(begin = x_521_begin_0, end = x_521_end_0, end_mask = x_521_end_mask_0, x = full_output_15_cast_fp16)[name = string("x_521_cast_fp16")];
            tensor<int32, [3]> var_2950_begin_0 = const()[name = string("op_2950_begin_0"), val = tensor<int32, [3]>([0, 0, 2400])];
            tensor<int32, [3]> var_2950_end_0 = const()[name = string("op_2950_end_0"), val = tensor<int32, [3]>([1, 256, 2407])];
            tensor<bool, [3]> var_2950_end_mask_0 = const()[name = string("op_2950_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 256, 7]> var_2950_cast_fp16 = slice_by_index(begin = var_2950_begin_0, end = var_2950_end_0, end_mask = var_2950_end_mask_0, x = input_77_cast_fp16)[name = string("op_2950_cast_fp16")];
            int32 var_2955 = const()[name = string("op_2955"), val = int32(1)];
            tensor<int32, [1]> x_523_axes_0 = const()[name = string("x_523_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 128, 1, 9600]> x_523_cast_fp16 = expand_dims(axes = x_523_axes_0, x = x_521_cast_fp16)[name = string("x_523_cast_fp16")];
            fp16 const_86_promoted_to_fp16 = const()[name = string("const_86_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 128, 1, 9600]> var_2960_cast_fp16 = mul(x = x_523_cast_fp16, y = const_86_promoted_to_fp16)[name = string("op_2960_cast_fp16")];
            bool x_525_interleave_0 = const()[name = string("x_525_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 256, 1, 9600]> x_525_cast_fp16 = concat(axis = var_2955, interleave = x_525_interleave_0, values = (x_523_cast_fp16, var_2960_cast_fp16))[name = string("x_525_cast_fp16")];
            tensor<int32, [1]> out_273_axes_0 = const()[name = string("out_273_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_2970_to_fp16 = const()[name = string("op_2970_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 256, 1, 9600]> out_273_cast_fp16 = layer_norm(axes = out_273_axes_0, epsilon = var_2970_to_fp16, x = x_525_cast_fp16)[name = string("out_273_cast_fp16")];
            tensor<fp16, [1, 256, 1, 1]> stages_4_0_norm_weight_to_fp16 = const()[name = string("stages_4_0_norm_weight_to_fp16"), val = tensor<fp16, [1, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686263744)))];
            tensor<fp16, [1, 256, 1, 9600]> out_275_cast_fp16 = mul(x = out_273_cast_fp16, y = stages_4_0_norm_weight_to_fp16)[name = string("out_275_cast_fp16")];
            tensor<int32, [2]> var_2976_split_sizes_0 = const()[name = string("op_2976_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            int32 var_2976_axis_0 = const()[name = string("op_2976_axis_0"), val = int32(1)];
            tensor<fp16, [1, 128, 1, 9600]> var_2976_cast_fp16_0, tensor<fp16, [1, 128, 1, 9600]> var_2976_cast_fp16_1 = split(axis = var_2976_axis_0, split_sizes = var_2976_split_sizes_0, x = out_275_cast_fp16)[name = string("op_2976_cast_fp16")];
            tensor<int32, [1]> x_529_axes_0 = const()[name = string("x_529_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 128, 9600]> x_529_cast_fp16 = squeeze(axes = x_529_axes_0, x = var_2976_cast_fp16_0)[name = string("x_529_cast_fp16")];
            int32 var_2981 = const()[name = string("op_2981"), val = int32(-1)];
            bool input_79_interleave_0 = const()[name = string("input_79_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 128, 9606]> input_79_cast_fp16 = concat(axis = var_2981, interleave = input_79_interleave_0, values = (var_781_cast_fp16_0, x_529_cast_fp16))[name = string("input_79_cast_fp16")];
            string x_531_pad_type_0 = const()[name = string("x_531_pad_type_0"), val = string("valid")];
            int32 x_531_groups_0 = const()[name = string("x_531_groups_0"), val = int32(128)];
            tensor<int32, [1]> x_531_strides_0 = const()[name = string("x_531_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_531_pad_0 = const()[name = string("x_531_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_531_dilations_0 = const()[name = string("x_531_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [128, 1, 7]> x_533_weight_0_to_fp16 = const()[name = string("x_533_weight_0_to_fp16"), val = tensor<fp16, [128, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686264320)))];
            tensor<fp16, [128]> x_533_bias_0_to_fp16 = const()[name = string("x_533_bias_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686266176)))];
            tensor<fp16, [1, 128, 9600]> x_533_cast_fp16 = conv(bias = x_533_bias_0_to_fp16, dilations = x_531_dilations_0, groups = x_531_groups_0, pad = x_531_pad_0, pad_type = x_531_pad_type_0, strides = x_531_strides_0, weight = x_533_weight_0_to_fp16, x = input_79_cast_fp16)[name = string("x_533_cast_fp16")];
            tensor<int32, [3]> var_3004_begin_0 = const()[name = string("op_3004_begin_0"), val = tensor<int32, [3]>([0, 0, 9600])];
            tensor<int32, [3]> var_3004_end_0 = const()[name = string("op_3004_end_0"), val = tensor<int32, [3]>([1, 128, 9606])];
            tensor<bool, [3]> var_3004_end_mask_0 = const()[name = string("op_3004_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 128, 6]> var_3004_cast_fp16 = slice_by_index(begin = var_3004_begin_0, end = var_3004_end_0, end_mask = var_3004_end_mask_0, x = input_79_cast_fp16)[name = string("op_3004_cast_fp16")];
            tensor<fp16, [1, 128, 9600]> x_535_cast_fp16 = add(x = x_521_cast_fp16, y = x_533_cast_fp16)[name = string("x_535_cast_fp16")];
            int32 var_3014 = const()[name = string("op_3014"), val = int32(1)];
            tensor<int32, [1]> x_537_axes_0 = const()[name = string("x_537_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 128, 1, 9600]> x_537_cast_fp16 = expand_dims(axes = x_537_axes_0, x = x_535_cast_fp16)[name = string("x_537_cast_fp16")];
            fp16 const_89_promoted_to_fp16 = const()[name = string("const_89_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 128, 1, 9600]> var_3019_cast_fp16 = mul(x = x_537_cast_fp16, y = const_89_promoted_to_fp16)[name = string("op_3019_cast_fp16")];
            bool x_539_interleave_0 = const()[name = string("x_539_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 256, 1, 9600]> x_539_cast_fp16 = concat(axis = var_3014, interleave = x_539_interleave_0, values = (x_537_cast_fp16, var_3019_cast_fp16))[name = string("x_539_cast_fp16")];
            tensor<int32, [1]> out_281_axes_0 = const()[name = string("out_281_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3029_to_fp16 = const()[name = string("op_3029_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 256, 1, 9600]> out_281_cast_fp16 = layer_norm(axes = out_281_axes_0, epsilon = var_3029_to_fp16, x = x_539_cast_fp16)[name = string("out_281_cast_fp16")];
            tensor<fp16, [1, 256, 1, 1]> stages_4_0_ffn_norm_weight_to_fp16 = const()[name = string("stages_4_0_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686266496)))];
            tensor<fp16, [1, 256, 1, 9600]> out_283_cast_fp16 = mul(x = out_281_cast_fp16, y = stages_4_0_ffn_norm_weight_to_fp16)[name = string("out_283_cast_fp16")];
            tensor<int32, [2]> var_3035_split_sizes_0 = const()[name = string("op_3035_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            int32 var_3035_axis_0 = const()[name = string("op_3035_axis_0"), val = int32(1)];
            tensor<fp16, [1, 128, 1, 9600]> var_3035_cast_fp16_0, tensor<fp16, [1, 128, 1, 9600]> var_3035_cast_fp16_1 = split(axis = var_3035_axis_0, split_sizes = var_3035_split_sizes_0, x = out_283_cast_fp16)[name = string("op_3035_cast_fp16")];
            tensor<int32, [1]> x_543_axes_0 = const()[name = string("x_543_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 128, 9600]> x_543_cast_fp16 = squeeze(axes = x_543_axes_0, x = var_3035_cast_fp16_0)[name = string("x_543_cast_fp16")];
            string input_81_pad_type_0 = const()[name = string("input_81_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_81_strides_0 = const()[name = string("input_81_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_81_pad_0 = const()[name = string("input_81_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_81_dilations_0 = const()[name = string("input_81_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_81_groups_0 = const()[name = string("input_81_groups_0"), val = int32(1)];
            tensor<fp16, [512, 128, 1]> var_3041_to_fp16 = const()[name = string("op_3041_to_fp16"), val = tensor<fp16, [512, 128, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686267072)))];
            tensor<fp16, [512]> stages_4_0_ffn_linear1_bias_to_fp16 = const()[name = string("stages_4_0_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686398208)))];
            tensor<fp16, [1, 512, 9600]> input_81_cast_fp16 = conv(bias = stages_4_0_ffn_linear1_bias_to_fp16, dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = var_3041_to_fp16, x = x_543_cast_fp16)[name = string("input_81_cast_fp16")];
            string x_545_mode_0 = const()[name = string("x_545_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 512, 9600]> x_545_cast_fp16 = gelu(mode = x_545_mode_0, x = input_81_cast_fp16)[name = string("x_545_cast_fp16")];
            string x_547_pad_type_0 = const()[name = string("x_547_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_547_strides_0 = const()[name = string("x_547_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_547_pad_0 = const()[name = string("x_547_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_547_dilations_0 = const()[name = string("x_547_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_547_groups_0 = const()[name = string("x_547_groups_0"), val = int32(1)];
            tensor<fp16, [128, 512, 1]> x_549_weight_0_to_fp16 = const()[name = string("x_549_weight_0_to_fp16"), val = tensor<fp16, [128, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686399296)))];
            tensor<fp16, [128]> x_549_bias_0_to_fp16 = const()[name = string("x_549_bias_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686530432)))];
            tensor<fp16, [1, 128, 9600]> x_549_cast_fp16 = conv(bias = x_549_bias_0_to_fp16, dilations = x_547_dilations_0, groups = x_547_groups_0, pad = x_547_pad_0, pad_type = x_547_pad_type_0, strides = x_547_strides_0, weight = x_549_weight_0_to_fp16, x = x_545_cast_fp16)[name = string("x_549_cast_fp16")];
            tensor<fp16, [1, 128, 9600]> x_551_cast_fp16 = add(x = x_535_cast_fp16, y = x_549_cast_fp16)[name = string("x_551_cast_fp16")];
            int32 var_3070 = const()[name = string("op_3070"), val = int32(1)];
            tensor<int32, [1]> x_553_axes_0 = const()[name = string("x_553_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 128, 1, 9600]> x_553_cast_fp16 = expand_dims(axes = x_553_axes_0, x = x_551_cast_fp16)[name = string("x_553_cast_fp16")];
            fp16 const_90_promoted_to_fp16 = const()[name = string("const_90_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 128, 1, 9600]> var_3075_cast_fp16 = mul(x = x_553_cast_fp16, y = const_90_promoted_to_fp16)[name = string("op_3075_cast_fp16")];
            bool x_555_interleave_0 = const()[name = string("x_555_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 256, 1, 9600]> x_555_cast_fp16 = concat(axis = var_3070, interleave = x_555_interleave_0, values = (x_553_cast_fp16, var_3075_cast_fp16))[name = string("x_555_cast_fp16")];
            tensor<int32, [1]> out_289_axes_0 = const()[name = string("out_289_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3085_to_fp16 = const()[name = string("op_3085_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 256, 1, 9600]> out_289_cast_fp16 = layer_norm(axes = out_289_axes_0, epsilon = var_3085_to_fp16, x = x_555_cast_fp16)[name = string("out_289_cast_fp16")];
            tensor<fp16, [1, 256, 1, 1]> stages_4_1_norm_weight_to_fp16 = const()[name = string("stages_4_1_norm_weight_to_fp16"), val = tensor<fp16, [1, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686530752)))];
            tensor<fp16, [1, 256, 1, 9600]> out_291_cast_fp16 = mul(x = out_289_cast_fp16, y = stages_4_1_norm_weight_to_fp16)[name = string("out_291_cast_fp16")];
            tensor<int32, [2]> var_3091_split_sizes_0 = const()[name = string("op_3091_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            int32 var_3091_axis_0 = const()[name = string("op_3091_axis_0"), val = int32(1)];
            tensor<fp16, [1, 128, 1, 9600]> var_3091_cast_fp16_0, tensor<fp16, [1, 128, 1, 9600]> var_3091_cast_fp16_1 = split(axis = var_3091_axis_0, split_sizes = var_3091_split_sizes_0, x = out_291_cast_fp16)[name = string("op_3091_cast_fp16")];
            tensor<int32, [1]> x_559_axes_0 = const()[name = string("x_559_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 128, 9600]> x_559_cast_fp16 = squeeze(axes = x_559_axes_0, x = var_3091_cast_fp16_0)[name = string("x_559_cast_fp16")];
            int32 var_3096 = const()[name = string("op_3096"), val = int32(-1)];
            bool input_83_interleave_0 = const()[name = string("input_83_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 128, 9606]> input_83_cast_fp16 = concat(axis = var_3096, interleave = input_83_interleave_0, values = (var_781_cast_fp16_1, x_559_cast_fp16))[name = string("input_83_cast_fp16")];
            string x_561_pad_type_0 = const()[name = string("x_561_pad_type_0"), val = string("valid")];
            int32 x_561_groups_0 = const()[name = string("x_561_groups_0"), val = int32(128)];
            tensor<int32, [1]> x_561_strides_0 = const()[name = string("x_561_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_561_pad_0 = const()[name = string("x_561_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_561_dilations_0 = const()[name = string("x_561_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [128, 1, 7]> x_563_weight_0_to_fp16 = const()[name = string("x_563_weight_0_to_fp16"), val = tensor<fp16, [128, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686531328)))];
            tensor<fp16, [128]> x_563_bias_0_to_fp16 = const()[name = string("x_563_bias_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686533184)))];
            tensor<fp16, [1, 128, 9600]> x_563_cast_fp16 = conv(bias = x_563_bias_0_to_fp16, dilations = x_561_dilations_0, groups = x_561_groups_0, pad = x_561_pad_0, pad_type = x_561_pad_type_0, strides = x_561_strides_0, weight = x_563_weight_0_to_fp16, x = input_83_cast_fp16)[name = string("x_563_cast_fp16")];
            tensor<int32, [3]> var_3119_begin_0 = const()[name = string("op_3119_begin_0"), val = tensor<int32, [3]>([0, 0, 9600])];
            tensor<int32, [3]> var_3119_end_0 = const()[name = string("op_3119_end_0"), val = tensor<int32, [3]>([1, 128, 9606])];
            tensor<bool, [3]> var_3119_end_mask_0 = const()[name = string("op_3119_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 128, 6]> var_3119_cast_fp16 = slice_by_index(begin = var_3119_begin_0, end = var_3119_end_0, end_mask = var_3119_end_mask_0, x = input_83_cast_fp16)[name = string("op_3119_cast_fp16")];
            tensor<fp16, [1, 128, 9600]> x_565_cast_fp16 = add(x = x_551_cast_fp16, y = x_563_cast_fp16)[name = string("x_565_cast_fp16")];
            int32 var_3129 = const()[name = string("op_3129"), val = int32(1)];
            tensor<int32, [1]> x_567_axes_0 = const()[name = string("x_567_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 128, 1, 9600]> x_567_cast_fp16 = expand_dims(axes = x_567_axes_0, x = x_565_cast_fp16)[name = string("x_567_cast_fp16")];
            fp16 const_93_promoted_to_fp16 = const()[name = string("const_93_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 128, 1, 9600]> var_3134_cast_fp16 = mul(x = x_567_cast_fp16, y = const_93_promoted_to_fp16)[name = string("op_3134_cast_fp16")];
            bool x_569_interleave_0 = const()[name = string("x_569_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 256, 1, 9600]> x_569_cast_fp16 = concat(axis = var_3129, interleave = x_569_interleave_0, values = (x_567_cast_fp16, var_3134_cast_fp16))[name = string("x_569_cast_fp16")];
            tensor<int32, [1]> out_297_axes_0 = const()[name = string("out_297_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3144_to_fp16 = const()[name = string("op_3144_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 256, 1, 9600]> out_297_cast_fp16 = layer_norm(axes = out_297_axes_0, epsilon = var_3144_to_fp16, x = x_569_cast_fp16)[name = string("out_297_cast_fp16")];
            tensor<fp16, [1, 256, 1, 1]> stages_4_1_ffn_norm_weight_to_fp16 = const()[name = string("stages_4_1_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686533504)))];
            tensor<fp16, [1, 256, 1, 9600]> out_299_cast_fp16 = mul(x = out_297_cast_fp16, y = stages_4_1_ffn_norm_weight_to_fp16)[name = string("out_299_cast_fp16")];
            tensor<int32, [2]> var_3150_split_sizes_0 = const()[name = string("op_3150_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            int32 var_3150_axis_0 = const()[name = string("op_3150_axis_0"), val = int32(1)];
            tensor<fp16, [1, 128, 1, 9600]> var_3150_cast_fp16_0, tensor<fp16, [1, 128, 1, 9600]> var_3150_cast_fp16_1 = split(axis = var_3150_axis_0, split_sizes = var_3150_split_sizes_0, x = out_299_cast_fp16)[name = string("op_3150_cast_fp16")];
            tensor<int32, [1]> x_573_axes_0 = const()[name = string("x_573_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 128, 9600]> x_573_cast_fp16 = squeeze(axes = x_573_axes_0, x = var_3150_cast_fp16_0)[name = string("x_573_cast_fp16")];
            string input_85_pad_type_0 = const()[name = string("input_85_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_85_strides_0 = const()[name = string("input_85_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_85_dilations_0 = const()[name = string("input_85_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_85_groups_0 = const()[name = string("input_85_groups_0"), val = int32(1)];
            tensor<fp16, [512, 128, 1]> var_3156_to_fp16 = const()[name = string("op_3156_to_fp16"), val = tensor<fp16, [512, 128, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686534080)))];
            tensor<fp16, [512]> stages_4_1_ffn_linear1_bias_to_fp16 = const()[name = string("stages_4_1_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686665216)))];
            tensor<fp16, [1, 512, 9600]> input_85_cast_fp16 = conv(bias = stages_4_1_ffn_linear1_bias_to_fp16, dilations = input_85_dilations_0, groups = input_85_groups_0, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = input_85_strides_0, weight = var_3156_to_fp16, x = x_573_cast_fp16)[name = string("input_85_cast_fp16")];
            string x_575_mode_0 = const()[name = string("x_575_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 512, 9600]> x_575_cast_fp16 = gelu(mode = x_575_mode_0, x = input_85_cast_fp16)[name = string("x_575_cast_fp16")];
            string x_577_pad_type_0 = const()[name = string("x_577_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_577_strides_0 = const()[name = string("x_577_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_577_pad_0 = const()[name = string("x_577_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_577_dilations_0 = const()[name = string("x_577_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_577_groups_0 = const()[name = string("x_577_groups_0"), val = int32(1)];
            tensor<fp16, [128, 512, 1]> x_579_weight_0_to_fp16 = const()[name = string("x_579_weight_0_to_fp16"), val = tensor<fp16, [128, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686666304)))];
            tensor<fp16, [128]> x_579_bias_0_to_fp16 = const()[name = string("x_579_bias_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686797440)))];
            tensor<fp16, [1, 128, 9600]> x_579_cast_fp16 = conv(bias = x_579_bias_0_to_fp16, dilations = x_577_dilations_0, groups = x_577_groups_0, pad = x_577_pad_0, pad_type = x_577_pad_type_0, strides = x_577_strides_0, weight = x_579_weight_0_to_fp16, x = x_575_cast_fp16)[name = string("x_579_cast_fp16")];
            tensor<fp16, [1, 128, 9600]> x_581_cast_fp16 = add(x = x_565_cast_fp16, y = x_579_cast_fp16)[name = string("x_581_cast_fp16")];
            int32 var_3185 = const()[name = string("op_3185"), val = int32(1)];
            tensor<int32, [1]> x_583_axes_0 = const()[name = string("x_583_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 128, 1, 9600]> x_583_cast_fp16 = expand_dims(axes = x_583_axes_0, x = x_581_cast_fp16)[name = string("x_583_cast_fp16")];
            fp16 const_94_promoted_to_fp16 = const()[name = string("const_94_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 128, 1, 9600]> var_3190_cast_fp16 = mul(x = x_583_cast_fp16, y = const_94_promoted_to_fp16)[name = string("op_3190_cast_fp16")];
            bool x_585_interleave_0 = const()[name = string("x_585_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 256, 1, 9600]> x_585_cast_fp16 = concat(axis = var_3185, interleave = x_585_interleave_0, values = (x_583_cast_fp16, var_3190_cast_fp16))[name = string("x_585_cast_fp16")];
            tensor<int32, [1]> out_305_axes_0 = const()[name = string("out_305_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3200_to_fp16 = const()[name = string("op_3200_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 256, 1, 9600]> out_305_cast_fp16 = layer_norm(axes = out_305_axes_0, epsilon = var_3200_to_fp16, x = x_585_cast_fp16)[name = string("out_305_cast_fp16")];
            tensor<fp16, [1, 256, 1, 1]> stages_4_2_norm_weight_to_fp16 = const()[name = string("stages_4_2_norm_weight_to_fp16"), val = tensor<fp16, [1, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686797760)))];
            tensor<fp16, [1, 256, 1, 9600]> out_307_cast_fp16 = mul(x = out_305_cast_fp16, y = stages_4_2_norm_weight_to_fp16)[name = string("out_307_cast_fp16")];
            tensor<int32, [2]> var_3206_split_sizes_0 = const()[name = string("op_3206_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            int32 var_3206_axis_0 = const()[name = string("op_3206_axis_0"), val = int32(1)];
            tensor<fp16, [1, 128, 1, 9600]> var_3206_cast_fp16_0, tensor<fp16, [1, 128, 1, 9600]> var_3206_cast_fp16_1 = split(axis = var_3206_axis_0, split_sizes = var_3206_split_sizes_0, x = out_307_cast_fp16)[name = string("op_3206_cast_fp16")];
            tensor<int32, [1]> x_589_axes_0 = const()[name = string("x_589_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 128, 9600]> x_589_cast_fp16 = squeeze(axes = x_589_axes_0, x = var_3206_cast_fp16_0)[name = string("x_589_cast_fp16")];
            int32 var_3211 = const()[name = string("op_3211"), val = int32(-1)];
            bool input_87_interleave_0 = const()[name = string("input_87_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 128, 9606]> input_87_cast_fp16 = concat(axis = var_3211, interleave = input_87_interleave_0, values = (var_781_cast_fp16_2, x_589_cast_fp16))[name = string("input_87_cast_fp16")];
            string x_591_pad_type_0 = const()[name = string("x_591_pad_type_0"), val = string("valid")];
            int32 x_591_groups_0 = const()[name = string("x_591_groups_0"), val = int32(128)];
            tensor<int32, [1]> x_591_strides_0 = const()[name = string("x_591_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_591_pad_0 = const()[name = string("x_591_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_591_dilations_0 = const()[name = string("x_591_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [128, 1, 7]> x_593_weight_0_to_fp16 = const()[name = string("x_593_weight_0_to_fp16"), val = tensor<fp16, [128, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686798336)))];
            tensor<fp16, [128]> x_593_bias_0_to_fp16 = const()[name = string("x_593_bias_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686800192)))];
            tensor<fp16, [1, 128, 9600]> x_593_cast_fp16 = conv(bias = x_593_bias_0_to_fp16, dilations = x_591_dilations_0, groups = x_591_groups_0, pad = x_591_pad_0, pad_type = x_591_pad_type_0, strides = x_591_strides_0, weight = x_593_weight_0_to_fp16, x = input_87_cast_fp16)[name = string("x_593_cast_fp16")];
            tensor<int32, [3]> var_3234_begin_0 = const()[name = string("op_3234_begin_0"), val = tensor<int32, [3]>([0, 0, 9600])];
            tensor<int32, [3]> var_3234_end_0 = const()[name = string("op_3234_end_0"), val = tensor<int32, [3]>([1, 128, 9606])];
            tensor<bool, [3]> var_3234_end_mask_0 = const()[name = string("op_3234_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 128, 6]> var_3234_cast_fp16 = slice_by_index(begin = var_3234_begin_0, end = var_3234_end_0, end_mask = var_3234_end_mask_0, x = input_87_cast_fp16)[name = string("op_3234_cast_fp16")];
            tensor<fp16, [1, 128, 9600]> x_595_cast_fp16 = add(x = x_581_cast_fp16, y = x_593_cast_fp16)[name = string("x_595_cast_fp16")];
            int32 var_3244 = const()[name = string("op_3244"), val = int32(1)];
            tensor<int32, [1]> x_597_axes_0 = const()[name = string("x_597_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 128, 1, 9600]> x_597_cast_fp16 = expand_dims(axes = x_597_axes_0, x = x_595_cast_fp16)[name = string("x_597_cast_fp16")];
            fp16 const_97_promoted_to_fp16 = const()[name = string("const_97_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 128, 1, 9600]> var_3249_cast_fp16 = mul(x = x_597_cast_fp16, y = const_97_promoted_to_fp16)[name = string("op_3249_cast_fp16")];
            bool x_599_interleave_0 = const()[name = string("x_599_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 256, 1, 9600]> x_599_cast_fp16 = concat(axis = var_3244, interleave = x_599_interleave_0, values = (x_597_cast_fp16, var_3249_cast_fp16))[name = string("x_599_cast_fp16")];
            tensor<int32, [1]> out_313_axes_0 = const()[name = string("out_313_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3259_to_fp16 = const()[name = string("op_3259_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 256, 1, 9600]> out_313_cast_fp16 = layer_norm(axes = out_313_axes_0, epsilon = var_3259_to_fp16, x = x_599_cast_fp16)[name = string("out_313_cast_fp16")];
            tensor<fp16, [1, 256, 1, 1]> stages_4_2_ffn_norm_weight_to_fp16 = const()[name = string("stages_4_2_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686800512)))];
            tensor<fp16, [1, 256, 1, 9600]> out_315_cast_fp16 = mul(x = out_313_cast_fp16, y = stages_4_2_ffn_norm_weight_to_fp16)[name = string("out_315_cast_fp16")];
            tensor<int32, [2]> var_3265_split_sizes_0 = const()[name = string("op_3265_split_sizes_0"), val = tensor<int32, [2]>([128, 128])];
            int32 var_3265_axis_0 = const()[name = string("op_3265_axis_0"), val = int32(1)];
            tensor<fp16, [1, 128, 1, 9600]> var_3265_cast_fp16_0, tensor<fp16, [1, 128, 1, 9600]> var_3265_cast_fp16_1 = split(axis = var_3265_axis_0, split_sizes = var_3265_split_sizes_0, x = out_315_cast_fp16)[name = string("op_3265_cast_fp16")];
            tensor<int32, [1]> x_603_axes_0 = const()[name = string("x_603_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 128, 9600]> x_603_cast_fp16 = squeeze(axes = x_603_axes_0, x = var_3265_cast_fp16_0)[name = string("x_603_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, [512, 128, 1]> var_3271_to_fp16 = const()[name = string("op_3271_to_fp16"), val = tensor<fp16, [512, 128, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686801088)))];
            tensor<fp16, [512]> stages_4_2_ffn_linear1_bias_to_fp16 = const()[name = string("stages_4_2_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686932224)))];
            tensor<fp16, [1, 512, 9600]> input_89_cast_fp16 = conv(bias = stages_4_2_ffn_linear1_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 = var_3271_to_fp16, x = x_603_cast_fp16)[name = string("input_89_cast_fp16")];
            string x_605_mode_0 = const()[name = string("x_605_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 512, 9600]> x_605_cast_fp16 = gelu(mode = x_605_mode_0, x = input_89_cast_fp16)[name = string("x_605_cast_fp16")];
            string x_607_pad_type_0 = const()[name = string("x_607_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_607_strides_0 = const()[name = string("x_607_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_607_pad_0 = const()[name = string("x_607_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_607_dilations_0 = const()[name = string("x_607_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_607_groups_0 = const()[name = string("x_607_groups_0"), val = int32(1)];
            tensor<fp16, [128, 512, 1]> x_609_weight_0_to_fp16 = const()[name = string("x_609_weight_0_to_fp16"), val = tensor<fp16, [128, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686933312)))];
            tensor<fp16, [128]> x_609_bias_0_to_fp16 = const()[name = string("x_609_bias_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687064448)))];
            tensor<fp16, [1, 128, 9600]> x_609_cast_fp16 = conv(bias = x_609_bias_0_to_fp16, dilations = x_607_dilations_0, groups = x_607_groups_0, pad = x_607_pad_0, pad_type = x_607_pad_type_0, strides = x_607_strides_0, weight = x_609_weight_0_to_fp16, x = x_605_cast_fp16)[name = string("x_609_cast_fp16")];
            tensor<fp16, [1, 128, 9600]> x_611_cast_fp16 = add(x = x_595_cast_fp16, y = x_609_cast_fp16)[name = string("x_611_cast_fp16")];
            int32 var_3302 = const()[name = string("op_3302"), val = int32(-1)];
            bool input_91_interleave_0 = const()[name = string("input_91_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 128, 9603]> input_91_cast_fp16 = concat(axis = var_3302, interleave = input_91_interleave_0, values = (var_781_cast_fp16_3, x_611_cast_fp16))[name = string("input_91_cast_fp16")];
            string full_output_17_pad_type_0 = const()[name = string("full_output_17_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> full_output_17_strides_0 = const()[name = string("full_output_17_strides_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [2]> full_output_17_pad_0 = const()[name = string("full_output_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> full_output_17_dilations_0 = const()[name = string("full_output_17_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 full_output_17_groups_0 = const()[name = string("full_output_17_groups_0"), val = int32(1)];
            tensor<int32, [3]> full_output_17_has_output_shape_output_shape_0 = const()[name = string("full_output_17_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 64, 19208])];
            tensor<fp16, [128, 64, 4]> upsample_layers_5_0_convtr_convtr_weight_to_fp16 = const()[name = string("upsample_layers_5_0_convtr_convtr_weight_to_fp16"), val = tensor<fp16, [128, 64, 4]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687064768)))];
            tensor<fp16, [64]> upsample_layers_5_0_convtr_convtr_bias_to_fp16 = const()[name = string("upsample_layers_5_0_convtr_convtr_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687130368)))];
            tensor<fp16, [1, 64, 19208]> full_output_17_has_output_shape_cast_fp16 = conv_transpose(bias = upsample_layers_5_0_convtr_convtr_bias_to_fp16, dilations = full_output_17_dilations_0, groups = full_output_17_groups_0, output_shape = full_output_17_has_output_shape_output_shape_0, pad = full_output_17_pad_0, pad_type = full_output_17_pad_type_0, strides = full_output_17_strides_0, weight = upsample_layers_5_0_convtr_convtr_weight_to_fp16, x = input_91_cast_fp16)[name = string("full_output_17_has_output_shape_cast_fp16")];
            tensor<int32, [3]> full_output_19_begin_0 = const()[name = string("full_output_19_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> full_output_19_end_0 = const()[name = string("full_output_19_end_0"), val = tensor<int32, [3]>([1, 64, 19206])];
            tensor<bool, [3]> full_output_19_end_mask_0 = const()[name = string("full_output_19_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp16, [1, 64, 19206]> full_output_19_cast_fp16 = slice_by_index(begin = full_output_19_begin_0, end = full_output_19_end_0, end_mask = full_output_19_end_mask_0, x = full_output_17_has_output_shape_cast_fp16)[name = string("full_output_19_cast_fp16")];
            tensor<int32, [3]> x_613_begin_0 = const()[name = string("x_613_begin_0"), val = tensor<int32, [3]>([0, 0, 6])];
            tensor<int32, [3]> x_613_end_0 = const()[name = string("x_613_end_0"), val = tensor<int32, [3]>([1, 64, 19206])];
            tensor<bool, [3]> x_613_end_mask_0 = const()[name = string("x_613_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 64, 19200]> x_613_cast_fp16 = slice_by_index(begin = x_613_begin_0, end = x_613_end_0, end_mask = x_613_end_mask_0, x = full_output_19_cast_fp16)[name = string("x_613_cast_fp16")];
            tensor<int32, [3]> var_3336_begin_0 = const()[name = string("op_3336_begin_0"), val = tensor<int32, [3]>([0, 0, 9600])];
            tensor<int32, [3]> var_3336_end_0 = const()[name = string("op_3336_end_0"), val = tensor<int32, [3]>([1, 128, 9603])];
            tensor<bool, [3]> var_3336_end_mask_0 = const()[name = string("op_3336_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 128, 3]> var_3336_cast_fp16 = slice_by_index(begin = var_3336_begin_0, end = var_3336_end_0, end_mask = var_3336_end_mask_0, x = input_91_cast_fp16)[name = string("op_3336_cast_fp16")];
            int32 var_3341 = const()[name = string("op_3341"), val = int32(1)];
            tensor<int32, [1]> x_615_axes_0 = const()[name = string("x_615_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 64, 1, 19200]> x_615_cast_fp16 = expand_dims(axes = x_615_axes_0, x = x_613_cast_fp16)[name = string("x_615_cast_fp16")];
            fp16 const_102_promoted_to_fp16 = const()[name = string("const_102_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 64, 1, 19200]> var_3346_cast_fp16 = mul(x = x_615_cast_fp16, y = const_102_promoted_to_fp16)[name = string("op_3346_cast_fp16")];
            bool x_617_interleave_0 = const()[name = string("x_617_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 128, 1, 19200]> x_617_cast_fp16 = concat(axis = var_3341, interleave = x_617_interleave_0, values = (x_615_cast_fp16, var_3346_cast_fp16))[name = string("x_617_cast_fp16")];
            tensor<int32, [1]> out_321_axes_0 = const()[name = string("out_321_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3356_to_fp16 = const()[name = string("op_3356_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 128, 1, 19200]> out_321_cast_fp16 = layer_norm(axes = out_321_axes_0, epsilon = var_3356_to_fp16, x = x_617_cast_fp16)[name = string("out_321_cast_fp16")];
            tensor<fp16, [1, 128, 1, 1]> stages_5_0_norm_weight_to_fp16 = const()[name = string("stages_5_0_norm_weight_to_fp16"), val = tensor<fp16, [1, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687130560)))];
            tensor<fp16, [1, 128, 1, 19200]> out_323_cast_fp16 = mul(x = out_321_cast_fp16, y = stages_5_0_norm_weight_to_fp16)[name = string("out_323_cast_fp16")];
            tensor<int32, [2]> var_3362_split_sizes_0 = const()[name = string("op_3362_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3362_axis_0 = const()[name = string("op_3362_axis_0"), val = int32(1)];
            tensor<fp16, [1, 64, 1, 19200]> var_3362_cast_fp16_0, tensor<fp16, [1, 64, 1, 19200]> var_3362_cast_fp16_1 = split(axis = var_3362_axis_0, split_sizes = var_3362_split_sizes_0, x = out_323_cast_fp16)[name = string("op_3362_cast_fp16")];
            tensor<int32, [1]> x_621_axes_0 = const()[name = string("x_621_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 64, 19200]> x_621_cast_fp16 = squeeze(axes = x_621_axes_0, x = var_3362_cast_fp16_0)[name = string("x_621_cast_fp16")];
            int32 var_3367 = const()[name = string("op_3367"), val = int32(-1)];
            bool input_93_interleave_0 = const()[name = string("input_93_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 64, 19206]> input_93_cast_fp16 = concat(axis = var_3367, interleave = input_93_interleave_0, values = (var_792_cast_fp16_0, x_621_cast_fp16))[name = string("input_93_cast_fp16")];
            string x_623_pad_type_0 = const()[name = string("x_623_pad_type_0"), val = string("valid")];
            int32 x_623_groups_0 = const()[name = string("x_623_groups_0"), val = int32(64)];
            tensor<int32, [1]> x_623_strides_0 = const()[name = string("x_623_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_623_pad_0 = const()[name = string("x_623_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_623_dilations_0 = const()[name = string("x_623_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [64, 1, 7]> x_625_weight_0_to_fp16 = const()[name = string("x_625_weight_0_to_fp16"), val = tensor<fp16, [64, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687130880)))];
            tensor<fp16, [64]> x_625_bias_0_to_fp16 = const()[name = string("x_625_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687131840)))];
            tensor<fp16, [1, 64, 19200]> x_625_cast_fp16 = conv(bias = x_625_bias_0_to_fp16, dilations = x_623_dilations_0, groups = x_623_groups_0, pad = x_623_pad_0, pad_type = x_623_pad_type_0, strides = x_623_strides_0, weight = x_625_weight_0_to_fp16, x = input_93_cast_fp16)[name = string("x_625_cast_fp16")];
            tensor<int32, [3]> var_3390_begin_0 = const()[name = string("op_3390_begin_0"), val = tensor<int32, [3]>([0, 0, 19200])];
            tensor<int32, [3]> var_3390_end_0 = const()[name = string("op_3390_end_0"), val = tensor<int32, [3]>([1, 64, 19206])];
            tensor<bool, [3]> var_3390_end_mask_0 = const()[name = string("op_3390_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 64, 6]> var_3390_cast_fp16 = slice_by_index(begin = var_3390_begin_0, end = var_3390_end_0, end_mask = var_3390_end_mask_0, x = input_93_cast_fp16)[name = string("op_3390_cast_fp16")];
            tensor<fp16, [1, 64, 19200]> x_627_cast_fp16 = add(x = x_613_cast_fp16, y = x_625_cast_fp16)[name = string("x_627_cast_fp16")];
            int32 var_3400 = const()[name = string("op_3400"), val = int32(1)];
            tensor<int32, [1]> x_629_axes_0 = const()[name = string("x_629_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 64, 1, 19200]> x_629_cast_fp16 = expand_dims(axes = x_629_axes_0, x = x_627_cast_fp16)[name = string("x_629_cast_fp16")];
            fp16 const_105_promoted_to_fp16 = const()[name = string("const_105_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 64, 1, 19200]> var_3405_cast_fp16 = mul(x = x_629_cast_fp16, y = const_105_promoted_to_fp16)[name = string("op_3405_cast_fp16")];
            bool x_631_interleave_0 = const()[name = string("x_631_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 128, 1, 19200]> x_631_cast_fp16 = concat(axis = var_3400, interleave = x_631_interleave_0, values = (x_629_cast_fp16, var_3405_cast_fp16))[name = string("x_631_cast_fp16")];
            tensor<int32, [1]> out_329_axes_0 = const()[name = string("out_329_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3415_to_fp16 = const()[name = string("op_3415_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 128, 1, 19200]> out_329_cast_fp16 = layer_norm(axes = out_329_axes_0, epsilon = var_3415_to_fp16, x = x_631_cast_fp16)[name = string("out_329_cast_fp16")];
            tensor<fp16, [1, 128, 1, 1]> stages_5_0_ffn_norm_weight_to_fp16 = const()[name = string("stages_5_0_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687132032)))];
            tensor<fp16, [1, 128, 1, 19200]> out_331_cast_fp16 = mul(x = out_329_cast_fp16, y = stages_5_0_ffn_norm_weight_to_fp16)[name = string("out_331_cast_fp16")];
            tensor<int32, [2]> var_3421_split_sizes_0 = const()[name = string("op_3421_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3421_axis_0 = const()[name = string("op_3421_axis_0"), val = int32(1)];
            tensor<fp16, [1, 64, 1, 19200]> var_3421_cast_fp16_0, tensor<fp16, [1, 64, 1, 19200]> var_3421_cast_fp16_1 = split(axis = var_3421_axis_0, split_sizes = var_3421_split_sizes_0, x = out_331_cast_fp16)[name = string("op_3421_cast_fp16")];
            tensor<int32, [1]> x_635_axes_0 = const()[name = string("x_635_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 64, 19200]> x_635_cast_fp16 = squeeze(axes = x_635_axes_0, x = var_3421_cast_fp16_0)[name = string("x_635_cast_fp16")];
            string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)];
            tensor<fp16, [256, 64, 1]> var_3427_to_fp16 = const()[name = string("op_3427_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687132352)))];
            tensor<fp16, [256]> stages_5_0_ffn_linear1_bias_to_fp16 = const()[name = string("stages_5_0_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687165184)))];
            tensor<fp16, [1, 256, 19200]> input_95_cast_fp16 = conv(bias = stages_5_0_ffn_linear1_bias_to_fp16, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = var_3427_to_fp16, x = x_635_cast_fp16)[name = string("input_95_cast_fp16")];
            string x_637_mode_0 = const()[name = string("x_637_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 256, 19200]> x_637_cast_fp16 = gelu(mode = x_637_mode_0, x = input_95_cast_fp16)[name = string("x_637_cast_fp16")];
            string x_639_pad_type_0 = const()[name = string("x_639_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_639_strides_0 = const()[name = string("x_639_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_639_pad_0 = const()[name = string("x_639_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_639_dilations_0 = const()[name = string("x_639_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_639_groups_0 = const()[name = string("x_639_groups_0"), val = int32(1)];
            tensor<fp16, [64, 256, 1]> x_641_weight_0_to_fp16 = const()[name = string("x_641_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687165760)))];
            tensor<fp16, [64]> x_641_bias_0_to_fp16 = const()[name = string("x_641_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687198592)))];
            tensor<fp16, [1, 64, 19200]> x_641_cast_fp16 = conv(bias = x_641_bias_0_to_fp16, dilations = x_639_dilations_0, groups = x_639_groups_0, pad = x_639_pad_0, pad_type = x_639_pad_type_0, strides = x_639_strides_0, weight = x_641_weight_0_to_fp16, x = x_637_cast_fp16)[name = string("x_641_cast_fp16")];
            tensor<fp16, [1, 64, 19200]> x_643_cast_fp16 = add(x = x_627_cast_fp16, y = x_641_cast_fp16)[name = string("x_643_cast_fp16")];
            int32 var_3456 = const()[name = string("op_3456"), val = int32(1)];
            tensor<int32, [1]> x_645_axes_0 = const()[name = string("x_645_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 64, 1, 19200]> x_645_cast_fp16 = expand_dims(axes = x_645_axes_0, x = x_643_cast_fp16)[name = string("x_645_cast_fp16")];
            fp16 const_106_promoted_to_fp16 = const()[name = string("const_106_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 64, 1, 19200]> var_3461_cast_fp16 = mul(x = x_645_cast_fp16, y = const_106_promoted_to_fp16)[name = string("op_3461_cast_fp16")];
            bool x_647_interleave_0 = const()[name = string("x_647_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 128, 1, 19200]> x_647_cast_fp16 = concat(axis = var_3456, interleave = x_647_interleave_0, values = (x_645_cast_fp16, var_3461_cast_fp16))[name = string("x_647_cast_fp16")];
            tensor<int32, [1]> out_337_axes_0 = const()[name = string("out_337_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3471_to_fp16 = const()[name = string("op_3471_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 128, 1, 19200]> out_337_cast_fp16 = layer_norm(axes = out_337_axes_0, epsilon = var_3471_to_fp16, x = x_647_cast_fp16)[name = string("out_337_cast_fp16")];
            tensor<fp16, [1, 128, 1, 1]> stages_5_1_norm_weight_to_fp16 = const()[name = string("stages_5_1_norm_weight_to_fp16"), val = tensor<fp16, [1, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687198784)))];
            tensor<fp16, [1, 128, 1, 19200]> out_339_cast_fp16 = mul(x = out_337_cast_fp16, y = stages_5_1_norm_weight_to_fp16)[name = string("out_339_cast_fp16")];
            tensor<int32, [2]> var_3477_split_sizes_0 = const()[name = string("op_3477_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3477_axis_0 = const()[name = string("op_3477_axis_0"), val = int32(1)];
            tensor<fp16, [1, 64, 1, 19200]> var_3477_cast_fp16_0, tensor<fp16, [1, 64, 1, 19200]> var_3477_cast_fp16_1 = split(axis = var_3477_axis_0, split_sizes = var_3477_split_sizes_0, x = out_339_cast_fp16)[name = string("op_3477_cast_fp16")];
            tensor<int32, [1]> x_651_axes_0 = const()[name = string("x_651_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 64, 19200]> x_651_cast_fp16 = squeeze(axes = x_651_axes_0, x = var_3477_cast_fp16_0)[name = string("x_651_cast_fp16")];
            int32 var_3482 = const()[name = string("op_3482"), val = int32(-1)];
            bool input_97_interleave_0 = const()[name = string("input_97_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 64, 19206]> input_97_cast_fp16 = concat(axis = var_3482, interleave = input_97_interleave_0, values = (var_792_cast_fp16_1, x_651_cast_fp16))[name = string("input_97_cast_fp16")];
            string x_653_pad_type_0 = const()[name = string("x_653_pad_type_0"), val = string("valid")];
            int32 x_653_groups_0 = const()[name = string("x_653_groups_0"), val = int32(64)];
            tensor<int32, [1]> x_653_strides_0 = const()[name = string("x_653_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_653_pad_0 = const()[name = string("x_653_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_653_dilations_0 = const()[name = string("x_653_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [64, 1, 7]> x_655_weight_0_to_fp16 = const()[name = string("x_655_weight_0_to_fp16"), val = tensor<fp16, [64, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687199104)))];
            tensor<fp16, [64]> x_655_bias_0_to_fp16 = const()[name = string("x_655_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687200064)))];
            tensor<fp16, [1, 64, 19200]> x_655_cast_fp16 = conv(bias = x_655_bias_0_to_fp16, dilations = x_653_dilations_0, groups = x_653_groups_0, pad = x_653_pad_0, pad_type = x_653_pad_type_0, strides = x_653_strides_0, weight = x_655_weight_0_to_fp16, x = input_97_cast_fp16)[name = string("x_655_cast_fp16")];
            tensor<int32, [3]> var_3505_begin_0 = const()[name = string("op_3505_begin_0"), val = tensor<int32, [3]>([0, 0, 19200])];
            tensor<int32, [3]> var_3505_end_0 = const()[name = string("op_3505_end_0"), val = tensor<int32, [3]>([1, 64, 19206])];
            tensor<bool, [3]> var_3505_end_mask_0 = const()[name = string("op_3505_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 64, 6]> var_3505_cast_fp16 = slice_by_index(begin = var_3505_begin_0, end = var_3505_end_0, end_mask = var_3505_end_mask_0, x = input_97_cast_fp16)[name = string("op_3505_cast_fp16")];
            tensor<fp16, [1, 64, 19200]> x_657_cast_fp16 = add(x = x_643_cast_fp16, y = x_655_cast_fp16)[name = string("x_657_cast_fp16")];
            int32 var_3515 = const()[name = string("op_3515"), val = int32(1)];
            tensor<int32, [1]> x_659_axes_0 = const()[name = string("x_659_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 64, 1, 19200]> x_659_cast_fp16 = expand_dims(axes = x_659_axes_0, x = x_657_cast_fp16)[name = string("x_659_cast_fp16")];
            fp16 const_109_promoted_to_fp16 = const()[name = string("const_109_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 64, 1, 19200]> var_3520_cast_fp16 = mul(x = x_659_cast_fp16, y = const_109_promoted_to_fp16)[name = string("op_3520_cast_fp16")];
            bool x_661_interleave_0 = const()[name = string("x_661_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 128, 1, 19200]> x_661_cast_fp16 = concat(axis = var_3515, interleave = x_661_interleave_0, values = (x_659_cast_fp16, var_3520_cast_fp16))[name = string("x_661_cast_fp16")];
            tensor<int32, [1]> out_345_axes_0 = const()[name = string("out_345_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3530_to_fp16 = const()[name = string("op_3530_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 128, 1, 19200]> out_345_cast_fp16 = layer_norm(axes = out_345_axes_0, epsilon = var_3530_to_fp16, x = x_661_cast_fp16)[name = string("out_345_cast_fp16")];
            tensor<fp16, [1, 128, 1, 1]> stages_5_1_ffn_norm_weight_to_fp16 = const()[name = string("stages_5_1_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687200256)))];
            tensor<fp16, [1, 128, 1, 19200]> out_347_cast_fp16 = mul(x = out_345_cast_fp16, y = stages_5_1_ffn_norm_weight_to_fp16)[name = string("out_347_cast_fp16")];
            tensor<int32, [2]> var_3536_split_sizes_0 = const()[name = string("op_3536_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3536_axis_0 = const()[name = string("op_3536_axis_0"), val = int32(1)];
            tensor<fp16, [1, 64, 1, 19200]> var_3536_cast_fp16_0, tensor<fp16, [1, 64, 1, 19200]> var_3536_cast_fp16_1 = split(axis = var_3536_axis_0, split_sizes = var_3536_split_sizes_0, x = out_347_cast_fp16)[name = string("op_3536_cast_fp16")];
            tensor<int32, [1]> x_665_axes_0 = const()[name = string("x_665_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 64, 19200]> x_665_cast_fp16 = squeeze(axes = x_665_axes_0, x = var_3536_cast_fp16_0)[name = string("x_665_cast_fp16")];
            string input_99_pad_type_0 = const()[name = string("input_99_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_99_strides_0 = const()[name = string("input_99_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_99_pad_0 = const()[name = string("input_99_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_99_dilations_0 = const()[name = string("input_99_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_99_groups_0 = const()[name = string("input_99_groups_0"), val = int32(1)];
            tensor<fp16, [256, 64, 1]> var_3542_to_fp16 = const()[name = string("op_3542_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687200576)))];
            tensor<fp16, [256]> stages_5_1_ffn_linear1_bias_to_fp16 = const()[name = string("stages_5_1_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687233408)))];
            tensor<fp16, [1, 256, 19200]> input_99_cast_fp16 = conv(bias = stages_5_1_ffn_linear1_bias_to_fp16, dilations = input_99_dilations_0, groups = input_99_groups_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = var_3542_to_fp16, x = x_665_cast_fp16)[name = string("input_99_cast_fp16")];
            string x_667_mode_0 = const()[name = string("x_667_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 256, 19200]> x_667_cast_fp16 = gelu(mode = x_667_mode_0, x = input_99_cast_fp16)[name = string("x_667_cast_fp16")];
            string x_669_pad_type_0 = const()[name = string("x_669_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_669_strides_0 = const()[name = string("x_669_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_669_pad_0 = const()[name = string("x_669_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_669_dilations_0 = const()[name = string("x_669_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_669_groups_0 = const()[name = string("x_669_groups_0"), val = int32(1)];
            tensor<fp16, [64, 256, 1]> x_671_weight_0_to_fp16 = const()[name = string("x_671_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687233984)))];
            tensor<fp16, [64]> x_671_bias_0_to_fp16 = const()[name = string("x_671_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687266816)))];
            tensor<fp16, [1, 64, 19200]> x_671_cast_fp16 = conv(bias = x_671_bias_0_to_fp16, dilations = x_669_dilations_0, groups = x_669_groups_0, pad = x_669_pad_0, pad_type = x_669_pad_type_0, strides = x_669_strides_0, weight = x_671_weight_0_to_fp16, x = x_667_cast_fp16)[name = string("x_671_cast_fp16")];
            tensor<fp16, [1, 64, 19200]> x_673_cast_fp16 = add(x = x_657_cast_fp16, y = x_671_cast_fp16)[name = string("x_673_cast_fp16")];
            int32 var_3571 = const()[name = string("op_3571"), val = int32(1)];
            tensor<int32, [1]> x_675_axes_0 = const()[name = string("x_675_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 64, 1, 19200]> x_675_cast_fp16 = expand_dims(axes = x_675_axes_0, x = x_673_cast_fp16)[name = string("x_675_cast_fp16")];
            fp16 const_110_promoted_to_fp16 = const()[name = string("const_110_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 64, 1, 19200]> var_3576_cast_fp16 = mul(x = x_675_cast_fp16, y = const_110_promoted_to_fp16)[name = string("op_3576_cast_fp16")];
            bool x_677_interleave_0 = const()[name = string("x_677_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 128, 1, 19200]> x_677_cast_fp16 = concat(axis = var_3571, interleave = x_677_interleave_0, values = (x_675_cast_fp16, var_3576_cast_fp16))[name = string("x_677_cast_fp16")];
            tensor<int32, [1]> out_353_axes_0 = const()[name = string("out_353_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3586_to_fp16 = const()[name = string("op_3586_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 128, 1, 19200]> out_353_cast_fp16 = layer_norm(axes = out_353_axes_0, epsilon = var_3586_to_fp16, x = x_677_cast_fp16)[name = string("out_353_cast_fp16")];
            tensor<fp16, [1, 128, 1, 1]> stages_5_2_norm_weight_to_fp16 = const()[name = string("stages_5_2_norm_weight_to_fp16"), val = tensor<fp16, [1, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687267008)))];
            tensor<fp16, [1, 128, 1, 19200]> out_355_cast_fp16 = mul(x = out_353_cast_fp16, y = stages_5_2_norm_weight_to_fp16)[name = string("out_355_cast_fp16")];
            tensor<int32, [2]> var_3592_split_sizes_0 = const()[name = string("op_3592_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3592_axis_0 = const()[name = string("op_3592_axis_0"), val = int32(1)];
            tensor<fp16, [1, 64, 1, 19200]> var_3592_cast_fp16_0, tensor<fp16, [1, 64, 1, 19200]> var_3592_cast_fp16_1 = split(axis = var_3592_axis_0, split_sizes = var_3592_split_sizes_0, x = out_355_cast_fp16)[name = string("op_3592_cast_fp16")];
            tensor<int32, [1]> x_681_axes_0 = const()[name = string("x_681_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 64, 19200]> x_681_cast_fp16 = squeeze(axes = x_681_axes_0, x = var_3592_cast_fp16_0)[name = string("x_681_cast_fp16")];
            int32 var_3597 = const()[name = string("op_3597"), val = int32(-1)];
            bool input_101_interleave_0 = const()[name = string("input_101_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 64, 19206]> input_101_cast_fp16 = concat(axis = var_3597, interleave = input_101_interleave_0, values = (var_792_cast_fp16_2, x_681_cast_fp16))[name = string("input_101_cast_fp16")];
            string x_683_pad_type_0 = const()[name = string("x_683_pad_type_0"), val = string("valid")];
            int32 x_683_groups_0 = const()[name = string("x_683_groups_0"), val = int32(64)];
            tensor<int32, [1]> x_683_strides_0 = const()[name = string("x_683_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_683_pad_0 = const()[name = string("x_683_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_683_dilations_0 = const()[name = string("x_683_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [64, 1, 7]> x_685_weight_0_to_fp16 = const()[name = string("x_685_weight_0_to_fp16"), val = tensor<fp16, [64, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687267328)))];
            tensor<fp16, [64]> x_685_bias_0_to_fp16 = const()[name = string("x_685_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687268288)))];
            tensor<fp16, [1, 64, 19200]> x_685_cast_fp16 = conv(bias = x_685_bias_0_to_fp16, dilations = x_683_dilations_0, groups = x_683_groups_0, pad = x_683_pad_0, pad_type = x_683_pad_type_0, strides = x_683_strides_0, weight = x_685_weight_0_to_fp16, x = input_101_cast_fp16)[name = string("x_685_cast_fp16")];
            tensor<int32, [3]> var_3620_begin_0 = const()[name = string("op_3620_begin_0"), val = tensor<int32, [3]>([0, 0, 19200])];
            tensor<int32, [3]> var_3620_end_0 = const()[name = string("op_3620_end_0"), val = tensor<int32, [3]>([1, 64, 19206])];
            tensor<bool, [3]> var_3620_end_mask_0 = const()[name = string("op_3620_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 64, 6]> var_3620_cast_fp16 = slice_by_index(begin = var_3620_begin_0, end = var_3620_end_0, end_mask = var_3620_end_mask_0, x = input_101_cast_fp16)[name = string("op_3620_cast_fp16")];
            tensor<fp16, [1, 64, 19200]> x_687_cast_fp16 = add(x = x_673_cast_fp16, y = x_685_cast_fp16)[name = string("x_687_cast_fp16")];
            int32 var_3630 = const()[name = string("op_3630"), val = int32(1)];
            tensor<int32, [1]> x_689_axes_0 = const()[name = string("x_689_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 64, 1, 19200]> x_689_cast_fp16 = expand_dims(axes = x_689_axes_0, x = x_687_cast_fp16)[name = string("x_689_cast_fp16")];
            fp16 const_113_promoted_to_fp16 = const()[name = string("const_113_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 64, 1, 19200]> var_3635_cast_fp16 = mul(x = x_689_cast_fp16, y = const_113_promoted_to_fp16)[name = string("op_3635_cast_fp16")];
            bool x_691_interleave_0 = const()[name = string("x_691_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 128, 1, 19200]> x_691_cast_fp16 = concat(axis = var_3630, interleave = x_691_interleave_0, values = (x_689_cast_fp16, var_3635_cast_fp16))[name = string("x_691_cast_fp16")];
            tensor<int32, [1]> out_361_axes_0 = const()[name = string("out_361_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3645_to_fp16 = const()[name = string("op_3645_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 128, 1, 19200]> out_361_cast_fp16 = layer_norm(axes = out_361_axes_0, epsilon = var_3645_to_fp16, x = x_691_cast_fp16)[name = string("out_361_cast_fp16")];
            tensor<fp16, [1, 128, 1, 1]> stages_5_2_ffn_norm_weight_to_fp16 = const()[name = string("stages_5_2_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687268480)))];
            tensor<fp16, [1, 128, 1, 19200]> out_363_cast_fp16 = mul(x = out_361_cast_fp16, y = stages_5_2_ffn_norm_weight_to_fp16)[name = string("out_363_cast_fp16")];
            tensor<int32, [2]> var_3651_split_sizes_0 = const()[name = string("op_3651_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3651_axis_0 = const()[name = string("op_3651_axis_0"), val = int32(1)];
            tensor<fp16, [1, 64, 1, 19200]> var_3651_cast_fp16_0, tensor<fp16, [1, 64, 1, 19200]> var_3651_cast_fp16_1 = split(axis = var_3651_axis_0, split_sizes = var_3651_split_sizes_0, x = out_363_cast_fp16)[name = string("op_3651_cast_fp16")];
            tensor<int32, [1]> x_695_axes_0 = const()[name = string("x_695_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 64, 19200]> x_695_cast_fp16 = squeeze(axes = x_695_axes_0, x = var_3651_cast_fp16_0)[name = string("x_695_cast_fp16")];
            string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)];
            tensor<fp16, [256, 64, 1]> var_3657_to_fp16 = const()[name = string("op_3657_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687268800)))];
            tensor<fp16, [256]> stages_5_2_ffn_linear1_bias_to_fp16 = const()[name = string("stages_5_2_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687301632)))];
            tensor<fp16, [1, 256, 19200]> input_103_cast_fp16 = conv(bias = stages_5_2_ffn_linear1_bias_to_fp16, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = var_3657_to_fp16, x = x_695_cast_fp16)[name = string("input_103_cast_fp16")];
            string x_697_mode_0 = const()[name = string("x_697_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 256, 19200]> x_697_cast_fp16 = gelu(mode = x_697_mode_0, x = input_103_cast_fp16)[name = string("x_697_cast_fp16")];
            string x_699_pad_type_0 = const()[name = string("x_699_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_699_strides_0 = const()[name = string("x_699_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_699_pad_0 = const()[name = string("x_699_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_699_dilations_0 = const()[name = string("x_699_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_699_groups_0 = const()[name = string("x_699_groups_0"), val = int32(1)];
            tensor<fp16, [64, 256, 1]> x_701_weight_0_to_fp16 = const()[name = string("x_701_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687302208)))];
            tensor<fp16, [64]> x_701_bias_0_to_fp16 = const()[name = string("x_701_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687335040)))];
            tensor<fp16, [1, 64, 19200]> x_701_cast_fp16 = conv(bias = x_701_bias_0_to_fp16, dilations = x_699_dilations_0, groups = x_699_groups_0, pad = x_699_pad_0, pad_type = x_699_pad_type_0, strides = x_699_strides_0, weight = x_701_weight_0_to_fp16, x = x_697_cast_fp16)[name = string("x_701_cast_fp16")];
            tensor<fp16, [1, 64, 19200]> x_703_cast_fp16 = add(x = x_687_cast_fp16, y = x_701_cast_fp16)[name = string("x_703_cast_fp16")];
            int32 var_3688 = const()[name = string("op_3688"), val = int32(-1)];
            bool input_105_interleave_0 = const()[name = string("input_105_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 64, 19203]> input_105_cast_fp16 = concat(axis = var_3688, interleave = input_105_interleave_0, values = (var_792_cast_fp16_3, x_703_cast_fp16))[name = string("input_105_cast_fp16")];
            string full_output_21_pad_type_0 = const()[name = string("full_output_21_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> full_output_21_strides_0 = const()[name = string("full_output_21_strides_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [2]> full_output_21_pad_0 = const()[name = string("full_output_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> full_output_21_dilations_0 = const()[name = string("full_output_21_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 full_output_21_groups_0 = const()[name = string("full_output_21_groups_0"), val = int32(1)];
            tensor<int32, [3]> full_output_21_has_output_shape_output_shape_0 = const()[name = string("full_output_21_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 32, 38408])];
            tensor<fp16, [64, 32, 4]> upsample_layers_6_0_convtr_convtr_weight_to_fp16 = const()[name = string("upsample_layers_6_0_convtr_convtr_weight_to_fp16"), val = tensor<fp16, [64, 32, 4]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687335232)))];
            tensor<fp16, [32]> upsample_layers_6_0_convtr_convtr_bias_to_fp16 = const()[name = string("upsample_layers_6_0_convtr_convtr_bias_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687351680)))];
            tensor<fp16, [1, 32, 38408]> full_output_21_has_output_shape_cast_fp16 = conv_transpose(bias = upsample_layers_6_0_convtr_convtr_bias_to_fp16, dilations = full_output_21_dilations_0, groups = full_output_21_groups_0, output_shape = full_output_21_has_output_shape_output_shape_0, pad = full_output_21_pad_0, pad_type = full_output_21_pad_type_0, strides = full_output_21_strides_0, weight = upsample_layers_6_0_convtr_convtr_weight_to_fp16, x = input_105_cast_fp16)[name = string("full_output_21_has_output_shape_cast_fp16")];
            tensor<int32, [3]> full_output_begin_0 = const()[name = string("full_output_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
            tensor<int32, [3]> full_output_end_0 = const()[name = string("full_output_end_0"), val = tensor<int32, [3]>([1, 32, 38406])];
            tensor<bool, [3]> full_output_end_mask_0 = const()[name = string("full_output_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
            tensor<fp16, [1, 32, 38406]> full_output_cast_fp16 = slice_by_index(begin = full_output_begin_0, end = full_output_end_0, end_mask = full_output_end_mask_0, x = full_output_21_has_output_shape_cast_fp16)[name = string("full_output_cast_fp16")];
            tensor<int32, [3]> x_705_begin_0 = const()[name = string("x_705_begin_0"), val = tensor<int32, [3]>([0, 0, 6])];
            tensor<int32, [3]> x_705_end_0 = const()[name = string("x_705_end_0"), val = tensor<int32, [3]>([1, 32, 38406])];
            tensor<bool, [3]> x_705_end_mask_0 = const()[name = string("x_705_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 32, 38400]> x_705_cast_fp16 = slice_by_index(begin = x_705_begin_0, end = x_705_end_0, end_mask = x_705_end_mask_0, x = full_output_cast_fp16)[name = string("x_705_cast_fp16")];
            tensor<int32, [3]> var_3722_begin_0 = const()[name = string("op_3722_begin_0"), val = tensor<int32, [3]>([0, 0, 19200])];
            tensor<int32, [3]> var_3722_end_0 = const()[name = string("op_3722_end_0"), val = tensor<int32, [3]>([1, 64, 19203])];
            tensor<bool, [3]> var_3722_end_mask_0 = const()[name = string("op_3722_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 64, 3]> var_3722_cast_fp16 = slice_by_index(begin = var_3722_begin_0, end = var_3722_end_0, end_mask = var_3722_end_mask_0, x = input_105_cast_fp16)[name = string("op_3722_cast_fp16")];
            int32 var_3727 = const()[name = string("op_3727"), val = int32(1)];
            tensor<int32, [1]> x_707_axes_0 = const()[name = string("x_707_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 32, 1, 38400]> x_707_cast_fp16 = expand_dims(axes = x_707_axes_0, x = x_705_cast_fp16)[name = string("x_707_cast_fp16")];
            fp16 const_118_promoted_to_fp16 = const()[name = string("const_118_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 32, 1, 38400]> var_3732_cast_fp16 = mul(x = x_707_cast_fp16, y = const_118_promoted_to_fp16)[name = string("op_3732_cast_fp16")];
            bool x_709_interleave_0 = const()[name = string("x_709_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 64, 1, 38400]> x_709_cast_fp16 = concat(axis = var_3727, interleave = x_709_interleave_0, values = (x_707_cast_fp16, var_3732_cast_fp16))[name = string("x_709_cast_fp16")];
            tensor<int32, [1]> out_369_axes_0 = const()[name = string("out_369_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3742_to_fp16 = const()[name = string("op_3742_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 64, 1, 38400]> out_369_cast_fp16 = layer_norm(axes = out_369_axes_0, epsilon = var_3742_to_fp16, x = x_709_cast_fp16)[name = string("out_369_cast_fp16")];
            tensor<fp16, [1, 64, 1, 1]> stages_6_0_norm_weight_to_fp16 = const()[name = string("stages_6_0_norm_weight_to_fp16"), val = tensor<fp16, [1, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687351808)))];
            tensor<fp16, [1, 64, 1, 38400]> out_371_cast_fp16 = mul(x = out_369_cast_fp16, y = stages_6_0_norm_weight_to_fp16)[name = string("out_371_cast_fp16")];
            tensor<int32, [2]> var_3748_split_sizes_0 = const()[name = string("op_3748_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            int32 var_3748_axis_0 = const()[name = string("op_3748_axis_0"), val = int32(1)];
            tensor<fp16, [1, 32, 1, 38400]> var_3748_cast_fp16_0, tensor<fp16, [1, 32, 1, 38400]> var_3748_cast_fp16_1 = split(axis = var_3748_axis_0, split_sizes = var_3748_split_sizes_0, x = out_371_cast_fp16)[name = string("op_3748_cast_fp16")];
            tensor<int32, [1]> x_713_axes_0 = const()[name = string("x_713_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 32, 38400]> x_713_cast_fp16 = squeeze(axes = x_713_axes_0, x = var_3748_cast_fp16_0)[name = string("x_713_cast_fp16")];
            int32 var_3753 = const()[name = string("op_3753"), val = int32(-1)];
            bool input_107_interleave_0 = const()[name = string("input_107_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 32, 38406]> input_107_cast_fp16 = concat(axis = var_3753, interleave = input_107_interleave_0, values = (var_803_cast_fp16_0, x_713_cast_fp16))[name = string("input_107_cast_fp16")];
            string x_715_pad_type_0 = const()[name = string("x_715_pad_type_0"), val = string("valid")];
            int32 x_715_groups_0 = const()[name = string("x_715_groups_0"), val = int32(32)];
            tensor<int32, [1]> x_715_strides_0 = const()[name = string("x_715_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_715_pad_0 = const()[name = string("x_715_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_715_dilations_0 = const()[name = string("x_715_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [32, 1, 7]> x_717_weight_0_to_fp16 = const()[name = string("x_717_weight_0_to_fp16"), val = tensor<fp16, [32, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687352000)))];
            tensor<fp16, [32]> x_717_bias_0_to_fp16 = const()[name = string("x_717_bias_0_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687352512)))];
            tensor<fp16, [1, 32, 38400]> x_717_cast_fp16 = conv(bias = x_717_bias_0_to_fp16, dilations = x_715_dilations_0, groups = x_715_groups_0, pad = x_715_pad_0, pad_type = x_715_pad_type_0, strides = x_715_strides_0, weight = x_717_weight_0_to_fp16, x = input_107_cast_fp16)[name = string("x_717_cast_fp16")];
            tensor<int32, [3]> var_3776_begin_0 = const()[name = string("op_3776_begin_0"), val = tensor<int32, [3]>([0, 0, 38400])];
            tensor<int32, [3]> var_3776_end_0 = const()[name = string("op_3776_end_0"), val = tensor<int32, [3]>([1, 32, 38406])];
            tensor<bool, [3]> var_3776_end_mask_0 = const()[name = string("op_3776_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 32, 6]> var_3776_cast_fp16 = slice_by_index(begin = var_3776_begin_0, end = var_3776_end_0, end_mask = var_3776_end_mask_0, x = input_107_cast_fp16)[name = string("op_3776_cast_fp16")];
            tensor<fp16, [1, 32, 38400]> x_719_cast_fp16 = add(x = x_705_cast_fp16, y = x_717_cast_fp16)[name = string("x_719_cast_fp16")];
            int32 var_3786 = const()[name = string("op_3786"), val = int32(1)];
            tensor<int32, [1]> x_721_axes_0 = const()[name = string("x_721_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 32, 1, 38400]> x_721_cast_fp16 = expand_dims(axes = x_721_axes_0, x = x_719_cast_fp16)[name = string("x_721_cast_fp16")];
            fp16 const_121_promoted_to_fp16 = const()[name = string("const_121_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 32, 1, 38400]> var_3791_cast_fp16 = mul(x = x_721_cast_fp16, y = const_121_promoted_to_fp16)[name = string("op_3791_cast_fp16")];
            bool x_723_interleave_0 = const()[name = string("x_723_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 64, 1, 38400]> x_723_cast_fp16 = concat(axis = var_3786, interleave = x_723_interleave_0, values = (x_721_cast_fp16, var_3791_cast_fp16))[name = string("x_723_cast_fp16")];
            tensor<int32, [1]> out_377_axes_0 = const()[name = string("out_377_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3801_to_fp16 = const()[name = string("op_3801_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 64, 1, 38400]> out_377_cast_fp16 = layer_norm(axes = out_377_axes_0, epsilon = var_3801_to_fp16, x = x_723_cast_fp16)[name = string("out_377_cast_fp16")];
            tensor<fp16, [1, 64, 1, 1]> stages_6_0_ffn_norm_weight_to_fp16 = const()[name = string("stages_6_0_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687352640)))];
            tensor<fp16, [1, 64, 1, 38400]> out_379_cast_fp16 = mul(x = out_377_cast_fp16, y = stages_6_0_ffn_norm_weight_to_fp16)[name = string("out_379_cast_fp16")];
            tensor<int32, [2]> var_3807_split_sizes_0 = const()[name = string("op_3807_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            int32 var_3807_axis_0 = const()[name = string("op_3807_axis_0"), val = int32(1)];
            tensor<fp16, [1, 32, 1, 38400]> var_3807_cast_fp16_0, tensor<fp16, [1, 32, 1, 38400]> var_3807_cast_fp16_1 = split(axis = var_3807_axis_0, split_sizes = var_3807_split_sizes_0, x = out_379_cast_fp16)[name = string("op_3807_cast_fp16")];
            tensor<int32, [1]> x_727_axes_0 = const()[name = string("x_727_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 32, 38400]> x_727_cast_fp16 = squeeze(axes = x_727_axes_0, x = var_3807_cast_fp16_0)[name = string("x_727_cast_fp16")];
            string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1)];
            tensor<fp16, [128, 32, 1]> var_3813_to_fp16 = const()[name = string("op_3813_to_fp16"), val = tensor<fp16, [128, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687352832)))];
            tensor<fp16, [128]> stages_6_0_ffn_linear1_bias_to_fp16 = const()[name = string("stages_6_0_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687361088)))];
            tensor<fp16, [1, 128, 38400]> input_109_cast_fp16 = conv(bias = stages_6_0_ffn_linear1_bias_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = var_3813_to_fp16, x = x_727_cast_fp16)[name = string("input_109_cast_fp16")];
            string x_729_mode_0 = const()[name = string("x_729_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 128, 38400]> x_729_cast_fp16 = gelu(mode = x_729_mode_0, x = input_109_cast_fp16)[name = string("x_729_cast_fp16")];
            string x_731_pad_type_0 = const()[name = string("x_731_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_731_strides_0 = const()[name = string("x_731_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_731_pad_0 = const()[name = string("x_731_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_731_dilations_0 = const()[name = string("x_731_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_731_groups_0 = const()[name = string("x_731_groups_0"), val = int32(1)];
            tensor<fp16, [32, 128, 1]> x_733_weight_0_to_fp16 = const()[name = string("x_733_weight_0_to_fp16"), val = tensor<fp16, [32, 128, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687361408)))];
            tensor<fp16, [32]> x_733_bias_0_to_fp16 = const()[name = string("x_733_bias_0_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687369664)))];
            tensor<fp16, [1, 32, 38400]> x_733_cast_fp16 = conv(bias = x_733_bias_0_to_fp16, dilations = x_731_dilations_0, groups = x_731_groups_0, pad = x_731_pad_0, pad_type = x_731_pad_type_0, strides = x_731_strides_0, weight = x_733_weight_0_to_fp16, x = x_729_cast_fp16)[name = string("x_733_cast_fp16")];
            tensor<fp16, [1, 32, 38400]> x_735_cast_fp16 = add(x = x_719_cast_fp16, y = x_733_cast_fp16)[name = string("x_735_cast_fp16")];
            int32 var_3842 = const()[name = string("op_3842"), val = int32(1)];
            tensor<int32, [1]> x_737_axes_0 = const()[name = string("x_737_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 32, 1, 38400]> x_737_cast_fp16 = expand_dims(axes = x_737_axes_0, x = x_735_cast_fp16)[name = string("x_737_cast_fp16")];
            fp16 const_122_promoted_to_fp16 = const()[name = string("const_122_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 32, 1, 38400]> var_3847_cast_fp16 = mul(x = x_737_cast_fp16, y = const_122_promoted_to_fp16)[name = string("op_3847_cast_fp16")];
            bool x_739_interleave_0 = const()[name = string("x_739_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 64, 1, 38400]> x_739_cast_fp16 = concat(axis = var_3842, interleave = x_739_interleave_0, values = (x_737_cast_fp16, var_3847_cast_fp16))[name = string("x_739_cast_fp16")];
            tensor<int32, [1]> out_385_axes_0 = const()[name = string("out_385_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3857_to_fp16 = const()[name = string("op_3857_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 64, 1, 38400]> out_385_cast_fp16 = layer_norm(axes = out_385_axes_0, epsilon = var_3857_to_fp16, x = x_739_cast_fp16)[name = string("out_385_cast_fp16")];
            tensor<fp16, [1, 64, 1, 1]> stages_6_1_norm_weight_to_fp16 = const()[name = string("stages_6_1_norm_weight_to_fp16"), val = tensor<fp16, [1, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687369792)))];
            tensor<fp16, [1, 64, 1, 38400]> out_387_cast_fp16 = mul(x = out_385_cast_fp16, y = stages_6_1_norm_weight_to_fp16)[name = string("out_387_cast_fp16")];
            tensor<int32, [2]> var_3863_split_sizes_0 = const()[name = string("op_3863_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            int32 var_3863_axis_0 = const()[name = string("op_3863_axis_0"), val = int32(1)];
            tensor<fp16, [1, 32, 1, 38400]> var_3863_cast_fp16_0, tensor<fp16, [1, 32, 1, 38400]> var_3863_cast_fp16_1 = split(axis = var_3863_axis_0, split_sizes = var_3863_split_sizes_0, x = out_387_cast_fp16)[name = string("op_3863_cast_fp16")];
            tensor<int32, [1]> x_743_axes_0 = const()[name = string("x_743_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 32, 38400]> x_743_cast_fp16 = squeeze(axes = x_743_axes_0, x = var_3863_cast_fp16_0)[name = string("x_743_cast_fp16")];
            int32 var_3868 = const()[name = string("op_3868"), val = int32(-1)];
            bool input_111_interleave_0 = const()[name = string("input_111_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 32, 38406]> input_111_cast_fp16 = concat(axis = var_3868, interleave = input_111_interleave_0, values = (var_803_cast_fp16_1, x_743_cast_fp16))[name = string("input_111_cast_fp16")];
            string x_745_pad_type_0 = const()[name = string("x_745_pad_type_0"), val = string("valid")];
            int32 x_745_groups_0 = const()[name = string("x_745_groups_0"), val = int32(32)];
            tensor<int32, [1]> x_745_strides_0 = const()[name = string("x_745_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_745_pad_0 = const()[name = string("x_745_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_745_dilations_0 = const()[name = string("x_745_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [32, 1, 7]> x_747_weight_0_to_fp16 = const()[name = string("x_747_weight_0_to_fp16"), val = tensor<fp16, [32, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687369984)))];
            tensor<fp16, [32]> x_747_bias_0_to_fp16 = const()[name = string("x_747_bias_0_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687370496)))];
            tensor<fp16, [1, 32, 38400]> x_747_cast_fp16 = conv(bias = x_747_bias_0_to_fp16, dilations = x_745_dilations_0, groups = x_745_groups_0, pad = x_745_pad_0, pad_type = x_745_pad_type_0, strides = x_745_strides_0, weight = x_747_weight_0_to_fp16, x = input_111_cast_fp16)[name = string("x_747_cast_fp16")];
            tensor<int32, [3]> var_3891_begin_0 = const()[name = string("op_3891_begin_0"), val = tensor<int32, [3]>([0, 0, 38400])];
            tensor<int32, [3]> var_3891_end_0 = const()[name = string("op_3891_end_0"), val = tensor<int32, [3]>([1, 32, 38406])];
            tensor<bool, [3]> var_3891_end_mask_0 = const()[name = string("op_3891_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 32, 6]> var_3891_cast_fp16 = slice_by_index(begin = var_3891_begin_0, end = var_3891_end_0, end_mask = var_3891_end_mask_0, x = input_111_cast_fp16)[name = string("op_3891_cast_fp16")];
            tensor<fp16, [1, 32, 38400]> x_749_cast_fp16 = add(x = x_735_cast_fp16, y = x_747_cast_fp16)[name = string("x_749_cast_fp16")];
            int32 var_3901 = const()[name = string("op_3901"), val = int32(1)];
            tensor<int32, [1]> x_751_axes_0 = const()[name = string("x_751_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 32, 1, 38400]> x_751_cast_fp16 = expand_dims(axes = x_751_axes_0, x = x_749_cast_fp16)[name = string("x_751_cast_fp16")];
            fp16 const_125_promoted_to_fp16 = const()[name = string("const_125_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 32, 1, 38400]> var_3906_cast_fp16 = mul(x = x_751_cast_fp16, y = const_125_promoted_to_fp16)[name = string("op_3906_cast_fp16")];
            bool x_753_interleave_0 = const()[name = string("x_753_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 64, 1, 38400]> x_753_cast_fp16 = concat(axis = var_3901, interleave = x_753_interleave_0, values = (x_751_cast_fp16, var_3906_cast_fp16))[name = string("x_753_cast_fp16")];
            tensor<int32, [1]> out_393_axes_0 = const()[name = string("out_393_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3916_to_fp16 = const()[name = string("op_3916_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 64, 1, 38400]> out_393_cast_fp16 = layer_norm(axes = out_393_axes_0, epsilon = var_3916_to_fp16, x = x_753_cast_fp16)[name = string("out_393_cast_fp16")];
            tensor<fp16, [1, 64, 1, 1]> stages_6_1_ffn_norm_weight_to_fp16 = const()[name = string("stages_6_1_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687370624)))];
            tensor<fp16, [1, 64, 1, 38400]> out_395_cast_fp16 = mul(x = out_393_cast_fp16, y = stages_6_1_ffn_norm_weight_to_fp16)[name = string("out_395_cast_fp16")];
            tensor<int32, [2]> var_3922_split_sizes_0 = const()[name = string("op_3922_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            int32 var_3922_axis_0 = const()[name = string("op_3922_axis_0"), val = int32(1)];
            tensor<fp16, [1, 32, 1, 38400]> var_3922_cast_fp16_0, tensor<fp16, [1, 32, 1, 38400]> var_3922_cast_fp16_1 = split(axis = var_3922_axis_0, split_sizes = var_3922_split_sizes_0, x = out_395_cast_fp16)[name = string("op_3922_cast_fp16")];
            tensor<int32, [1]> x_757_axes_0 = const()[name = string("x_757_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 32, 38400]> x_757_cast_fp16 = squeeze(axes = x_757_axes_0, x = var_3922_cast_fp16_0)[name = string("x_757_cast_fp16")];
            string input_113_pad_type_0 = const()[name = string("input_113_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_113_strides_0 = const()[name = string("input_113_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_113_pad_0 = const()[name = string("input_113_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_113_dilations_0 = const()[name = string("input_113_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_113_groups_0 = const()[name = string("input_113_groups_0"), val = int32(1)];
            tensor<fp16, [128, 32, 1]> var_3928_to_fp16 = const()[name = string("op_3928_to_fp16"), val = tensor<fp16, [128, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687370816)))];
            tensor<fp16, [128]> stages_6_1_ffn_linear1_bias_to_fp16 = const()[name = string("stages_6_1_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687379072)))];
            tensor<fp16, [1, 128, 38400]> input_113_cast_fp16 = conv(bias = stages_6_1_ffn_linear1_bias_to_fp16, dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = var_3928_to_fp16, x = x_757_cast_fp16)[name = string("input_113_cast_fp16")];
            string x_759_mode_0 = const()[name = string("x_759_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 128, 38400]> x_759_cast_fp16 = gelu(mode = x_759_mode_0, x = input_113_cast_fp16)[name = string("x_759_cast_fp16")];
            string x_761_pad_type_0 = const()[name = string("x_761_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_761_strides_0 = const()[name = string("x_761_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_761_pad_0 = const()[name = string("x_761_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_761_dilations_0 = const()[name = string("x_761_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_761_groups_0 = const()[name = string("x_761_groups_0"), val = int32(1)];
            tensor<fp16, [32, 128, 1]> x_763_weight_0_to_fp16 = const()[name = string("x_763_weight_0_to_fp16"), val = tensor<fp16, [32, 128, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687379392)))];
            tensor<fp16, [32]> x_763_bias_0_to_fp16 = const()[name = string("x_763_bias_0_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687387648)))];
            tensor<fp16, [1, 32, 38400]> x_763_cast_fp16 = conv(bias = x_763_bias_0_to_fp16, dilations = x_761_dilations_0, groups = x_761_groups_0, pad = x_761_pad_0, pad_type = x_761_pad_type_0, strides = x_761_strides_0, weight = x_763_weight_0_to_fp16, x = x_759_cast_fp16)[name = string("x_763_cast_fp16")];
            tensor<fp16, [1, 32, 38400]> x_765_cast_fp16 = add(x = x_749_cast_fp16, y = x_763_cast_fp16)[name = string("x_765_cast_fp16")];
            int32 var_3957 = const()[name = string("op_3957"), val = int32(1)];
            tensor<int32, [1]> x_767_axes_0 = const()[name = string("x_767_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 32, 1, 38400]> x_767_cast_fp16 = expand_dims(axes = x_767_axes_0, x = x_765_cast_fp16)[name = string("x_767_cast_fp16")];
            fp16 const_126_promoted_to_fp16 = const()[name = string("const_126_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 32, 1, 38400]> var_3962_cast_fp16 = mul(x = x_767_cast_fp16, y = const_126_promoted_to_fp16)[name = string("op_3962_cast_fp16")];
            bool x_769_interleave_0 = const()[name = string("x_769_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 64, 1, 38400]> x_769_cast_fp16 = concat(axis = var_3957, interleave = x_769_interleave_0, values = (x_767_cast_fp16, var_3962_cast_fp16))[name = string("x_769_cast_fp16")];
            tensor<int32, [1]> out_401_axes_0 = const()[name = string("out_401_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_3972_to_fp16 = const()[name = string("op_3972_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 64, 1, 38400]> out_401_cast_fp16 = layer_norm(axes = out_401_axes_0, epsilon = var_3972_to_fp16, x = x_769_cast_fp16)[name = string("out_401_cast_fp16")];
            tensor<fp16, [1, 64, 1, 1]> stages_6_2_norm_weight_to_fp16 = const()[name = string("stages_6_2_norm_weight_to_fp16"), val = tensor<fp16, [1, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687387776)))];
            tensor<fp16, [1, 64, 1, 38400]> out_403_cast_fp16 = mul(x = out_401_cast_fp16, y = stages_6_2_norm_weight_to_fp16)[name = string("out_403_cast_fp16")];
            tensor<int32, [2]> var_3978_split_sizes_0 = const()[name = string("op_3978_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            int32 var_3978_axis_0 = const()[name = string("op_3978_axis_0"), val = int32(1)];
            tensor<fp16, [1, 32, 1, 38400]> var_3978_cast_fp16_0, tensor<fp16, [1, 32, 1, 38400]> var_3978_cast_fp16_1 = split(axis = var_3978_axis_0, split_sizes = var_3978_split_sizes_0, x = out_403_cast_fp16)[name = string("op_3978_cast_fp16")];
            tensor<int32, [1]> x_773_axes_0 = const()[name = string("x_773_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 32, 38400]> x_773_cast_fp16 = squeeze(axes = x_773_axes_0, x = var_3978_cast_fp16_0)[name = string("x_773_cast_fp16")];
            int32 var_3983 = const()[name = string("op_3983"), val = int32(-1)];
            bool input_115_interleave_0 = const()[name = string("input_115_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 32, 38406]> input_115_cast_fp16 = concat(axis = var_3983, interleave = input_115_interleave_0, values = (var_803_cast_fp16_2, x_773_cast_fp16))[name = string("input_115_cast_fp16")];
            string x_775_pad_type_0 = const()[name = string("x_775_pad_type_0"), val = string("valid")];
            int32 x_775_groups_0 = const()[name = string("x_775_groups_0"), val = int32(32)];
            tensor<int32, [1]> x_775_strides_0 = const()[name = string("x_775_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_775_pad_0 = const()[name = string("x_775_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_775_dilations_0 = const()[name = string("x_775_dilations_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [32, 1, 7]> x_777_weight_0_to_fp16 = const()[name = string("x_777_weight_0_to_fp16"), val = tensor<fp16, [32, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687387968)))];
            tensor<fp16, [32]> x_777_bias_0_to_fp16 = const()[name = string("x_777_bias_0_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687388480)))];
            tensor<fp16, [1, 32, 38400]> x_777_cast_fp16 = conv(bias = x_777_bias_0_to_fp16, dilations = x_775_dilations_0, groups = x_775_groups_0, pad = x_775_pad_0, pad_type = x_775_pad_type_0, strides = x_775_strides_0, weight = x_777_weight_0_to_fp16, x = input_115_cast_fp16)[name = string("x_777_cast_fp16")];
            tensor<int32, [3]> var_4006_begin_0 = const()[name = string("op_4006_begin_0"), val = tensor<int32, [3]>([0, 0, 38400])];
            tensor<int32, [3]> var_4006_end_0 = const()[name = string("op_4006_end_0"), val = tensor<int32, [3]>([1, 32, 38406])];
            tensor<bool, [3]> var_4006_end_mask_0 = const()[name = string("op_4006_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 32, 6]> var_4006_cast_fp16 = slice_by_index(begin = var_4006_begin_0, end = var_4006_end_0, end_mask = var_4006_end_mask_0, x = input_115_cast_fp16)[name = string("op_4006_cast_fp16")];
            tensor<fp16, [1, 32, 38400]> x_779_cast_fp16 = add(x = x_765_cast_fp16, y = x_777_cast_fp16)[name = string("x_779_cast_fp16")];
            int32 var_4016 = const()[name = string("op_4016"), val = int32(1)];
            tensor<int32, [1]> x_781_axes_0 = const()[name = string("x_781_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 32, 1, 38400]> x_781_cast_fp16 = expand_dims(axes = x_781_axes_0, x = x_779_cast_fp16)[name = string("x_781_cast_fp16")];
            fp16 const_129_promoted_to_fp16 = const()[name = string("const_129_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 32, 1, 38400]> var_4021_cast_fp16 = mul(x = x_781_cast_fp16, y = const_129_promoted_to_fp16)[name = string("op_4021_cast_fp16")];
            bool x_783_interleave_0 = const()[name = string("x_783_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 64, 1, 38400]> x_783_cast_fp16 = concat(axis = var_4016, interleave = x_783_interleave_0, values = (x_781_cast_fp16, var_4021_cast_fp16))[name = string("x_783_cast_fp16")];
            tensor<int32, [1]> out_409_axes_0 = const()[name = string("out_409_axes_0"), val = tensor<int32, [1]>([1])];
            fp16 var_4031_to_fp16 = const()[name = string("op_4031_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 64, 1, 38400]> out_409_cast_fp16 = layer_norm(axes = out_409_axes_0, epsilon = var_4031_to_fp16, x = x_783_cast_fp16)[name = string("out_409_cast_fp16")];
            tensor<fp16, [1, 64, 1, 1]> stages_6_2_ffn_norm_weight_to_fp16 = const()[name = string("stages_6_2_ffn_norm_weight_to_fp16"), val = tensor<fp16, [1, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687388608)))];
            tensor<fp16, [1, 64, 1, 38400]> out_411_cast_fp16 = mul(x = out_409_cast_fp16, y = stages_6_2_ffn_norm_weight_to_fp16)[name = string("out_411_cast_fp16")];
            tensor<int32, [2]> var_4037_split_sizes_0 = const()[name = string("op_4037_split_sizes_0"), val = tensor<int32, [2]>([32, 32])];
            int32 var_4037_axis_0 = const()[name = string("op_4037_axis_0"), val = int32(1)];
            tensor<fp16, [1, 32, 1, 38400]> var_4037_cast_fp16_0, tensor<fp16, [1, 32, 1, 38400]> var_4037_cast_fp16_1 = split(axis = var_4037_axis_0, split_sizes = var_4037_split_sizes_0, x = out_411_cast_fp16)[name = string("op_4037_cast_fp16")];
            tensor<int32, [1]> x_787_axes_0 = const()[name = string("x_787_axes_0"), val = tensor<int32, [1]>([-2])];
            tensor<fp16, [1, 32, 38400]> x_787_cast_fp16 = squeeze(axes = x_787_axes_0, x = var_4037_cast_fp16_0)[name = string("x_787_cast_fp16")];
            string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)];
            tensor<fp16, [128, 32, 1]> var_4043_to_fp16 = const()[name = string("op_4043_to_fp16"), val = tensor<fp16, [128, 32, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687388800)))];
            tensor<fp16, [128]> stages_6_2_ffn_linear1_bias_to_fp16 = const()[name = string("stages_6_2_ffn_linear1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687397056)))];
            tensor<fp16, [1, 128, 38400]> input_117_cast_fp16 = conv(bias = stages_6_2_ffn_linear1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = var_4043_to_fp16, x = x_787_cast_fp16)[name = string("input_117_cast_fp16")];
            string x_789_mode_0 = const()[name = string("x_789_mode_0"), val = string("EXACT")];
            tensor<fp16, [1, 128, 38400]> x_789_cast_fp16 = gelu(mode = x_789_mode_0, x = input_117_cast_fp16)[name = string("x_789_cast_fp16")];
            string x_791_pad_type_0 = const()[name = string("x_791_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> x_791_strides_0 = const()[name = string("x_791_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> x_791_pad_0 = const()[name = string("x_791_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> x_791_dilations_0 = const()[name = string("x_791_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 x_791_groups_0 = const()[name = string("x_791_groups_0"), val = int32(1)];
            tensor<fp16, [32, 128, 1]> x_793_weight_0_to_fp16 = const()[name = string("x_793_weight_0_to_fp16"), val = tensor<fp16, [32, 128, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687397376)))];
            tensor<fp16, [32]> x_793_bias_0_to_fp16 = const()[name = string("x_793_bias_0_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687405632)))];
            tensor<fp16, [1, 32, 38400]> x_793_cast_fp16 = conv(bias = x_793_bias_0_to_fp16, dilations = x_791_dilations_0, groups = x_791_groups_0, pad = x_791_pad_0, pad_type = x_791_pad_type_0, strides = x_791_strides_0, weight = x_793_weight_0_to_fp16, x = x_789_cast_fp16)[name = string("x_793_cast_fp16")];
            tensor<fp16, [1, 32, 38400]> x_cast_fp16 = add(x = x_779_cast_fp16, y = x_793_cast_fp16)[name = string("x_cast_fp16")];
            int32 var_4069 = const()[name = string("op_4069"), val = int32(-1)];
            bool input_interleave_0 = const()[name = string("input_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 32, 38406]> input_cast_fp16 = concat(axis = var_4069, interleave = input_interleave_0, values = (var_803_cast_fp16_3, x_cast_fp16))[name = string("input_cast_fp16")];
            string var_4082_pad_type_0 = const()[name = string("op_4082_pad_type_0"), val = string("valid")];
            tensor<int32, [1]> var_4082_strides_0 = const()[name = string("op_4082_strides_0"), val = tensor<int32, [1]>([1])];
            tensor<int32, [2]> var_4082_pad_0 = const()[name = string("op_4082_pad_0"), val = tensor<int32, [2]>([0, 0])];
            tensor<int32, [1]> var_4082_dilations_0 = const()[name = string("op_4082_dilations_0"), val = tensor<int32, [1]>([1])];
            int32 var_4082_groups_0 = const()[name = string("op_4082_groups_0"), val = int32(1)];
            tensor<fp16, [1, 32, 7]> head_conv_conv_weight_to_fp16 = const()[name = string("head_conv_conv_weight_to_fp16"), val = tensor<fp16, [1, 32, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(687405760)))];
            tensor<fp16, [1]> head_conv_conv_bias_to_fp16 = const()[name = string("head_conv_conv_bias_to_fp16"), val = tensor<fp16, [1]>([0x1.f8p-19])];
            tensor<fp16, [1, 1, 38400]> output = conv(bias = head_conv_conv_bias_to_fp16, dilations = var_4082_dilations_0, groups = var_4082_groups_0, pad = var_4082_pad_0, pad_type = var_4082_pad_type_0, strides = var_4082_strides_0, weight = head_conv_conv_weight_to_fp16, x = input_cast_fp16)[name = string("op_4082_cast_fp16")];
            tensor<int32, [3]> var_4091_begin_0 = const()[name = string("op_4091_begin_0"), val = tensor<int32, [3]>([0, 0, 38400])];
            tensor<int32, [3]> var_4091_end_0 = const()[name = string("op_4091_end_0"), val = tensor<int32, [3]>([1, 32, 38406])];
            tensor<bool, [3]> var_4091_end_mask_0 = const()[name = string("op_4091_end_mask_0"), val = tensor<bool, [3]>([true, true, true])];
            tensor<fp16, [1, 32, 6]> var_4091_cast_fp16 = slice_by_index(begin = var_4091_begin_0, end = var_4091_end_0, end_mask = var_4091_end_mask_0, x = input_cast_fp16)[name = string("op_4091_cast_fp16")];
            int32 var_4093 = const()[name = string("op_4093"), val = int32(-1)];
            bool var_4094_interleave_0 = const()[name = string("op_4094_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 64, 6]> new_cache_0 = concat(axis = var_4093, interleave = var_4094_interleave_0, values = var_831_cast_fp16)[name = string("op_4094_cast_fp16")];
            int32 var_4096 = const()[name = string("op_4096"), val = int32(-1)];
            bool var_4097_interleave_0 = const()[name = string("op_4097_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2048, 63]> new_cache_1 = concat(axis = var_4096, interleave = var_4097_interleave_0, values = (var_885_cast_fp16, var_1000_cast_fp16, var_1115_cast_fp16, var_1230_cast_fp16, var_1345_cast_fp16, var_1460_cast_fp16, var_1575_cast_fp16, var_1690_cast_fp16, var_1792_cast_fp16))[name = string("op_4097_cast_fp16")];
            int32 var_4099 = const()[name = string("op_4099"), val = int32(-1)];
            bool var_4100_interleave_0 = const()[name = string("op_4100_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 1024, 27]> new_cache_2 = concat(axis = var_4099, interleave = var_4100_interleave_0, values = (var_1846_cast_fp16, var_1961_cast_fp16, var_2076_cast_fp16, var_2178_cast_fp16))[name = string("op_4100_cast_fp16")];
            int32 var_4102 = const()[name = string("op_4102"), val = int32(-1)];
            bool var_4103_interleave_0 = const()[name = string("op_4103_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 512, 27]> new_cache_3 = concat(axis = var_4102, interleave = var_4103_interleave_0, values = (var_2232_cast_fp16, var_2347_cast_fp16, var_2462_cast_fp16, var_2564_cast_fp16))[name = string("op_4103_cast_fp16")];
            int32 var_4105 = const()[name = string("op_4105"), val = int32(-1)];
            bool var_4106_interleave_0 = const()[name = string("op_4106_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 256, 25]> new_cache_4 = concat(axis = var_4105, interleave = var_4106_interleave_0, values = (var_2618_cast_fp16, var_2733_cast_fp16, var_2848_cast_fp16, var_2950_cast_fp16))[name = string("op_4106_cast_fp16")];
            int32 var_4108 = const()[name = string("op_4108"), val = int32(-1)];
            bool var_4109_interleave_0 = const()[name = string("op_4109_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 128, 21]> new_cache_5 = concat(axis = var_4108, interleave = var_4109_interleave_0, values = (var_3004_cast_fp16, var_3119_cast_fp16, var_3234_cast_fp16, var_3336_cast_fp16))[name = string("op_4109_cast_fp16")];
            int32 var_4111 = const()[name = string("op_4111"), val = int32(-1)];
            bool var_4112_interleave_0 = const()[name = string("op_4112_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 64, 21]> new_cache_6 = concat(axis = var_4111, interleave = var_4112_interleave_0, values = (var_3390_cast_fp16, var_3505_cast_fp16, var_3620_cast_fp16, var_3722_cast_fp16))[name = string("op_4112_cast_fp16")];
            int32 var_4114 = const()[name = string("op_4114"), val = int32(-1)];
            bool var_4115_interleave_0 = const()[name = string("op_4115_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 32, 24]> new_cache_7 = concat(axis = var_4114, interleave = var_4115_interleave_0, values = (var_3776_cast_fp16, var_3891_cast_fp16, var_4006_cast_fp16, var_4091_cast_fp16))[name = string("op_4115_cast_fp16")];
        } -> (output, new_cache_0, new_cache_1, new_cache_2, new_cache_3, new_cache_4, new_cache_5, new_cache_6, new_cache_7);
}