program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.11.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
{
    func main<ios18>(tensor<fp32, [1, 1, 80, 231]> mel) {
            int32 var_5 = const()[name = string("op_5"), val = int32(-1)];
            fp32 var_10 = const()[name = string("op_10"), val = fp32(0x1.99999ap-3)];
            string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_1_strides_0 = const()[name = string("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_1_dilations_0 = const()[name = string("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_1_groups_0 = const()[name = string("input_1_groups_0"), val = int32(1)];
            string mel_to_fp16_dtype_0 = const()[name = string("mel_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [64, 1, 3, 3]> weight_3_to_fp16 = const()[name = string("weight_3_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
            tensor<fp16, [64]> style_encoder_shared_0_bias_to_fp16 = const()[name = string("style_encoder_shared_0_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1280)))];
            tensor<fp16, [1, 1, 80, 231]> mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = string("cast_121")];
            tensor<fp16, [1, 64, 80, 231]> input_1_cast_fp16 = conv(bias = style_encoder_shared_0_bias_to_fp16, dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = weight_3_to_fp16, x = mel_to_fp16)[name = string("input_1_cast_fp16")];
            string x_1_pad_type_0 = const()[name = string("x_1_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> x_1_strides_0 = const()[name = string("x_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> x_1_pad_0 = const()[name = string("x_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> x_1_dilations_0 = const()[name = string("x_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 x_1_groups_0 = const()[name = string("x_1_groups_0"), val = int32(1)];
            tensor<fp16, [128, 64, 1, 1]> weight_7_to_fp16 = const()[name = string("weight_7_to_fp16"), val = tensor<fp16, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1472)))];
            tensor<fp16, [1, 128, 80, 231]> x_1_cast_fp16 = conv(dilations = x_1_dilations_0, groups = x_1_groups_0, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = x_1_strides_0, weight = weight_7_to_fp16, x = input_1_cast_fp16)[name = string("x_1_cast_fp16")];
            tensor<int32, [4]> var_81_begin_0 = const()[name = string("op_81_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
            tensor<int32, [4]> var_81_end_0 = const()[name = string("op_81_end_0"), val = tensor<int32, [4]>([1, 128, 80, 231])];
            tensor<bool, [4]> var_81_end_mask_0 = const()[name = string("op_81_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<bool, [4]> var_81_squeeze_mask_0 = const()[name = string("op_81_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 128, 80]> var_81_cast_fp16 = slice_by_index(begin = var_81_begin_0, end = var_81_end_0, end_mask = var_81_end_mask_0, squeeze_mask = var_81_squeeze_mask_0, x = x_1_cast_fp16)[name = string("op_81_cast_fp16")];
            tensor<int32, [1]> var_82_axes_0 = const()[name = string("op_82_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1, 128, 80, 1]> var_82_cast_fp16 = expand_dims(axes = var_82_axes_0, x = var_81_cast_fp16)[name = string("op_82_cast_fp16")];
            bool x_3_interleave_0 = const()[name = string("x_3_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 128, 80, 232]> x_3_cast_fp16 = concat(axis = var_5, interleave = x_3_interleave_0, values = (x_1_cast_fp16, var_82_cast_fp16))[name = string("x_3_cast_fp16")];
            tensor<int32, [2]> var_85 = const()[name = string("op_85"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_86 = const()[name = string("op_86"), val = tensor<int32, [2]>([2, 2])];
            string var_88_pad_type_0 = const()[name = string("op_88_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_88_pad_0 = const()[name = string("op_88_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            bool var_88_exclude_padding_from_average_0 = const()[name = string("op_88_exclude_padding_from_average_0"), val = bool(false)];
            bool var_88_ceil_mode_0 = const()[name = string("op_88_ceil_mode_0"), val = bool(false)];
            tensor<fp16, [1, 128, 40, 116]> var_88_cast_fp16 = avg_pool(ceil_mode = var_88_ceil_mode_0, exclude_padding_from_average = var_88_exclude_padding_from_average_0, kernel_sizes = var_85, pad = var_88_pad_0, pad_type = var_88_pad_type_0, strides = var_86, x = x_3_cast_fp16)[name = string("op_88_cast_fp16")];
            tensor<fp16, [1, 64, 80, 231]> input_3_cast_fp16 = leaky_relu(alpha = var_10, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")];
            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)];
            tensor<fp16, [64, 64, 3, 3]> weight_11_to_fp16 = const()[name = string("weight_11_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17920)))];
            tensor<fp16, [64]> style_encoder_shared_1_conv1_bias_to_fp16 = const()[name = string("style_encoder_shared_1_conv1_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91712)))];
            tensor<fp16, [1, 64, 80, 231]> input_5_cast_fp16 = conv(bias = style_encoder_shared_1_conv1_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 = weight_11_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")];
            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(64)];
            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [64, 1, 3, 3]> weight_15_to_fp16 = const()[name = string("weight_15_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91904)))];
            tensor<fp16, [64]> style_encoder_shared_1_downsample_res_conv_bias_to_fp16 = const()[name = string("style_encoder_shared_1_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93120)))];
            tensor<fp16, [1, 64, 40, 116]> input_7_cast_fp16 = conv(bias = style_encoder_shared_1_downsample_res_conv_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = weight_15_to_fp16, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")];
            tensor<fp16, [1, 64, 40, 116]> input_9_cast_fp16 = leaky_relu(alpha = var_10, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")];
            string var_133_pad_type_0 = const()[name = string("op_133_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_133_pad_0 = const()[name = string("op_133_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> var_133_strides_0 = const()[name = string("op_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_133_dilations_0 = const()[name = string("op_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_133_groups_0 = const()[name = string("op_133_groups_0"), val = int32(1)];
            tensor<fp16, [128, 64, 3, 3]> weight_19_to_fp16 = const()[name = string("weight_19_to_fp16"), val = tensor<fp16, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93312)))];
            tensor<fp16, [128]> style_encoder_shared_1_conv2_bias_to_fp16 = const()[name = string("style_encoder_shared_1_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240832)))];
            tensor<fp16, [1, 128, 40, 116]> var_133_cast_fp16 = conv(bias = style_encoder_shared_1_conv2_bias_to_fp16, dilations = var_133_dilations_0, groups = var_133_groups_0, pad = var_133_pad_0, pad_type = var_133_pad_type_0, strides = var_133_strides_0, weight = weight_19_to_fp16, x = input_9_cast_fp16)[name = string("op_133_cast_fp16")];
            tensor<fp16, [1, 128, 40, 116]> x_5_cast_fp16 = add(x = var_88_cast_fp16, y = var_133_cast_fp16)[name = string("x_5_cast_fp16")];
            fp16 _inversed_input_11_y_0_to_fp16 = const()[name = string("_inversed_input_11_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
            tensor<fp16, [1, 128, 40, 116]> _inversed_input_11_cast_fp16 = mul(x = x_5_cast_fp16, y = _inversed_input_11_y_0_to_fp16)[name = string("_inversed_input_11_cast_fp16")];
            string x_7_pad_type_0 = const()[name = string("x_7_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> x_7_strides_0 = const()[name = string("x_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> x_7_pad_0 = const()[name = string("x_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> x_7_dilations_0 = const()[name = string("x_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 x_7_groups_0 = const()[name = string("x_7_groups_0"), val = int32(1)];
            tensor<fp16, [256, 128, 1, 1]> weight_23_to_fp16 = const()[name = string("weight_23_to_fp16"), val = tensor<fp16, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(241152)))];
            tensor<fp16, [1, 256, 40, 116]> x_7_cast_fp16 = conv(dilations = x_7_dilations_0, groups = x_7_groups_0, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = x_7_strides_0, weight = weight_23_to_fp16, x = _inversed_input_11_cast_fp16)[name = string("x_7_cast_fp16")];
            tensor<int32, [2]> var_169 = const()[name = string("op_169"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_170 = const()[name = string("op_170"), val = tensor<int32, [2]>([2, 2])];
            string var_172_pad_type_0 = const()[name = string("op_172_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_172_pad_0 = const()[name = string("op_172_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            bool var_172_exclude_padding_from_average_0 = const()[name = string("op_172_exclude_padding_from_average_0"), val = bool(false)];
            bool var_172_ceil_mode_0 = const()[name = string("op_172_ceil_mode_0"), val = bool(false)];
            tensor<fp16, [1, 256, 20, 58]> var_172_cast_fp16 = avg_pool(ceil_mode = var_172_ceil_mode_0, exclude_padding_from_average = var_172_exclude_padding_from_average_0, kernel_sizes = var_169, pad = var_172_pad_0, pad_type = var_172_pad_type_0, strides = var_170, x = x_7_cast_fp16)[name = string("op_172_cast_fp16")];
            tensor<fp16, [1, 128, 40, 116]> input_13_cast_fp16 = leaky_relu(alpha = var_10, x = _inversed_input_11_cast_fp16)[name = string("input_13_cast_fp16")];
            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)];
            tensor<fp16, [128, 128, 3, 3]> weight_27_to_fp16 = const()[name = string("weight_27_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306752)))];
            tensor<fp16, [128]> style_encoder_shared_2_conv1_bias_to_fp16 = const()[name = string("style_encoder_shared_2_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601728)))];
            tensor<fp16, [1, 128, 40, 116]> input_15_cast_fp16 = conv(bias = style_encoder_shared_2_conv1_bias_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = weight_27_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")];
            string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor<int32, [2]>([2, 2])];
            int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(128)];
            tensor<int32, [2]> input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [128, 1, 3, 3]> weight_31_to_fp16 = const()[name = string("weight_31_to_fp16"), val = tensor<fp16, [128, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(602048)))];
            tensor<fp16, [128]> style_encoder_shared_2_downsample_res_conv_bias_to_fp16 = const()[name = string("style_encoder_shared_2_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(604416)))];
            tensor<fp16, [1, 128, 20, 58]> input_17_cast_fp16 = conv(bias = style_encoder_shared_2_downsample_res_conv_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 = weight_31_to_fp16, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")];
            tensor<fp16, [1, 128, 20, 58]> input_19_cast_fp16 = leaky_relu(alpha = var_10, x = input_17_cast_fp16)[name = string("input_19_cast_fp16")];
            string var_217_pad_type_0 = const()[name = string("op_217_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_217_pad_0 = const()[name = string("op_217_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> var_217_strides_0 = const()[name = string("op_217_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_217_dilations_0 = const()[name = string("op_217_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_217_groups_0 = const()[name = string("op_217_groups_0"), val = int32(1)];
            tensor<fp16, [256, 128, 3, 3]> weight_35_to_fp16 = const()[name = string("weight_35_to_fp16"), val = tensor<fp16, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(604736)))];
            tensor<fp16, [256]> style_encoder_shared_2_conv2_bias_to_fp16 = const()[name = string("style_encoder_shared_2_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1194624)))];
            tensor<fp16, [1, 256, 20, 58]> var_217_cast_fp16 = conv(bias = style_encoder_shared_2_conv2_bias_to_fp16, dilations = var_217_dilations_0, groups = var_217_groups_0, pad = var_217_pad_0, pad_type = var_217_pad_type_0, strides = var_217_strides_0, weight = weight_35_to_fp16, x = input_19_cast_fp16)[name = string("op_217_cast_fp16")];
            tensor<fp16, [1, 256, 20, 58]> x_9_cast_fp16 = add(x = var_172_cast_fp16, y = var_217_cast_fp16)[name = string("x_9_cast_fp16")];
            fp16 _inversed_input_21_y_0_to_fp16 = const()[name = string("_inversed_input_21_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
            tensor<fp16, [1, 256, 20, 58]> _inversed_input_21_cast_fp16 = mul(x = x_9_cast_fp16, y = _inversed_input_21_y_0_to_fp16)[name = string("_inversed_input_21_cast_fp16")];
            string x_11_pad_type_0 = const()[name = string("x_11_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> x_11_strides_0 = const()[name = string("x_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> x_11_pad_0 = const()[name = string("x_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> x_11_dilations_0 = const()[name = string("x_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 x_11_groups_0 = const()[name = string("x_11_groups_0"), val = int32(1)];
            tensor<fp16, [512, 256, 1, 1]> weight_39_to_fp16 = const()[name = string("weight_39_to_fp16"), val = tensor<fp16, [512, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1195200)))];
            tensor<fp16, [1, 512, 20, 58]> x_11_cast_fp16 = conv(dilations = x_11_dilations_0, groups = x_11_groups_0, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = x_11_strides_0, weight = weight_39_to_fp16, x = _inversed_input_21_cast_fp16)[name = string("x_11_cast_fp16")];
            tensor<int32, [2]> var_253 = const()[name = string("op_253"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_254 = const()[name = string("op_254"), val = tensor<int32, [2]>([2, 2])];
            string var_256_pad_type_0 = const()[name = string("op_256_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_256_pad_0 = const()[name = string("op_256_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            bool var_256_exclude_padding_from_average_0 = const()[name = string("op_256_exclude_padding_from_average_0"), val = bool(false)];
            bool var_256_ceil_mode_0 = const()[name = string("op_256_ceil_mode_0"), val = bool(false)];
            tensor<fp16, [1, 512, 10, 29]> var_256_cast_fp16 = avg_pool(ceil_mode = var_256_ceil_mode_0, exclude_padding_from_average = var_256_exclude_padding_from_average_0, kernel_sizes = var_253, pad = var_256_pad_0, pad_type = var_256_pad_type_0, strides = var_254, x = x_11_cast_fp16)[name = string("op_256_cast_fp16")];
            tensor<fp16, [1, 256, 20, 58]> input_23_cast_fp16 = leaky_relu(alpha = var_10, x = _inversed_input_21_cast_fp16)[name = string("input_23_cast_fp16")];
            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 3, 3]> weight_43_to_fp16 = const()[name = string("weight_43_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1457408)))];
            tensor<fp16, [256]> style_encoder_shared_3_conv1_bias_to_fp16 = const()[name = string("style_encoder_shared_3_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2637120)))];
            tensor<fp16, [1, 256, 20, 58]> input_25_cast_fp16 = conv(bias = style_encoder_shared_3_conv1_bias_to_fp16, dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = weight_43_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")];
            string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor<int32, [2]>([2, 2])];
            int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> weight_47_to_fp16 = const()[name = string("weight_47_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2637696)))];
            tensor<fp16, [256]> style_encoder_shared_3_downsample_res_conv_bias_to_fp16 = const()[name = string("style_encoder_shared_3_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2642368)))];
            tensor<fp16, [1, 256, 10, 29]> input_27_cast_fp16 = conv(bias = style_encoder_shared_3_downsample_res_conv_bias_to_fp16, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = weight_47_to_fp16, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")];
            tensor<fp16, [1, 256, 10, 29]> input_29_cast_fp16 = leaky_relu(alpha = var_10, x = input_27_cast_fp16)[name = string("input_29_cast_fp16")];
            string var_301_pad_type_0 = const()[name = string("op_301_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_301_pad_0 = const()[name = string("op_301_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> var_301_strides_0 = const()[name = string("op_301_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_301_dilations_0 = const()[name = string("op_301_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_301_groups_0 = const()[name = string("op_301_groups_0"), val = int32(1)];
            tensor<fp16, [512, 256, 3, 3]> weight_51_to_fp16 = const()[name = string("weight_51_to_fp16"), val = tensor<fp16, [512, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2642944)))];
            tensor<fp16, [512]> style_encoder_shared_3_conv2_bias_to_fp16 = const()[name = string("style_encoder_shared_3_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5002304)))];
            tensor<fp16, [1, 512, 10, 29]> var_301_cast_fp16 = conv(bias = style_encoder_shared_3_conv2_bias_to_fp16, dilations = var_301_dilations_0, groups = var_301_groups_0, pad = var_301_pad_0, pad_type = var_301_pad_type_0, strides = var_301_strides_0, weight = weight_51_to_fp16, x = input_29_cast_fp16)[name = string("op_301_cast_fp16")];
            tensor<fp16, [1, 512, 10, 29]> x_13_cast_fp16 = add(x = var_256_cast_fp16, y = var_301_cast_fp16)[name = string("x_13_cast_fp16")];
            fp16 _inversed_x_15_y_0_to_fp16 = const()[name = string("_inversed_x_15_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
            tensor<fp16, [1, 512, 10, 29]> _inversed_x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = _inversed_x_15_y_0_to_fp16)[name = string("_inversed_x_15_cast_fp16")];
            tensor<int32, [4]> var_320_begin_0 = const()[name = string("op_320_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
            tensor<int32, [4]> var_320_end_0 = const()[name = string("op_320_end_0"), val = tensor<int32, [4]>([1, 512, 10, 29])];
            tensor<bool, [4]> var_320_end_mask_0 = const()[name = string("op_320_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<bool, [4]> var_320_squeeze_mask_0 = const()[name = string("op_320_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 512, 10]> var_320_cast_fp16 = slice_by_index(begin = var_320_begin_0, end = var_320_end_0, end_mask = var_320_end_mask_0, squeeze_mask = var_320_squeeze_mask_0, x = _inversed_x_15_cast_fp16)[name = string("op_320_cast_fp16")];
            tensor<int32, [1]> var_321_axes_0 = const()[name = string("op_321_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1, 512, 10, 1]> var_321_cast_fp16 = expand_dims(axes = var_321_axes_0, x = var_320_cast_fp16)[name = string("op_321_cast_fp16")];
            bool x_17_interleave_0 = const()[name = string("x_17_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 512, 10, 30]> x_17_cast_fp16 = concat(axis = var_5, interleave = x_17_interleave_0, values = (_inversed_x_15_cast_fp16, var_321_cast_fp16))[name = string("x_17_cast_fp16")];
            tensor<int32, [2]> var_324 = const()[name = string("op_324"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_325 = const()[name = string("op_325"), val = tensor<int32, [2]>([2, 2])];
            string var_327_pad_type_0 = const()[name = string("op_327_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_327_pad_0 = const()[name = string("op_327_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            bool var_327_exclude_padding_from_average_0 = const()[name = string("op_327_exclude_padding_from_average_0"), val = bool(false)];
            bool var_327_ceil_mode_0 = const()[name = string("op_327_ceil_mode_0"), val = bool(false)];
            tensor<fp16, [1, 512, 5, 15]> var_327_cast_fp16 = avg_pool(ceil_mode = var_327_ceil_mode_0, exclude_padding_from_average = var_327_exclude_padding_from_average_0, kernel_sizes = var_324, pad = var_327_pad_0, pad_type = var_327_pad_type_0, strides = var_325, x = x_17_cast_fp16)[name = string("op_327_cast_fp16")];
            tensor<fp16, [1, 512, 10, 29]> input_31_cast_fp16 = leaky_relu(alpha = var_10, x = _inversed_x_15_cast_fp16)[name = string("input_31_cast_fp16")];
            string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)];
            tensor<fp16, [512, 512, 3, 3]> weight_55_to_fp16 = const()[name = string("weight_55_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5003392)))];
            tensor<fp16, [512]> style_encoder_shared_4_conv1_bias_to_fp16 = const()[name = string("style_encoder_shared_4_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9722048)))];
            tensor<fp16, [1, 512, 10, 29]> input_33_cast_fp16 = conv(bias = style_encoder_shared_4_conv1_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 = weight_55_to_fp16, x = input_31_cast_fp16)[name = string("input_33_cast_fp16")];
            string input_35_pad_type_0 = const()[name = string("input_35_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_35_strides_0 = const()[name = string("input_35_strides_0"), val = tensor<int32, [2]>([2, 2])];
            int32 input_35_groups_0 = const()[name = string("input_35_groups_0"), val = int32(512)];
            tensor<int32, [2]> input_35_dilations_0 = const()[name = string("input_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [512, 1, 3, 3]> weight_59_to_fp16 = const()[name = string("weight_59_to_fp16"), val = tensor<fp16, [512, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9723136)))];
            tensor<fp16, [512]> style_encoder_shared_4_downsample_res_conv_bias_to_fp16 = const()[name = string("style_encoder_shared_4_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9732416)))];
            tensor<fp16, [1, 512, 5, 15]> input_35_cast_fp16 = conv(bias = style_encoder_shared_4_downsample_res_conv_bias_to_fp16, dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = weight_59_to_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")];
            tensor<fp16, [1, 512, 5, 15]> input_37_cast_fp16 = leaky_relu(alpha = var_10, x = input_35_cast_fp16)[name = string("input_37_cast_fp16")];
            string var_372_pad_type_0 = const()[name = string("op_372_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_372_pad_0 = const()[name = string("op_372_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> var_372_strides_0 = const()[name = string("op_372_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_372_dilations_0 = const()[name = string("op_372_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_372_groups_0 = const()[name = string("op_372_groups_0"), val = int32(1)];
            tensor<fp16, [512, 512, 3, 3]> weight_63_to_fp16 = const()[name = string("weight_63_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9733504)))];
            tensor<fp16, [512]> style_encoder_shared_4_conv2_bias_to_fp16 = const()[name = string("style_encoder_shared_4_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14452160)))];
            tensor<fp16, [1, 512, 5, 15]> var_372_cast_fp16 = conv(bias = style_encoder_shared_4_conv2_bias_to_fp16, dilations = var_372_dilations_0, groups = var_372_groups_0, pad = var_372_pad_0, pad_type = var_372_pad_type_0, strides = var_372_strides_0, weight = weight_63_to_fp16, x = input_37_cast_fp16)[name = string("op_372_cast_fp16")];
            tensor<fp16, [1, 512, 5, 15]> x_19_cast_fp16 = add(x = var_327_cast_fp16, y = var_372_cast_fp16)[name = string("x_19_cast_fp16")];
            fp16 _inversed_input_39_y_0_to_fp16 = const()[name = string("_inversed_input_39_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
            tensor<fp16, [1, 512, 5, 15]> _inversed_input_39_cast_fp16 = mul(x = x_19_cast_fp16, y = _inversed_input_39_y_0_to_fp16)[name = string("_inversed_input_39_cast_fp16")];
            tensor<fp16, [1, 512, 5, 15]> input_41_cast_fp16 = leaky_relu(alpha = var_10, x = _inversed_input_39_cast_fp16)[name = string("input_41_cast_fp16")];
            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
            tensor<fp16, [512, 512, 5, 5]> weight_67_to_fp16 = const()[name = string("weight_67_to_fp16"), val = tensor<fp16, [512, 512, 5, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14453248)))];
            tensor<fp16, [512]> style_encoder_shared_6_bias_to_fp16 = const()[name = string("style_encoder_shared_6_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27560512)))];
            tensor<fp16, [1, 512, 1, 11]> input_43_cast_fp16 = conv(bias = style_encoder_shared_6_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 = weight_67_to_fp16, x = input_41_cast_fp16)[name = string("input_43_cast_fp16")];
            tensor<int32, [2]> input_45_axes_0 = const()[name = string("input_45_axes_0"), val = tensor<int32, [2]>([-2, -1])];
            bool input_45_keep_dims_0 = const()[name = string("input_45_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 512, 1, 1]> input_45_cast_fp16 = reduce_mean(axes = input_45_axes_0, keep_dims = input_45_keep_dims_0, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")];
            tensor<fp16, [1, 512, 1, 1]> h_1_cast_fp16 = leaky_relu(alpha = var_10, x = input_45_cast_fp16)[name = string("h_1_cast_fp16")];
            tensor<int32, [2]> var_393 = const()[name = string("op_393"), val = tensor<int32, [2]>([1, -1])];
            tensor<fp16, [1, 512]> input_47_cast_fp16 = reshape(shape = var_393, x = h_1_cast_fp16)[name = string("input_47_cast_fp16")];
            tensor<fp16, [128, 512]> style_encoder_unshared_weight_to_fp16 = const()[name = string("style_encoder_unshared_weight_to_fp16"), val = tensor<fp16, [128, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27561600)))];
            tensor<fp16, [128]> style_encoder_unshared_bias_to_fp16 = const()[name = string("style_encoder_unshared_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27692736)))];
            tensor<fp16, [1, 128]> linear_0_cast_fp16 = linear(bias = style_encoder_unshared_bias_to_fp16, weight = style_encoder_unshared_weight_to_fp16, x = input_47_cast_fp16)[name = string("linear_0_cast_fp16")];
            int32 var_399 = const()[name = string("op_399"), val = int32(-1)];
            fp32 var_404 = const()[name = string("op_404"), val = fp32(0x1.99999ap-3)];
            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
            tensor<fp16, [64, 1, 3, 3]> weight_71_to_fp16 = const()[name = string("weight_71_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27693056)))];
            tensor<fp16, [64]> predictor_encoder_shared_0_bias_to_fp16 = const()[name = string("predictor_encoder_shared_0_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27694272)))];
            tensor<fp16, [1, 64, 80, 231]> input_49_cast_fp16 = conv(bias = predictor_encoder_shared_0_bias_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = weight_71_to_fp16, x = mel_to_fp16)[name = string("input_49_cast_fp16")];
            string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)];
            tensor<fp16, [128, 64, 1, 1]> weight_75_to_fp16 = const()[name = string("weight_75_to_fp16"), val = tensor<fp16, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27694464)))];
            tensor<fp16, [1, 128, 80, 231]> x_21_cast_fp16 = conv(dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = weight_75_to_fp16, x = input_49_cast_fp16)[name = string("x_21_cast_fp16")];
            tensor<int32, [4]> var_475_begin_0 = const()[name = string("op_475_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
            tensor<int32, [4]> var_475_end_0 = const()[name = string("op_475_end_0"), val = tensor<int32, [4]>([1, 128, 80, 231])];
            tensor<bool, [4]> var_475_end_mask_0 = const()[name = string("op_475_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<bool, [4]> var_475_squeeze_mask_0 = const()[name = string("op_475_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 128, 80]> var_475_cast_fp16 = slice_by_index(begin = var_475_begin_0, end = var_475_end_0, end_mask = var_475_end_mask_0, squeeze_mask = var_475_squeeze_mask_0, x = x_21_cast_fp16)[name = string("op_475_cast_fp16")];
            tensor<int32, [1]> var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1, 128, 80, 1]> var_476_cast_fp16 = expand_dims(axes = var_476_axes_0, x = var_475_cast_fp16)[name = string("op_476_cast_fp16")];
            bool x_23_interleave_0 = const()[name = string("x_23_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 128, 80, 232]> x_23_cast_fp16 = concat(axis = var_399, interleave = x_23_interleave_0, values = (x_21_cast_fp16, var_476_cast_fp16))[name = string("x_23_cast_fp16")];
            tensor<int32, [2]> var_479 = const()[name = string("op_479"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_480 = const()[name = string("op_480"), val = tensor<int32, [2]>([2, 2])];
            string var_482_pad_type_0 = const()[name = string("op_482_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_482_pad_0 = const()[name = string("op_482_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            bool var_482_exclude_padding_from_average_0 = const()[name = string("op_482_exclude_padding_from_average_0"), val = bool(false)];
            bool var_482_ceil_mode_0 = const()[name = string("op_482_ceil_mode_0"), val = bool(false)];
            tensor<fp16, [1, 128, 40, 116]> var_482_cast_fp16 = avg_pool(ceil_mode = var_482_ceil_mode_0, exclude_padding_from_average = var_482_exclude_padding_from_average_0, kernel_sizes = var_479, pad = var_482_pad_0, pad_type = var_482_pad_type_0, strides = var_480, x = x_23_cast_fp16)[name = string("op_482_cast_fp16")];
            tensor<fp16, [1, 64, 80, 231]> input_51_cast_fp16 = leaky_relu(alpha = var_404, x = input_49_cast_fp16)[name = string("input_51_cast_fp16")];
            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
            tensor<fp16, [64, 64, 3, 3]> weight_79_to_fp16 = const()[name = string("weight_79_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27710912)))];
            tensor<fp16, [64]> predictor_encoder_shared_1_conv1_bias_to_fp16 = const()[name = string("predictor_encoder_shared_1_conv1_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27784704)))];
            tensor<fp16, [1, 64, 80, 231]> input_53_cast_fp16 = conv(bias = predictor_encoder_shared_1_conv1_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 = weight_79_to_fp16, x = input_51_cast_fp16)[name = string("input_53_cast_fp16")];
            string input_55_pad_type_0 = const()[name = string("input_55_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_55_pad_0 = const()[name = string("input_55_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_55_strides_0 = const()[name = string("input_55_strides_0"), val = tensor<int32, [2]>([2, 2])];
            int32 input_55_groups_0 = const()[name = string("input_55_groups_0"), val = int32(64)];
            tensor<int32, [2]> input_55_dilations_0 = const()[name = string("input_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [64, 1, 3, 3]> weight_83_to_fp16 = const()[name = string("weight_83_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27784896)))];
            tensor<fp16, [64]> predictor_encoder_shared_1_downsample_res_conv_bias_to_fp16 = const()[name = string("predictor_encoder_shared_1_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27786112)))];
            tensor<fp16, [1, 64, 40, 116]> input_55_cast_fp16 = conv(bias = predictor_encoder_shared_1_downsample_res_conv_bias_to_fp16, dilations = input_55_dilations_0, groups = input_55_groups_0, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = input_55_strides_0, weight = weight_83_to_fp16, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")];
            tensor<fp16, [1, 64, 40, 116]> input_57_cast_fp16 = leaky_relu(alpha = var_404, x = input_55_cast_fp16)[name = string("input_57_cast_fp16")];
            string var_527_pad_type_0 = const()[name = string("op_527_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_527_pad_0 = const()[name = string("op_527_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> var_527_strides_0 = const()[name = string("op_527_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_527_dilations_0 = const()[name = string("op_527_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_527_groups_0 = const()[name = string("op_527_groups_0"), val = int32(1)];
            tensor<fp16, [128, 64, 3, 3]> weight_87_to_fp16 = const()[name = string("weight_87_to_fp16"), val = tensor<fp16, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27786304)))];
            tensor<fp16, [128]> predictor_encoder_shared_1_conv2_bias_to_fp16 = const()[name = string("predictor_encoder_shared_1_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27933824)))];
            tensor<fp16, [1, 128, 40, 116]> var_527_cast_fp16 = conv(bias = predictor_encoder_shared_1_conv2_bias_to_fp16, dilations = var_527_dilations_0, groups = var_527_groups_0, pad = var_527_pad_0, pad_type = var_527_pad_type_0, strides = var_527_strides_0, weight = weight_87_to_fp16, x = input_57_cast_fp16)[name = string("op_527_cast_fp16")];
            tensor<fp16, [1, 128, 40, 116]> x_25_cast_fp16 = add(x = var_482_cast_fp16, y = var_527_cast_fp16)[name = string("x_25_cast_fp16")];
            fp16 _inversed_input_59_y_0_to_fp16 = const()[name = string("_inversed_input_59_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
            tensor<fp16, [1, 128, 40, 116]> _inversed_input_59_cast_fp16 = mul(x = x_25_cast_fp16, y = _inversed_input_59_y_0_to_fp16)[name = string("_inversed_input_59_cast_fp16")];
            string x_27_pad_type_0 = const()[name = string("x_27_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> x_27_strides_0 = const()[name = string("x_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> x_27_pad_0 = const()[name = string("x_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> x_27_dilations_0 = const()[name = string("x_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 x_27_groups_0 = const()[name = string("x_27_groups_0"), val = int32(1)];
            tensor<fp16, [256, 128, 1, 1]> weight_91_to_fp16 = const()[name = string("weight_91_to_fp16"), val = tensor<fp16, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27934144)))];
            tensor<fp16, [1, 256, 40, 116]> x_27_cast_fp16 = conv(dilations = x_27_dilations_0, groups = x_27_groups_0, pad = x_27_pad_0, pad_type = x_27_pad_type_0, strides = x_27_strides_0, weight = weight_91_to_fp16, x = _inversed_input_59_cast_fp16)[name = string("x_27_cast_fp16")];
            tensor<int32, [2]> var_563 = const()[name = string("op_563"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_564 = const()[name = string("op_564"), val = tensor<int32, [2]>([2, 2])];
            string var_566_pad_type_0 = const()[name = string("op_566_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_566_pad_0 = const()[name = string("op_566_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            bool var_566_exclude_padding_from_average_0 = const()[name = string("op_566_exclude_padding_from_average_0"), val = bool(false)];
            bool var_566_ceil_mode_0 = const()[name = string("op_566_ceil_mode_0"), val = bool(false)];
            tensor<fp16, [1, 256, 20, 58]> var_566_cast_fp16 = avg_pool(ceil_mode = var_566_ceil_mode_0, exclude_padding_from_average = var_566_exclude_padding_from_average_0, kernel_sizes = var_563, pad = var_566_pad_0, pad_type = var_566_pad_type_0, strides = var_564, x = x_27_cast_fp16)[name = string("op_566_cast_fp16")];
            tensor<fp16, [1, 128, 40, 116]> input_61_cast_fp16 = leaky_relu(alpha = var_404, x = _inversed_input_59_cast_fp16)[name = string("input_61_cast_fp16")];
            string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)];
            tensor<fp16, [128, 128, 3, 3]> weight_95_to_fp16 = const()[name = string("weight_95_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27999744)))];
            tensor<fp16, [128]> predictor_encoder_shared_2_conv1_bias_to_fp16 = const()[name = string("predictor_encoder_shared_2_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28294720)))];
            tensor<fp16, [1, 128, 40, 116]> input_63_cast_fp16 = conv(bias = predictor_encoder_shared_2_conv1_bias_to_fp16, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = weight_95_to_fp16, x = input_61_cast_fp16)[name = string("input_63_cast_fp16")];
            string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor<int32, [2]>([2, 2])];
            int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(128)];
            tensor<int32, [2]> input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [128, 1, 3, 3]> weight_99_to_fp16 = const()[name = string("weight_99_to_fp16"), val = tensor<fp16, [128, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28295040)))];
            tensor<fp16, [128]> predictor_encoder_shared_2_downsample_res_conv_bias_to_fp16 = const()[name = string("predictor_encoder_shared_2_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28297408)))];
            tensor<fp16, [1, 128, 20, 58]> input_65_cast_fp16 = conv(bias = predictor_encoder_shared_2_downsample_res_conv_bias_to_fp16, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = weight_99_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")];
            tensor<fp16, [1, 128, 20, 58]> input_67_cast_fp16 = leaky_relu(alpha = var_404, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")];
            string var_611_pad_type_0 = const()[name = string("op_611_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_611_pad_0 = const()[name = string("op_611_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> var_611_strides_0 = const()[name = string("op_611_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_611_dilations_0 = const()[name = string("op_611_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_611_groups_0 = const()[name = string("op_611_groups_0"), val = int32(1)];
            tensor<fp16, [256, 128, 3, 3]> weight_103_to_fp16 = const()[name = string("weight_103_to_fp16"), val = tensor<fp16, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28297728)))];
            tensor<fp16, [256]> predictor_encoder_shared_2_conv2_bias_to_fp16 = const()[name = string("predictor_encoder_shared_2_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28887616)))];
            tensor<fp16, [1, 256, 20, 58]> var_611_cast_fp16 = conv(bias = predictor_encoder_shared_2_conv2_bias_to_fp16, dilations = var_611_dilations_0, groups = var_611_groups_0, pad = var_611_pad_0, pad_type = var_611_pad_type_0, strides = var_611_strides_0, weight = weight_103_to_fp16, x = input_67_cast_fp16)[name = string("op_611_cast_fp16")];
            tensor<fp16, [1, 256, 20, 58]> x_29_cast_fp16 = add(x = var_566_cast_fp16, y = var_611_cast_fp16)[name = string("x_29_cast_fp16")];
            fp16 _inversed_input_69_y_0_to_fp16 = const()[name = string("_inversed_input_69_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
            tensor<fp16, [1, 256, 20, 58]> _inversed_input_69_cast_fp16 = mul(x = x_29_cast_fp16, y = _inversed_input_69_y_0_to_fp16)[name = string("_inversed_input_69_cast_fp16")];
            string x_31_pad_type_0 = const()[name = string("x_31_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> x_31_strides_0 = const()[name = string("x_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> x_31_pad_0 = const()[name = string("x_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> x_31_dilations_0 = const()[name = string("x_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 x_31_groups_0 = const()[name = string("x_31_groups_0"), val = int32(1)];
            tensor<fp16, [512, 256, 1, 1]> weight_107_to_fp16 = const()[name = string("weight_107_to_fp16"), val = tensor<fp16, [512, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28888192)))];
            tensor<fp16, [1, 512, 20, 58]> x_31_cast_fp16 = conv(dilations = x_31_dilations_0, groups = x_31_groups_0, pad = x_31_pad_0, pad_type = x_31_pad_type_0, strides = x_31_strides_0, weight = weight_107_to_fp16, x = _inversed_input_69_cast_fp16)[name = string("x_31_cast_fp16")];
            tensor<int32, [2]> var_647 = const()[name = string("op_647"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_648 = const()[name = string("op_648"), val = tensor<int32, [2]>([2, 2])];
            string var_650_pad_type_0 = const()[name = string("op_650_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_650_pad_0 = const()[name = string("op_650_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            bool var_650_exclude_padding_from_average_0 = const()[name = string("op_650_exclude_padding_from_average_0"), val = bool(false)];
            bool var_650_ceil_mode_0 = const()[name = string("op_650_ceil_mode_0"), val = bool(false)];
            tensor<fp16, [1, 512, 10, 29]> var_650_cast_fp16 = avg_pool(ceil_mode = var_650_ceil_mode_0, exclude_padding_from_average = var_650_exclude_padding_from_average_0, kernel_sizes = var_647, pad = var_650_pad_0, pad_type = var_650_pad_type_0, strides = var_648, x = x_31_cast_fp16)[name = string("op_650_cast_fp16")];
            tensor<fp16, [1, 256, 20, 58]> input_71_cast_fp16 = leaky_relu(alpha = var_404, x = _inversed_input_69_cast_fp16)[name = string("input_71_cast_fp16")];
            string input_73_pad_type_0 = const()[name = string("input_73_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_73_pad_0 = const()[name = string("input_73_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_73_strides_0 = const()[name = string("input_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_73_dilations_0 = const()[name = string("input_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_73_groups_0 = const()[name = string("input_73_groups_0"), val = int32(1)];
            tensor<fp16, [256, 256, 3, 3]> weight_111_to_fp16 = const()[name = string("weight_111_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29150400)))];
            tensor<fp16, [256]> predictor_encoder_shared_3_conv1_bias_to_fp16 = const()[name = string("predictor_encoder_shared_3_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30330112)))];
            tensor<fp16, [1, 256, 20, 58]> input_73_cast_fp16 = conv(bias = predictor_encoder_shared_3_conv1_bias_to_fp16, dilations = input_73_dilations_0, groups = input_73_groups_0, pad = input_73_pad_0, pad_type = input_73_pad_type_0, strides = input_73_strides_0, weight = weight_111_to_fp16, x = input_71_cast_fp16)[name = string("input_73_cast_fp16")];
            string input_75_pad_type_0 = const()[name = string("input_75_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_75_pad_0 = const()[name = string("input_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_75_strides_0 = const()[name = string("input_75_strides_0"), val = tensor<int32, [2]>([2, 2])];
            int32 input_75_groups_0 = const()[name = string("input_75_groups_0"), val = int32(256)];
            tensor<int32, [2]> input_75_dilations_0 = const()[name = string("input_75_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [256, 1, 3, 3]> weight_115_to_fp16 = const()[name = string("weight_115_to_fp16"), val = tensor<fp16, [256, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30330688)))];
            tensor<fp16, [256]> predictor_encoder_shared_3_downsample_res_conv_bias_to_fp16 = const()[name = string("predictor_encoder_shared_3_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30335360)))];
            tensor<fp16, [1, 256, 10, 29]> input_75_cast_fp16 = conv(bias = predictor_encoder_shared_3_downsample_res_conv_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 = weight_115_to_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")];
            tensor<fp16, [1, 256, 10, 29]> input_77_cast_fp16 = leaky_relu(alpha = var_404, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")];
            string var_695_pad_type_0 = const()[name = string("op_695_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_695_pad_0 = const()[name = string("op_695_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> var_695_strides_0 = const()[name = string("op_695_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_695_dilations_0 = const()[name = string("op_695_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_695_groups_0 = const()[name = string("op_695_groups_0"), val = int32(1)];
            tensor<fp16, [512, 256, 3, 3]> weight_119_to_fp16 = const()[name = string("weight_119_to_fp16"), val = tensor<fp16, [512, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30335936)))];
            tensor<fp16, [512]> predictor_encoder_shared_3_conv2_bias_to_fp16 = const()[name = string("predictor_encoder_shared_3_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32695296)))];
            tensor<fp16, [1, 512, 10, 29]> var_695_cast_fp16 = conv(bias = predictor_encoder_shared_3_conv2_bias_to_fp16, dilations = var_695_dilations_0, groups = var_695_groups_0, pad = var_695_pad_0, pad_type = var_695_pad_type_0, strides = var_695_strides_0, weight = weight_119_to_fp16, x = input_77_cast_fp16)[name = string("op_695_cast_fp16")];
            tensor<fp16, [1, 512, 10, 29]> x_33_cast_fp16 = add(x = var_650_cast_fp16, y = var_695_cast_fp16)[name = string("x_33_cast_fp16")];
            fp16 _inversed_x_35_y_0_to_fp16 = const()[name = string("_inversed_x_35_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
            tensor<fp16, [1, 512, 10, 29]> _inversed_x_35_cast_fp16 = mul(x = x_33_cast_fp16, y = _inversed_x_35_y_0_to_fp16)[name = string("_inversed_x_35_cast_fp16")];
            tensor<int32, [4]> var_714_begin_0 = const()[name = string("op_714_begin_0"), val = tensor<int32, [4]>([0, 0, 0, -1])];
            tensor<int32, [4]> var_714_end_0 = const()[name = string("op_714_end_0"), val = tensor<int32, [4]>([1, 512, 10, 29])];
            tensor<bool, [4]> var_714_end_mask_0 = const()[name = string("op_714_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<bool, [4]> var_714_squeeze_mask_0 = const()[name = string("op_714_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, true])];
            tensor<fp16, [1, 512, 10]> var_714_cast_fp16 = slice_by_index(begin = var_714_begin_0, end = var_714_end_0, end_mask = var_714_end_mask_0, squeeze_mask = var_714_squeeze_mask_0, x = _inversed_x_35_cast_fp16)[name = string("op_714_cast_fp16")];
            tensor<int32, [1]> var_715_axes_0 = const()[name = string("op_715_axes_0"), val = tensor<int32, [1]>([-1])];
            tensor<fp16, [1, 512, 10, 1]> var_715_cast_fp16 = expand_dims(axes = var_715_axes_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")];
            bool x_37_interleave_0 = const()[name = string("x_37_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 512, 10, 30]> x_37_cast_fp16 = concat(axis = var_399, interleave = x_37_interleave_0, values = (_inversed_x_35_cast_fp16, var_715_cast_fp16))[name = string("x_37_cast_fp16")];
            tensor<int32, [2]> var_718 = const()[name = string("op_718"), val = tensor<int32, [2]>([2, 2])];
            tensor<int32, [2]> var_719 = const()[name = string("op_719"), val = tensor<int32, [2]>([2, 2])];
            string var_721_pad_type_0 = const()[name = string("op_721_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_721_pad_0 = const()[name = string("op_721_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            bool var_721_exclude_padding_from_average_0 = const()[name = string("op_721_exclude_padding_from_average_0"), val = bool(false)];
            bool var_721_ceil_mode_0 = const()[name = string("op_721_ceil_mode_0"), val = bool(false)];
            tensor<fp16, [1, 512, 5, 15]> var_721_cast_fp16 = avg_pool(ceil_mode = var_721_ceil_mode_0, exclude_padding_from_average = var_721_exclude_padding_from_average_0, kernel_sizes = var_718, pad = var_721_pad_0, pad_type = var_721_pad_type_0, strides = var_719, x = x_37_cast_fp16)[name = string("op_721_cast_fp16")];
            tensor<fp16, [1, 512, 10, 29]> input_79_cast_fp16 = leaky_relu(alpha = var_404, x = _inversed_x_35_cast_fp16)[name = string("input_79_cast_fp16")];
            string input_81_pad_type_0 = const()[name = string("input_81_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_81_pad_0 = const()[name = string("input_81_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_81_strides_0 = const()[name = string("input_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> input_81_dilations_0 = const()[name = string("input_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_81_groups_0 = const()[name = string("input_81_groups_0"), val = int32(1)];
            tensor<fp16, [512, 512, 3, 3]> weight_123_to_fp16 = const()[name = string("weight_123_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32696384)))];
            tensor<fp16, [512]> predictor_encoder_shared_4_conv1_bias_to_fp16 = const()[name = string("predictor_encoder_shared_4_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37415040)))];
            tensor<fp16, [1, 512, 10, 29]> input_81_cast_fp16 = conv(bias = predictor_encoder_shared_4_conv1_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 = weight_123_to_fp16, x = input_79_cast_fp16)[name = string("input_81_cast_fp16")];
            string input_83_pad_type_0 = const()[name = string("input_83_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> input_83_pad_0 = const()[name = string("input_83_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> input_83_strides_0 = const()[name = string("input_83_strides_0"), val = tensor<int32, [2]>([2, 2])];
            int32 input_83_groups_0 = const()[name = string("input_83_groups_0"), val = int32(512)];
            tensor<int32, [2]> input_83_dilations_0 = const()[name = string("input_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<fp16, [512, 1, 3, 3]> weight_127_to_fp16 = const()[name = string("weight_127_to_fp16"), val = tensor<fp16, [512, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37416128)))];
            tensor<fp16, [512]> predictor_encoder_shared_4_downsample_res_conv_bias_to_fp16 = const()[name = string("predictor_encoder_shared_4_downsample_res_conv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37425408)))];
            tensor<fp16, [1, 512, 5, 15]> input_83_cast_fp16 = conv(bias = predictor_encoder_shared_4_downsample_res_conv_bias_to_fp16, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = weight_127_to_fp16, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")];
            tensor<fp16, [1, 512, 5, 15]> input_85_cast_fp16 = leaky_relu(alpha = var_404, x = input_83_cast_fp16)[name = string("input_85_cast_fp16")];
            string var_766_pad_type_0 = const()[name = string("op_766_pad_type_0"), val = string("custom")];
            tensor<int32, [4]> var_766_pad_0 = const()[name = string("op_766_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<int32, [2]> var_766_strides_0 = const()[name = string("op_766_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [2]> var_766_dilations_0 = const()[name = string("op_766_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_766_groups_0 = const()[name = string("op_766_groups_0"), val = int32(1)];
            tensor<fp16, [512, 512, 3, 3]> weight_131_to_fp16 = const()[name = string("weight_131_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37426496)))];
            tensor<fp16, [512]> predictor_encoder_shared_4_conv2_bias_to_fp16 = const()[name = string("predictor_encoder_shared_4_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42145152)))];
            tensor<fp16, [1, 512, 5, 15]> var_766_cast_fp16 = conv(bias = predictor_encoder_shared_4_conv2_bias_to_fp16, dilations = var_766_dilations_0, groups = var_766_groups_0, pad = var_766_pad_0, pad_type = var_766_pad_type_0, strides = var_766_strides_0, weight = weight_131_to_fp16, x = input_85_cast_fp16)[name = string("op_766_cast_fp16")];
            tensor<fp16, [1, 512, 5, 15]> x_cast_fp16 = add(x = var_721_cast_fp16, y = var_766_cast_fp16)[name = string("x_cast_fp16")];
            fp16 _inversed_input_87_y_0_to_fp16 = const()[name = string("_inversed_input_87_y_0_to_fp16"), val = fp16(0x1.6ap-1)];
            tensor<fp16, [1, 512, 5, 15]> _inversed_input_87_cast_fp16 = mul(x = x_cast_fp16, y = _inversed_input_87_y_0_to_fp16)[name = string("_inversed_input_87_cast_fp16")];
            tensor<fp16, [1, 512, 5, 15]> input_89_cast_fp16 = leaky_relu(alpha = var_404, x = _inversed_input_87_cast_fp16)[name = string("input_89_cast_fp16")];
            string input_91_pad_type_0 = const()[name = string("input_91_pad_type_0"), val = string("valid")];
            tensor<int32, [2]> input_91_strides_0 = const()[name = string("input_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            tensor<int32, [4]> input_91_pad_0 = const()[name = string("input_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_91_dilations_0 = const()[name = string("input_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_91_groups_0 = const()[name = string("input_91_groups_0"), val = int32(1)];
            tensor<fp16, [512, 512, 5, 5]> weight_135_to_fp16 = const()[name = string("weight_135_to_fp16"), val = tensor<fp16, [512, 512, 5, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42146240)))];
            tensor<fp16, [512]> predictor_encoder_shared_6_bias_to_fp16 = const()[name = string("predictor_encoder_shared_6_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55253504)))];
            tensor<fp16, [1, 512, 1, 11]> input_91_cast_fp16 = conv(bias = predictor_encoder_shared_6_bias_to_fp16, dilations = input_91_dilations_0, groups = input_91_groups_0, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = input_91_strides_0, weight = weight_135_to_fp16, x = input_89_cast_fp16)[name = string("input_91_cast_fp16")];
            tensor<int32, [2]> input_93_axes_0 = const()[name = string("input_93_axes_0"), val = tensor<int32, [2]>([-2, -1])];
            bool input_93_keep_dims_0 = const()[name = string("input_93_keep_dims_0"), val = bool(true)];
            tensor<fp16, [1, 512, 1, 1]> input_93_cast_fp16 = reduce_mean(axes = input_93_axes_0, keep_dims = input_93_keep_dims_0, x = input_91_cast_fp16)[name = string("input_93_cast_fp16")];
            tensor<fp16, [1, 512, 1, 1]> h_cast_fp16 = leaky_relu(alpha = var_404, x = input_93_cast_fp16)[name = string("h_cast_fp16")];
            tensor<int32, [2]> var_787 = const()[name = string("op_787"), val = tensor<int32, [2]>([1, -1])];
            tensor<fp16, [1, 512]> input_cast_fp16 = reshape(shape = var_787, x = h_cast_fp16)[name = string("input_cast_fp16")];
            tensor<fp16, [128, 512]> predictor_encoder_unshared_weight_to_fp16 = const()[name = string("predictor_encoder_unshared_weight_to_fp16"), val = tensor<fp16, [128, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55254592)))];
            tensor<fp16, [128]> predictor_encoder_unshared_bias_to_fp16 = const()[name = string("predictor_encoder_unshared_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55385728)))];
            tensor<fp16, [1, 128]> linear_1_cast_fp16 = linear(bias = predictor_encoder_unshared_bias_to_fp16, weight = predictor_encoder_unshared_weight_to_fp16, x = input_cast_fp16)[name = string("linear_1_cast_fp16")];
            int32 var_793 = const()[name = string("op_793"), val = int32(1)];
            bool var_794_interleave_0 = const()[name = string("op_794_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 256]> var_794 = concat(axis = var_793, interleave = var_794_interleave_0, values = (linear_0_cast_fp16, linear_1_cast_fp16))[name = string("op_794_cast_fp16")];
        } -> (var_794);
}